The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

Mohammadreza Mohammadi; John Finnan; Chris Baker; Mark Sterling

doi:10.1155/2020/4138469

The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

Mohammadi, Mohammadreza;Finnan, John;Baker, Chris;Sterling, Mark 2020-01-22 00:00:00 Hindawi Advances in Meteorology Volume 2020, Article ID 4138469, 16 pages https://doi.org/10.1155/2020/4138469 Research Article The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland 1 2 1 1 Mohammadreza Mohammadi , John Finnan, Chris Baker, and Mark Sterling School of Engineering, University of Birmingham, Birmingham B15 2TT, UK Teagasc Crops Research Centre, Oak Park, Carlow R93 XE12, Ireland Correspondence should be addressed to Mohammadreza Mohammadi; mxm755@student.bham.ac.uk Received 9 April 2019; Accepted 11 December 2019; Published 22 January 2020 Academic Editor: Giacomo Gerosa Copyright © 2020 Mohammadreza Mohammadi et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. )is paper examines the impact that climate change may have on the lodging of oats in the Republic of Ireland and the UK. )rough the consideration of a novel lodging model representing the motion of an oat plant due to the interaction of wind and rain and integrating future predictions of wind and rainfall due to climate change, appropriate conclusions have been made. In order to provide meteorological data for the lodging model, wind and rainfall inputs are analysed using 30 years’ time series corresponding to peak lodging months (June and July) from 38 meteorological stations in the United Kingdom and the Irish Republic, which enables the relevant probability density functions (PDFs) to be established. Moreover, climate data for the next six decades in the British Isles produced by UK climate change projections (UKCP18) are analysed, and future wind and rainfall PDFs are obtained. It is observed that the predicted changes likely to occur during the key growing period (June to July) in the next 30 years are in keeping with variations, which can occur due to diﬀerent husbandry treatments/plant varieties. In addition, the utility of a double exponential function for representing the rainfall probability has been observed with appropriate values for the constants given. climate change and will face a reduction in the crop pro- 1. Introduction duction. Similar impact is expected for eastern parts of New Climate change, which results from the increasing trend of Zealand, while tillage areas near major New Zealand rivers greenhouse gas emission, can cause major variations in will beneﬁt from the future climate conditions [4]. )e meteorological parameters [1]. Global temperature, for in- climate change is also expected to reduce crop production in stance, has increased by 0.74 C in the period from 1906 to the UK and Ireland [6, 7], where southern and eastern 2005, and precipitation patterns have changed in some parts England regions will be most aﬀected [4]. of the world [2]. As a large water consumer and being Furthermore, it is not clear how current problems in temperature dependent, the agriculture sector could be agriculture such as lodging—the permanent displacement of crops from the root or the stem due to strong winds and high dramatically aﬀected, either positively or negatively, by these changes worldwide. For example, future climate changes are amount of rainfall—might vary in future, due to climate supposed to have a negative eﬀect on cereal production in change eﬀects. It is with this issue that this paper is con- western Africa, southern Europe, and central and southern cerned, and we will study in particular the change in lodging Asia [3, 4], while, most parts of eastern Africa, northern risk in the United Kingdom (UK) and the Republic of Europe, northern America, and eastern and southeastern Ireland. Asia will beneﬁt from projected future meteorological As in other parts of the world, lodging has a negative conditions [4, 5]. Moreover, major parts of southern and impact on the agriculture sector in the UK and Republic of eastern Australian farmlands will be substantially aﬀected by Ireland, where damage to cereals and oilseed rape costs 2 Advances in Meteorology decline or small increase in the South and East were detected about £50 m on average each year and can reach up to more than £170 m in severe lodging years [8, 9]. )e costs incurred [31]. Future projections demonstrate an increase from 1 C to ° ° by lodging are not only due to yield loss but also are due to 1.6 C and up to 2.3 C by 2100 in mean annual temperatures the outcome of lower grain quality, increased drying costs, in Ireland and the UK [32, 33]. Furthermore, the Republic of and longer harvest time [8, 10–12]. )is substantial impact Ireland is expected to experience a decline in mean annual, has resulted in several studies of the interaction of wind with spring, and summer precipitation amounts by midcentury, plants in order to understand the physics of the phenom- and the number of extended dry periods is expected to enon. )e earliest notable work in this ﬁeld was Wright [13] increase during autumn and summer [34]. Similarly, the UK who suggested an exponential function for wind proﬁle over summer rainfall is projected to drop by 47% by 2070, while plant canopies. In the following decades, several studies an increase of 35% in winter precipitation is expected [33]. provided information about turbulence ﬂow over plant Climate simulators have also demonstrated a decline in canopies: Lu and Willmarth [14] discussed eddies above a energy content of the wind in all seasons except winter in plant canopy; Raupach et al. [15, 16] proposed the existence both countries [35]. Due to availability of new projections, of a mixing layer above the canopy and revealed that large which include not only precipitation but for the ﬁrst time coherent structures dominate the dynamics of the turbulent also wind data, it is now possible to study how future ﬂow, and Py et al. [17] observed the streamwise ﬂow length precipitation and wind might aﬀect the lodging risk in oats. scale to be proportional to canopy height. )eoretical In addition to the meteorological conditions, lodging can models developed by Baker et al. [12, 18, 19] together with be inﬂuenced by plant properties as aﬀected by variety and experimental studies on wheat [11, 20], barley [21], and the crop husbandry, including sowing rate, nitrogen rate, sunﬂower [22, 23] have provided a viable method to un- nitrogen timing, and plant growth regulator (PGR) appli- derstand the phenomenon and to predict the risk of lodging cation [25]. Moreover, other environmental conditions occurrence. As the main cause of the lodging is adverse which aﬀect plant growth such as topography, soil type, weather conditions, i.e., high rainfall and strong winds [19], sunshine soil moisture, temperature, pests, and diseases can several studies have addressed the issue of how variations in also aﬀect the plant biological properties [8, 25, 36]. )e meteorological parameters aﬀect lodging occurrences. Eas- contribution of each factor in the lodging process is hard to son et al. [24] reported that lodging is associated not only assess as the phenomenon is very complex. Nevertheless, with strong gusts (greater than 25 km/h (7 m/s)) but also Berry et al. [11] quantiﬁed the risk of lodging for wheat crops may occur in low wind speeds (16 km/h (4 m/s) or less). grown under diﬀerent treatments and showed the lodging Meanwhile, Berry et al. [25] demonstrated that lodging can timing and quantity can be estimated by a calibrated lodging be prevented or substantially reduced using appropriate model. At present, there is no enough data for oats to fully husbandry even in adverse weather conditions. In addition, quantify the impact of the full range of management impacts Baker et al. [12, 19] and Sterling et al. [9, 26] developed on lodging risk. probabilistic frameworks where the risk of lodging could be Based on the above, the aim of the current paper is to calculated via an understanding of the probability of crop investigate possible eﬀects of climate change on oat lodging failure in adverse weather conditions. However, it is not in the UK and the Republic of Ireland. )is study is part of a clear how these probabilities might vary in future. )e only wider research to study lodging in oats, funded by Teagasc notable work in this ﬁeld was by Martinez-Vasquez [27] who (the Republic of Ireland’s Agricultural and Food Develop- developed a lodging risk analysis building on the generalized ment Authority) [9, 37, 38]. )e project elaborates the lodging model [12] together with UKCP09 climate projector. generalised model developed in [12] to study the oat failure However, due to lack of knowledge about the parameters risk, for diﬀerent treatments/varieties as well as various required for the generalized lodging model for oats, the meteorological conditions (i.e., wind speed and rainfall). )e model used was not calibrated resulting in signiﬁcant approach used to investigate the aerodynamic parameters of inaccuracies in the risk calculation. Since the publication of the lodging model (Section 2.2) was also applied for other this work, a new version of the UK climate projector ongoing projects at the University of Birmingham to study (UKCP18) has been released which not only provides lodging in maize, oilseed rape, and rice, funded by UK updated projections but also includes both future precipi- Biology and Biotechnology Research Council (BBSRC) tation and wind projections. In addition, recent experi- [39, 40]. mental studies on oats have enabled a calibrated lodging Oat has been selected as the case study as the crop that model for oats [9]. has a high propensity to lodge in the UK and Ireland weather Historical climate observations show an average increase conditions [41–43]. Moreover, oat grains have been reported of 0.5%–1% per ten-year rainfall in most of northern as a rich source of vitamins, minerals, and antioxidant, as hemisphere’s mid and high latitudes [28]. Nevertheless, in well as having other health beneﬁts such as reducing the England and Wales, annual mean rainfall has not changed cholesterol level and blood sugar [44–46]. Consequently, oat noticeably since 1766, and seasonal precipitation seems to is cultivated in about 9% of crop tillage areas in Ireland [47]. show a decline in summer [29]. Additionally, historical data Although the percentage of cultivated farmlands of oats is demonstrate a signiﬁcant variation of rainfall in diﬀerent lower (about 1%) in the UK, it has the highest increasing rate years, whilst an overall increase in wintry precipitation can (7.8%) in the major cereal crops (wheat, barley, oat, and be observed [30]. In the Republic of Ireland, an increase in oilseed rape) [48]. )e methodology used in this research is annual rainfall in the North and West of the country and a given in Section 2, including an outline of historical data Advances in Meteorology 3 sources, the conceptual lodging model, and the prediction of 2016). )ese speciﬁc datasets were selected based on the future climate scenarios. Section 3 then outlines the de- availability of long-term data (1987–2016) and proximity to velopment of wind and rainfall probability distributions regions where oats are commercially grown (i.e., mainly the from historical data and describes the possible future eastern and southern parts of Ireland and Eastern Scotland, changes in these distributions due to climate change eﬀects. as well as Western and Southern England [50] (RSK ADAS Section 4 then presents an analysis of lodging risk, both for Ltd, personal communication, 2016)). )ese data were the current situation and for the predicted future climate. analysed to ﬁnd rainfall and wind probability density func- Finally, the implications of the results are discussed in tions (PDFs) which will be described further in Section 3.1. Section 5. 2.2. Lodging Model and Risk Calculation. In this section, the 2. Methodology generalized lodging model [12] is described brieﬂy since it is 2.1. Historical Data. To evaluate historical meteorological a key to understanding the risk of changes in climate and is conditions during the last three decades, data from 38 based on wind and rainfall probability density functions. In stations were collected from Met Eireann (the Irish Mete- this model, the external bending moment that a plant ex- periences as a result of the wind is compared with the plant’s orological Service) [35], the United Kingdom’s Meteoro- logical Oﬃce Integrated Data Archive System (MIDAS) stem and anchorage resistance [8]. Accordingly, two failure Land and Marine Surface Stations [49], and the Meteoro- velocities for the stem and root failure can be deﬁned. )e logical Oﬃce National Meteorological Archive (Met Oﬃce stem failure criteria can be written in the format of a stem National Meteorological Archive, personal communication, failure (lodging) velocity (U ), i.e., Ls 0.5 2 3 ω (X/g) σπa /4 􏼁 􏼐(1 − ((a − t)/a)) 􏼑n ⎛ ⎝ ⎞ ⎠ (1) U � , Ls 0.5 2 2 2 1 + ω (X/g)􏼁 0.5ρA X􏼁 (cos(αx/l) − cot α sin(αx/l)) 1 + I 4g + g (π/4θ)􏼁􏼁 n CF MB MR where ω � 2πf , f is the natural frequency, is the radial oat canopies and to obtain required aerodynamic parameters n n n frequency, X is the height of the centre of mass of the for the model. Full details relating to these experiments can canopy, g is the gravity acceleration, σ is the stem yield be found in [9, 37, 52]. Furthermore, additional experiments stress, a is the stem radius, t is the stem wall thickness, n is were undertaken to identify the plant-related parameters the number of stems per plant, ρ is the air density, A is the grown under diﬀerent varieties/treatments and various soil CF plant shear area for a plant in a canopy, α is a dimensionless conditions. )ese ﬁeld experiments were mainly based on parameter, x is the distance up to the stem from the ground, l agronomic measurement protocols developed by Berry et al. is the length of stem, I is the turbulence intensity, and θ is [53]. the damping ratio. Additionally, g and g are the gust Figure 1 illustrates graphically how equations (1) and MB MR factor of broad-banded stem moment and the gust factor of (2) can be interpreted. In Figure 1, the vertical axis resonant stem moment, respectively [51]. represents the daily rainfall (i), and the horizontal axis is Similarly, the failure root velocity known as root lodging the hourly mean wind speed (U). Various regions have (U ) can be deﬁned as been deﬁned in Figure 1. For example, the curve LR 0.5 (equation (3)) deﬁnes the lodging/no-lodging boundary cSd U � , and illustrates the relative contributions of rainfall and 􏼠 􏼡 LR 2 2 1 + ω (X/g)􏼁/ω (x/g)􏼁 0.5ρA X 􏼁 1 + 2Ig 􏼁 n n CF MB wind speed required for lodging to occur. )e curve is (2) given by where S is the soil shear strength, d is the eﬀective root diameter, and c is a constant. As the stem and root lodging i � 1 − 􏼠 􏼡i , (3) LR velocities (equations (1) and (2)) are based on a variety of crop parameters (e.g., natural frequency and drag area), where i is the daily rainfall and i is the reference rainfall diﬀerent experiments were undertaken to investigate these corresponding to zero wind speed. It should be noted that parameters in two separate ﬁeld trials—one in 2017 and one Figure 1 is plotted for a sample data from oats, and thus, the in 2018. )e experimental setup designed to study the curve and dashed lines can be replotted for other oat sample turbulent ﬂow over plant canopies and the dynamic of plant data or other crops. movement of crops included two sonic anemometers (to record wind velocity above the canopy) and two video )e risk of lodging can be obtained using integration of cameras to observe the crop’s movement. )e acquired wind joint (wind and rain) probability density function in the and video data were later postprocessed through standard region where the risk of lodging exists. Baker et al. [12] used wind engineering methods to study the turbulent ﬂow over a Rayleigh distribution for wind PDF given by 4 Advances in Meteorology 0.8 0.7 20 Root lodging 0.6 0.5 Root and stem 0.4 lodging 0.3 0.2 No lodging 0.1 U U LS LR 0 2 4 6 8 10 12 14 16 18 20 0 510 Rainfall (mm) U (m/s) Bansha (Ireland) Haddington (Scotland) Figure 1: Lodging regions in the daily rainfall/hourly mean wind Hereford (England) speed plane for a sample oat plant. Figure 2: Rainfall probability density function for selected Irish, Scottish, and English meteorological stations in the period from 2 U − U /λ ( ) 1987 to 2016 for June and July [35, 49] (Met Oﬃce National p(U) � e , (4) 􏼒 􏼓􏼒 􏼓 λ λ Meteorological Archive, personal communication, 2016). where p(U) is the PDF for (U) and λ is a parameter used to characterize the wind climate. )e Rayleigh distribution was and Scotland. In Figure 2, the horizontal axis illustrates preferred rather than the Weibull distribution since it en- rainfall, and the vertical axis shows the correspondent abled an analytical form of the lodging risk to be calculated. probability. )ese are for the months of June and July, when For the rainfall PDF, an exponential function was used: lodging events are known to occur. To identify an appro- priate function, a curve is ﬁtted on each station using − (i/m) (5) p(i) � 􏼒 􏼓e , MATLAB, and a double exponential was found to be the best representative function: where m is the mean daily rainfall and i is the daily rainfall. − bi − di At the time, equation (5) was a convenient expression; P(i) � ae + ce , (6) however, Baker et al. [12] emphasised the necessity of ad- ditional research in order to establish a more appropriate where i is the amount of daily rainfall, P(i) is the probability, representation for the rainfall PDFs [12, 26]. and a, b, c, and d are site-dependent coeﬃcients. Despite the geographic variation of rainfall, it was found that the overall PDFs can be deﬁned at regional scales for Ireland, Scotland, 2.3. Future Scenario Projection. UKCP18 provides the most and England (Table 1). Furthermore, it was observed that, recent projections for future climate conditions in the through appropriate selection of the values of a, b, c, and d, coming decades based on a number of data sources and an overall curve could be obtained which represented all of emission scenarios for diﬀerent periods and locations [54]. the data irrespective of location to a reasonable degree of Emission scenarios in UKCP18 are deﬁned as Representative accuracy, i.e., 0.2%. It should be noted that the values of the Concentration Pathways (RCPs), which determine the aforementioned constants are not independent but have amount of greenhouse gases causing certain radiative been chosen to ensure that they are consistent with the forcing at the high altitude of the Earth’s atmosphere by cumulative density function (CDF) tending to unity as the 2100, in comparison to preindustrial levels [55]. Four forcing rainfall tends to inﬁnity. levels are used: 2.6, 4.5, 6.0, and 8.5 W/m , which are deﬁned A similar analysis was undertaken for the wind speed, as RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios [33]. and it was observed that a Weibull distribution, given by Land projections in the UKCP18 include probabilistic, k− 1 global, and regional outcomes. Probabilistic projections are k U − (U/λ) (7) P(U) � 􏼒 􏼓 e , designed to demonstrate the ranges of uncertainty in the λ λ outputs for a certain period, location (region), and diﬀerent emission scenarios. Global/regional projections both use best represented the data. Here, λ and k are parameters RCP 8.5 and illustrate 28/12 climate projections at a 60 km/ governing the scale and shape of the distribution, respec- 12 km grid resolution, respectively [56]. tively. Figure 3 illustrates the results of the analysis for 10 stations in Ireland and the UK together with the ﬁnal curve used to represent all data (λ � 4.4, k � 1.8). )e largest 3. Climate Data and Predictions diﬀerence between the actual data and the ﬁtted curve is 3.1. Wind and Rainfall PDFs. Figure 2 shows sample data ∼12% and occurs in low-speed conditions, i.e., conditions relating to PDFs for selected stations in Ireland, England, when lodging risk is minimum. i (mm/day) No lodging Probability Advances in Meteorology 5 Table 1: Coeﬃcients for regional and overall representative curves similar outcomes, only data corresponding to RCP 2.6 are for rainfall PDFs and corresponding curve diﬀerence with actual presented here. )e ﬁgure shows drier conditions for data. southern regions of England in June and July, while western regions of Scotland are projected to experience wetter cli- a b c d Mean-squared error mate in June. Ireland 0.60 0.75 0.02 0.01 0.002 A probabilistic projection tool was employed to analyse England 0.70 0.88 0.03 0.15 0.002 data at 16 stations across southern and western areas of Scotland 0.60 0.75 0.03 0.15 0.002 England, as well as eastern and southern regions of Scotland Overall 0.62 0.83 0.03 0.12 0.002 (areas where oats are commercially grown). Results illustrate that for all stations, diﬀerent emission scenarios have only a slight eﬀect on precipitation rate anomaly (%), although the 0.25 diﬀerence between emission scenario plots is larger in July. Figure 5 illustrates an example of a CDF for monthly rainfall 0.2 changes at a sample weather station (Herford, England) for diﬀerent emission scenarios. More details, regarding the th th anomaly ranges from 10 percentile to 90 percentile, are 0.15 presented in Table 2. As illustrated in Figure 5, diﬀerent RCPs result in diﬀerent CDFs, which are perhaps not too surprising given the complexity of the climate model and the 0.1 uncertainty associated with this particular area. 0.05 3.2.2. Global Projections (60 km Resolution). Global pro- jections are based on 28 climate models at 60 km grid resolution including 15 simulations of the Met Oﬃce Hadley 0 5 1015202530 Centre model (HadGEM3-GC3.05), and 13 other outputs Wind (m/s) are adopted from the Intergovernmental Panel on Climate Fitted curve South farnborough (England) Change’s 5th Assessment Report, CMIP5-13 [30]. Using Lossiemouth (Scotland) Cork airport (Ireland) these two series of climate models increases the range of Figure 3: Wind probability density function 1987–2016 for se- plausible futures. lected Irish, Scottish, and English meteorological stations [35, 49]. )e HadGEM3-GC3.05 is a coupled atmosphere-ocean conﬁguration, including diﬀerent levels of stratosphere, atmospheric chemistry, vegetation, and ocean biology [33]. 3.2. Future Climate Projection. Projections of UKCP18 show In each model’s output, all plausible variants perturbed in warmer, wetter winters and hotter, drier summers for the the given climate model conﬁguration, building a perturbed UK. All the regions of the UK are predicted to face higher parameter ensemble (PPE) [57]. )ese variants can be temperatures, and the increase is greater in summers rather classiﬁed as convection parameters, mountain eﬀects, at- than in winters. Perhaps not surprisingly, geographic and mospheric boundary layer conditions, cloud radiation and seasonal variation of precipitation is likely to continue to microphysics features, and aerosol parameters which can be exist in future. )is section discusses results from the found in [57]. Later, PPEs were ﬁltered to provide highest UKCP18 where probabilistic, global, and regional projec- plausibility and diversity of outputs, producing 15 simula- tions are presented in Sections 3.2.1–3.2.3. tions [57]. In order to add diversity to the projections, 13 CMIP5 3.2.1. Probabilistic Projections (25 km Resolution). models (CMIP5-13) are also provided simulating global and Probabilistic projections merge historical weather data with zonal mean temperatures in the Earth’s surface, global trend climate models and statistics at 25 km grid resolution to of sea surface temperature (SST) bias and Atlantic Merid- provide outputs for diﬀerent emission scenarios and are an ional Overturning Circulation (AMOC), as well as clima- appropriate tool to study the eﬀect of diﬀerent RCPs on tological conditions over the North Atlantic and Europe [57]. Table 3 shows models incorporating in CMIP5-13 and precipitation anomalies. However, the tool provides data only for UK areas and does not include projections for the associated modelling groups. Irish Republic. Figure 4 illustrates precipitation rate Figure 6 shows results of global projection from these 28 anomalies in June and July, respectively, in all the UK areas climate models at 60 km resolution. In addition to model using the 1981–2010 baseline and geographic variations in designations described in Table 3, 15 PPEs from HadGEM3- rainfall anomalies can be clearly observed. )e ﬁgure in- GC3.05 are presented as ﬁve-digit numbers. )ese numbers th th th cludes three panels for 10 , 50 , and 90 percentiles, and are allocated to name selected PPEs by UKCP18 designers each square indicates the range of change in the area. For and do not have any signiﬁcance (Met Oﬃce, personal example, a grid showing 10% precipitation anomaly rate in communication, 2019). )e results illustrate that in the most th 50 percentile represents 50% probability that monthly severe predictions, southern regions of Ireland might get rainfall will increase by less than 10% [54]. As all RCPs show 30–40% drier in June and July. However, some models Probability 6 Advances in Meteorology th th th 10 percentile 50 percentile 90 percentile –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (a) th th th 10 percentile 50 percentile 90 percentile –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (b) Figure 4: Monthly average precipitation rate anomaly (%) for RCP 2.6 from 2020 to 2049 using baseline 1981–2010 and scenario RCP 2.6 (a) in June and (b) in July. predict a diﬀerent trend suggesting an increase of precipi- With respect to England, the majority of the projections tation of up to 40% increase in precipitation. In general, the suggest that June will be 10% to 30% drier, although regions majority of the models show a predicted diﬀerence of ±20% in the South could experience up to 30% increase in rainfall. in June and July. In July, most models show drier conditions (up to 60% Advances in Meteorology 7 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 –60 –40 –20 0 20 40 60 –60 –40 –20 0 20 40 60 Precipitation rate anomaly (%) Precipitation rate anomaly (%) RCP 2.6 RCP 6.0 RCP 2.6 RCP 6.0 RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5 (a) (b) Figure 5: Cumulative distribution function for precipitation rate anomaly in Herford, England, for RCPs used in the UKCP18 in (a) June and (b) July. are presented for 2020–2049 and 2050–2079 periods. Prob- especially in southern parts) whilst the extreme cases suggest a 40% increase in rainfall. Finally, precipitation in Scotland is abilistic projections demonstrate anomaly ranges from the th th expected to experience ±30% and ±20% anomalies in June 10 percentile to 90 percentile, while regional and global and July, respectively. variations represent the largest anomaly projected. For the same periods (2020–2049 and 2050–2079), wind predictions show ±1 m/s change in all studied areas. )e Republic of 3.2.3. Regional Projections (12 km Resolution). Regional Ireland is mainly projected to face a reduction in average wind projections are based on HadGEM3-GC3.05 and use 12 PPEs speed except in a few areas on the northern and southern in a downscaled area in comparison to the global projection coasts. Results for the UK appear to be spatially variable. which enables the eﬀect of physiographic features including mountains, coasts, urban areas, lakes, and rivers being con- sidered. Figure 7 illustrates precipitation maps of anomalies 4. Lodging Risk Assessment for regional projections for the RCP 8.5 scenario. )ese 4.1. Lodging Risk in Current Climate Conditions. To inves- outputs are generated by 12 projections from the Met Oﬃce tigate the risk of lodging in autumn-sown oats in current Hadley Centre model at 12 km scale resolution. In general, conditions, a database of 1,000 synthetic plants was gen- most models show the UK and Ireland will tend to experience erated based on mean values and standard deviations of drier condition in June and July with variations corre- plant parameters including panicle area, stem radius, stem sponding to Ireland between ±20% and up to 50% reduction wall thickness, centre of gravity, eﬀective root diameter, in the monthly rainfall in southern regions. Furthermore, the anchorage depth of the rooting system, and the number of majority of the models implies southern and western regions stems per plant provided in Table 4 (in keeping with the of England will become drier in June and July while variation approach of [11]), i.e., for each synthetic sample, the plant of projections in eastern parts is from 40% drier to 50% wetter parameters were randomly generated assuming a corre- condition. Finally, Scottish areas are projected to experience sponding normal distribution (see [11]). Experience has mainly ±30% anomaly in precipitation. shown that 1,000 samples are signiﬁcant to ensure that the Figure 8 shows the monthly average wind speed anomaly results are statistically independent, and thus, relevant at 10m above the ground from 2020 to 2049 for June and conclusions can be drawn. In order to provide the input to July. )is ﬁgure illustrates the wind speed change in both the database, plant data (mean and standard deviations) England and Ireland is±1 m/s, i.e., a relatively small change. were obtained from ﬁeld experiments undertaken in 2016- A slight increase is observed in Scotland, but again, this 2017 at Knockbeg, County Laois, the Republic of Ireland predicted increase is small and from a lodging perspective is ° ° (52.86 N, 6.94 E, 54 MSL). Plants were raised from two unlikely to be signiﬁcant. varieties (an oat variety susceptible to lodging (Barra) and an oat variety with moderate resistance to lodging (Husky)) 3.2.4. Summary of Projections. UKCP18 outputs produced grown under diﬀerent combinations of agronomic treat- by diﬀerent models are summarized in Table 2, where results ments designed to create a range of lodging pressures. )us, Probability of being less than (%) Probability of being less than (%) 8 Advances in Meteorology − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − Table 2: Monthly average precipitation rate anomaly (%) using baseline 1981–2010 in June and July. 2020–2049 2050–2079 Month Region Projection RCP 2.6 RCP 4.5 RCP 6 RCP 8.5 RCP 2.6 RCP 4.5 RCP 6 RCP 8.5 Probabilistic 26% to +17% 24% to +19% 24% to +19% 27% to +19% 37% to +3% 42% to 2% 43% to +2% 50% to +4% South England Regional 50% to +50% 70% to +20% Global 40% to +40% 70% to +20% Probabilistic 31% to +22% 30% to +22% 29% to +22% 32% to +21% 38% to +4% 43% to +3% 44% to +3% 51% to +4% West England Regional 50% to +30% 70% to +10% June Global 40% to +40% 70% to +20% Probabilistic 18% to +22% 17% to +22% 17% to +23% 17% to +22% 25% to +14% 27% to +14% 27% to +14% 29% to +14% Scotland Regional 30% to +60% 40% to +30% Global 40% to +40% 50% to +30% Regional 50% to +20% 70% to 0% Ireland Global 40% to +30% 60% to +20% Probabilistic 43% to +16% 44% to +16% 44% to +17% 47% to +14% 44% to +12% 49% to +9% 49% to 10% 56% to +7% South England Regional 40% to +50% 60% to +10% Global 50% to +50% 70% to +30% Probabilistic 27% to +19% 29% to +19% 28% to +19% 31% to +18% 37% to +9% 41% to +8% 40% to +8% 47% to +5% West England Regional 40% to +20% 60% to +10% July Global 30% to +50% 60% to +30% Probabilistic 28% to +22% 28% to +22% 28% to +22% 30% to +21% 38% to +15% 40% to +17% 40% to +17% 47% to +19% Scotland Regional 40% to +30% 50% to +10% Global 30% to +40% 50% to +20% Regional 50% to +30% 60% to 10% Ireland Global 40% to +40% 60% to +40% Advances in Meteorology 9 Table 3: )e CMIP5-13 models used in UKCP18 under the RCP wind speed and rainfall in the future. We will use this range 8.5 scenario [57]. of variations in the analysis of risk that follows. Using the range of values of rainfall and wind speed Model Modelling group calculated above, revised PDFs for these variables can be designation determined, corresponding to likely future climate condi- Centro Euro-Mediterraneo per I Cambiamenti CMCC-CM tions. )ese were calculated by calculating cumulative dis- Climatici tribution functions through integrating the PDFs for current Beijing Climate Centre, China Meteorological BCC-CSM1 conditions, applying the predicted rainfall and wind Administration Canadian Centre for Climate Modelling and anomalies to these CDFs and then diﬀerentiating them to CanESM2 Analysis obtain PDFs relevant to future conditions. Typical values are Commonwealth Scientiﬁc and Industrial shown in Figure 9. ACCESS1-3 Research Organization (CSIRO) and Bureau of Risk calculation for oats was then carried out using the Meteorology (BOM), Australia lodging model described in Section 2.2. Using the new wind CESM1-BGC Community Earth System Model Contributors and rainfall PDFs and averaged agronomic values (Table 4), Centre National de Recherches the failure probability in each anomaly range is obtained. Met ´ eorologiques/Centre ´ Europeen ´ de CNRM-CM5 Figure 10 shows the risk contour for Irish conditions, which Recherche et Formation Avanc´ee en Calcul is similar to what is found for England and Scotland. )e Scientiﬁque ﬁgure shows the lodging risk is more aﬀected by changes in EC-EARTH EC-EARTH consortium NOAA Geophysical Fluid Dynamics the wind speed compared to the rainfall. )us, if the wind GFDL-ESM2G Laboratory conditions do not change considerably in the future, then the HadGEM2-ES Met Oﬃce Hadley Centre risk of lodging is unlikely to vary signiﬁcantly from present IPSL-CM5A- conditions. Institute Pierre-Simon Laplace MR MPI-ESM-MR Max-Planck-Institut fur ¨ Meteorologie 5. Discussion MRI-CGCM3 Meteorological Research Institute CCSM4 National Centre for Atmospheric Research )e above analysis has illustrated that the probability of adverse weather occurrence is a key factor in determining four diﬀerent synthetic databases were generated corre- lodging risk. )is paper suggests that a double exponential curve can suﬃciently represent wind and rainfall PDFs to an sponding to variety/seed rate combinations outlined in Table 5, as well as a database is generated based on Table 4 acceptable level of accuracy. Although these functions were evaluated for the months of June and July, it is noted that a associated with natural variations of oat parameters. As illustrated in equations (1) and (2), in addition to the slight change in the studied period would not signiﬁcantly alter the obtained PDFs. agronomic parameters (stem strength etc.), the lodging model also relies on soil and aerodynamic parameters, including drag Monthly precipitation projections elucidated a dramatic change especially in the second half of the current century, coeﬃcient, air density, natural frequency, damping ratio, and turbulence intensity, provided in Table 4. )e aerodynamic which can reach up to 70%, while monthly average wind speed anomalies were expected to vary by only ±1 m/s. parameters were evaluated using standard wind engineering methods [9], while soil shear strength was measured in the Although such projections are always associated with larger th th studied site. Table 5 shows the 10 uncertainties in modelling longer periods, it was found that –90 percentile range as th even such a sharp rainfall variation would aﬀect the lodging well as the 50 percentile lodging risks in the generated synthetic databases. A relatively large spread of risk in each risk by less than 5%, while 1 m/s reduction/increase in mean hourly wind speed could change the failure risk more than sample variety/seed rate can be observed and illustrates that diﬀerent husbandry treatments/varieties can result in con- 10%. )us, demonstrating that in general, the wind speed is the governing parameter for lodging. siderable diﬀerences in failure probabilities—this is an im- portant result which will be discussed in the following sections. )e lodging risk was evaluated for the whole range of wind and rainfall variation, with the majority of climate )e risk of lodging might also change in diﬀerent sites due to diﬀerences in meteorological conditions, especially models indicating a decline in the average wind speed in Ireland. Hence, it is reasonable to assume that on average, wind PDF (Figure 3). Accordingly, the lodging risk was the risk of oat lodging is likely to reduce. However, the same assessed for average agronomic values (Table 4) for 9 studied conclusion cannot be drawn for the UK. Although these stations in England, Scotland, and Ireland (3 stations in each conclusions are made based on variations in monthly av- region). )e risk assessment was undertaken using the representative (overall) rainfall PDF (Table 1) and site- erages, it is expected that extreme wind events (e.g., storms) will not aﬀect the aforementioned risk assessment as a re- speciﬁc wind PDFs. Results show the lodging risk range in English stations is 10–17%, in Irish stations is 26–27%, and lation between climate change and summer storminess in the UK which has not been established [33]. Moreover, in Scottish stations is 18–27% stations. North Atlantic cyclones are expected to reduce by 10%, the number of extreme storms is projected to be rare, and the 4.2. Lodging Risk in Future Climate Conditions. Based on the majority of such adverse weather conditions is expected to data of Table 2, Table 6 illustrates the possible variation in happen in winter [32]; all implying UK and Ireland will not 10 Advances in Meteorology 02491 02305 01649 bcc-csm1 02868 EC-EARTH HadGEM2-ES CMCC-CM ACCESS1-3 01843 01935 IPSL-CM5A-MR 02832 02335 CESM1-BGC 00834 01554 MPI-ESM-MR 02123 00000 02242 GFDL-ESM2G 01113 MRI-CGCM3 CCSM4 CanESM2 CNRM-CM5 00605 –80 –70 –60 –50 –40 –30 –20 –10 0 102030405060 Precipitation rate anomaly (%) (a) 02491 01649 ACCESS1-3 02832 00000 02123 01554 bcc-csm1 02868 01935 CESM1-BGC 00834 CCSM4 01113 02242 02305 HadGEM2-ES 02335 MPI-ESM-MR CMCC-CM IPSL-CM5A-MR 01843 00605 GFDL-ESM2G MRI-CGCM3 EC-EARTH CanESM2 CNRM-CM5 –80 –70 –60 –50 –40 –30 –20 –10 0 102030405060 Precipitation rate anomaly (%) (b) Figure 6: Monthly average precipitation rate anomaly (%) from 2020 to 2049 using baseline 1981–2010 and scenario RCP 8.5 for (a) June and (b) July. ()e four-digit number/letters above the projections correspond to the relevant models used for the projections). face more frequent summer storms in comparison to the metrological conditions can be described by representative current conditions. function which were found accurate enough to be used in )is research quantiﬁed the oat-lodging risk for the ﬁrst the British and Irish farmlands, more studies are recom- time based on experimental data obtained from experi- mended to investigate if the plant properties change in ments undertaken in Carlow, Ireland. Although the diﬀerent sites/years. Moreover, temperature, sunshine, Advances in Meteorology 11 02491 01649 02305 01935 02868 00000 01843 01554 02335 02123 02242 01113 –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (a) 02491 00000 01649 02123 01554 01935 01113 02868 01843 02242 02305 02335 –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (b) Figure 7: Monthly average precipitation rate anomaly (%) in the period from 2020 to 2049 using baseline 1981–2010 and scenario RCP 8.5 for (a) June and (b) July. ()e ﬁve-digit numbers above each map indicate the PPE model used for the projection). weather and soil moisture, plant diseases, pest, and other these parameters and the potential eﬀect of climate change environmental parameters can aﬀect attributes of plants on these factors can also be a point of interest for future associated with lodging [8, 25]. )e natural variation of research studies. 12 Advances in Meteorology 02491 01935 02868 00000 01843 02335 02305 01649 01554 02242 02123 01113 –2 –1 012 –1 Wind speed anomaly at 10 m (ms ) (a) 00000 01843 02305 01649 01554 02335 02123 02868 01935 02491 02242 01113 –2 –1 012 –1 Wind speed anomaly at 10 m (ms ) (b) Figure 8: Monthly average wind speed anomaly at 10 m from 2020 to 2049 using baseline 1981–2010 and scenario RCP 8.5 for (a) June and (b) July. Importantly, from a grower’s perspective, this analysis a result of plant variations and growing practices—this is an has demonstrated that the eﬀect of climate change on interesting result and enables the potential change in lodging lodging risk is similar to the variation in risk which occurs as to be appreciated at farm scale. Advances in Meteorology 13 Table 4: Agronomic, aerodynamic, and soil parameters (letter abbreviation for parameters used can be found in Section 2.2). Agronomic parameters Mean Standard deviation n 2.02 0.7 a (mm) 3.2 0.6 t (mm) 0.9 0.3 L (m) 1.4 0.3 X (m) 0.64 0.081 d (mm) 58 9.6 l (mm) 91.9 10 Aerodynamic/soil parameters ρ (Kg/m ) 1.2 — A (m ) 0.016 0.002 CF θ 0.1 0.04 f (Hz) 1.1 0.03 I 0.5 0.16 S (K Pa) 35 5.1 Table 5: Lodging risk variation in diﬀerent treatments and seed rates. 2 th th th Variety Seed rate (m ) Risk range (10 –90 percentile) 50 percentile risk Susceptible 200 seeds 0.06–0.60 0.21 Susceptible 500 seeds 0.11–0.62 0.32 Moderate resistance 200 seeds 0.03–0.46 0.20 Moderate resistance 500 seeds 0.04–0.60 0.26 Table 6: Monthly average wind and rainfall rate anomaly (%) generated by most of the models using baseline 1981–2010 in June and July. Most likely monthly anomaly to happen Region 2020–2049 2050–2079 Rain Wind Rain Wind South England − 30% to 10% ±1 m/s − 40% to 0% − 1 m/s to 0 West England − 30% to 10% ±1 m/s − 40% to 0% ±1 m/s Ireland ±20 − 1 m/s to 0 − 40% to 0% − 1 m/s to 0 Scotland ±20 ±1 m/s − 40% to 10% ±1 m/s 0.9 0.3 0.8 0.25 0.7 0.6 0.2 0.5 0.15 0.4 0.3 0.1 0.2 0.05 0.1 0 0 0 510 15 20 0 5 101520 Rainfall (mm) Wind speed (m/s) Current conditions Current condition 70% reduction (extreme anomaly) +1 m/s 30% reduction (most likely anomaly) –1 m/s 10% increase (most likely anomaly) 50% increase (extreme anomaly) (a) (b) Figure 9: (a) Rainfall and (b) wind PDFs in the current and future climate conditions for Haslemere station (South England). Probability Probability 14 Advances in Meteorology 1 ceda.ac.uk for the UK. Additionally, some meteorological data for the UK are provided by Meteorological Oﬃce National Meteorological Archive. Requests to access this archive should be made to Meteorological oﬃce, enquiries@ metoﬃce.gov.uk. Climate change projection data are available from http://ukclimateprojections.metoﬃce.gov.uk. Conflicts of Interest )e authors declare that there are no conﬂicts of interest –1 regarding the publication of this paper. 30 70 0 110 150 Rainfall changes (%) Acknowledgments 0–10 20–30 )is paper was dedicated to one of the authors, John Finnan, 10–20 30–40 who died in an aircraft accident during the course of the Figure 10: Lodging risk based on wind and rainfall anomalies. research. John’s expertise and input was instrumental for the agricultural elements of the research. However, John also had an uncanny knack of delivering a constructive and well- 6. Concluding Remarks timed challenge on the meteorological aspects of the re- search, thereby ensuring that those authors who claim to )is paper has examined the impact that climate change profess such expertise reﬂected long and hard on how their could have on lodging in oats. )e following conclusions can message could be delivered more appropriately—something be made: which is key to ensuring successful transdisciplinary re- search. Perhaps, this is something that we all might like to (i) A double exponential PDF can be used to represent reﬂect on as subject boundaries become less opaque—he rainfall with an acceptable degree of accuracy. would have liked that. )anks are expressed to Walsh (ii) )e risk of oat lodging occurring within a speciﬁed Fellowship which funded the ﬁrst author and to those or- period of time (typically June-July) is a complex and ganisations mentioned in the paper that provided their data nonlinear interaction of wind and rain. free of charge. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2020 Mohammadreza Mohammadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 1687-9309
eISSN: 1687-9317
DOI: 10.1155/2020/4138469
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Abstract

Hindawi Advances in Meteorology Volume 2020, Article ID 4138469, 16 pages https://doi.org/10.1155/2020/4138469 Research Article The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland 1 2 1 1 Mohammadreza Mohammadi , John Finnan, Chris Baker, and Mark Sterling School of Engineering, University of Birmingham, Birmingham B15 2TT, UK Teagasc Crops Research Centre, Oak Park, Carlow R93 XE12, Ireland Correspondence should be addressed to Mohammadreza Mohammadi; mxm755@student.bham.ac.uk Received 9 April 2019; Accepted 11 December 2019; Published 22 January 2020 Academic Editor: Giacomo Gerosa Copyright © 2020 Mohammadreza Mohammadi et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. )is paper examines the impact that climate change may have on the lodging of oats in the Republic of Ireland and the UK. )rough the consideration of a novel lodging model representing the motion of an oat plant due to the interaction of wind and rain and integrating future predictions of wind and rainfall due to climate change, appropriate conclusions have been made. In order to provide meteorological data for the lodging model, wind and rainfall inputs are analysed using 30 years’ time series corresponding to peak lodging months (June and July) from 38 meteorological stations in the United Kingdom and the Irish Republic, which enables the relevant probability density functions (PDFs) to be established. Moreover, climate data for the next six decades in the British Isles produced by UK climate change projections (UKCP18) are analysed, and future wind and rainfall PDFs are obtained. It is observed that the predicted changes likely to occur during the key growing period (June to July) in the next 30 years are in keeping with variations, which can occur due to diﬀerent husbandry treatments/plant varieties. In addition, the utility of a double exponential function for representing the rainfall probability has been observed with appropriate values for the constants given. climate change and will face a reduction in the crop pro- 1. Introduction duction. Similar impact is expected for eastern parts of New Climate change, which results from the increasing trend of Zealand, while tillage areas near major New Zealand rivers greenhouse gas emission, can cause major variations in will beneﬁt from the future climate conditions [4]. )e meteorological parameters [1]. Global temperature, for in- climate change is also expected to reduce crop production in stance, has increased by 0.74 C in the period from 1906 to the UK and Ireland [6, 7], where southern and eastern 2005, and precipitation patterns have changed in some parts England regions will be most aﬀected [4]. of the world [2]. As a large water consumer and being Furthermore, it is not clear how current problems in temperature dependent, the agriculture sector could be agriculture such as lodging—the permanent displacement of crops from the root or the stem due to strong winds and high dramatically aﬀected, either positively or negatively, by these changes worldwide. For example, future climate changes are amount of rainfall—might vary in future, due to climate supposed to have a negative eﬀect on cereal production in change eﬀects. It is with this issue that this paper is con- western Africa, southern Europe, and central and southern cerned, and we will study in particular the change in lodging Asia [3, 4], while, most parts of eastern Africa, northern risk in the United Kingdom (UK) and the Republic of Europe, northern America, and eastern and southeastern Ireland. Asia will beneﬁt from projected future meteorological As in other parts of the world, lodging has a negative conditions [4, 5]. Moreover, major parts of southern and impact on the agriculture sector in the UK and Republic of eastern Australian farmlands will be substantially aﬀected by Ireland, where damage to cereals and oilseed rape costs 2 Advances in Meteorology decline or small increase in the South and East were detected about £50 m on average each year and can reach up to more than £170 m in severe lodging years [8, 9]. )e costs incurred [31]. Future projections demonstrate an increase from 1 C to ° ° by lodging are not only due to yield loss but also are due to 1.6 C and up to 2.3 C by 2100 in mean annual temperatures the outcome of lower grain quality, increased drying costs, in Ireland and the UK [32, 33]. Furthermore, the Republic of and longer harvest time [8, 10–12]. )is substantial impact Ireland is expected to experience a decline in mean annual, has resulted in several studies of the interaction of wind with spring, and summer precipitation amounts by midcentury, plants in order to understand the physics of the phenom- and the number of extended dry periods is expected to enon. )e earliest notable work in this ﬁeld was Wright [13] increase during autumn and summer [34]. Similarly, the UK who suggested an exponential function for wind proﬁle over summer rainfall is projected to drop by 47% by 2070, while plant canopies. In the following decades, several studies an increase of 35% in winter precipitation is expected [33]. provided information about turbulence ﬂow over plant Climate simulators have also demonstrated a decline in canopies: Lu and Willmarth [14] discussed eddies above a energy content of the wind in all seasons except winter in plant canopy; Raupach et al. [15, 16] proposed the existence both countries [35]. Due to availability of new projections, of a mixing layer above the canopy and revealed that large which include not only precipitation but for the ﬁrst time coherent structures dominate the dynamics of the turbulent also wind data, it is now possible to study how future ﬂow, and Py et al. [17] observed the streamwise ﬂow length precipitation and wind might aﬀect the lodging risk in oats. scale to be proportional to canopy height. )eoretical In addition to the meteorological conditions, lodging can models developed by Baker et al. [12, 18, 19] together with be inﬂuenced by plant properties as aﬀected by variety and experimental studies on wheat [11, 20], barley [21], and the crop husbandry, including sowing rate, nitrogen rate, sunﬂower [22, 23] have provided a viable method to un- nitrogen timing, and plant growth regulator (PGR) appli- derstand the phenomenon and to predict the risk of lodging cation [25]. Moreover, other environmental conditions occurrence. As the main cause of the lodging is adverse which aﬀect plant growth such as topography, soil type, weather conditions, i.e., high rainfall and strong winds [19], sunshine soil moisture, temperature, pests, and diseases can several studies have addressed the issue of how variations in also aﬀect the plant biological properties [8, 25, 36]. )e meteorological parameters aﬀect lodging occurrences. Eas- contribution of each factor in the lodging process is hard to son et al. [24] reported that lodging is associated not only assess as the phenomenon is very complex. Nevertheless, with strong gusts (greater than 25 km/h (7 m/s)) but also Berry et al. [11] quantiﬁed the risk of lodging for wheat crops may occur in low wind speeds (16 km/h (4 m/s) or less). grown under diﬀerent treatments and showed the lodging Meanwhile, Berry et al. [25] demonstrated that lodging can timing and quantity can be estimated by a calibrated lodging be prevented or substantially reduced using appropriate model. At present, there is no enough data for oats to fully husbandry even in adverse weather conditions. In addition, quantify the impact of the full range of management impacts Baker et al. [12, 19] and Sterling et al. [9, 26] developed on lodging risk. probabilistic frameworks where the risk of lodging could be Based on the above, the aim of the current paper is to calculated via an understanding of the probability of crop investigate possible eﬀects of climate change on oat lodging failure in adverse weather conditions. However, it is not in the UK and the Republic of Ireland. )is study is part of a clear how these probabilities might vary in future. )e only wider research to study lodging in oats, funded by Teagasc notable work in this ﬁeld was by Martinez-Vasquez [27] who (the Republic of Ireland’s Agricultural and Food Develop- developed a lodging risk analysis building on the generalized ment Authority) [9, 37, 38]. )e project elaborates the lodging model [12] together with UKCP09 climate projector. generalised model developed in [12] to study the oat failure However, due to lack of knowledge about the parameters risk, for diﬀerent treatments/varieties as well as various required for the generalized lodging model for oats, the meteorological conditions (i.e., wind speed and rainfall). )e model used was not calibrated resulting in signiﬁcant approach used to investigate the aerodynamic parameters of inaccuracies in the risk calculation. Since the publication of the lodging model (Section 2.2) was also applied for other this work, a new version of the UK climate projector ongoing projects at the University of Birmingham to study (UKCP18) has been released which not only provides lodging in maize, oilseed rape, and rice, funded by UK updated projections but also includes both future precipi- Biology and Biotechnology Research Council (BBSRC) tation and wind projections. In addition, recent experi- [39, 40]. mental studies on oats have enabled a calibrated lodging Oat has been selected as the case study as the crop that model for oats [9]. has a high propensity to lodge in the UK and Ireland weather Historical climate observations show an average increase conditions [41–43]. Moreover, oat grains have been reported of 0.5%–1% per ten-year rainfall in most of northern as a rich source of vitamins, minerals, and antioxidant, as hemisphere’s mid and high latitudes [28]. Nevertheless, in well as having other health beneﬁts such as reducing the England and Wales, annual mean rainfall has not changed cholesterol level and blood sugar [44–46]. Consequently, oat noticeably since 1766, and seasonal precipitation seems to is cultivated in about 9% of crop tillage areas in Ireland [47]. show a decline in summer [29]. Additionally, historical data Although the percentage of cultivated farmlands of oats is demonstrate a signiﬁcant variation of rainfall in diﬀerent lower (about 1%) in the UK, it has the highest increasing rate years, whilst an overall increase in wintry precipitation can (7.8%) in the major cereal crops (wheat, barley, oat, and be observed [30]. In the Republic of Ireland, an increase in oilseed rape) [48]. )e methodology used in this research is annual rainfall in the North and West of the country and a given in Section 2, including an outline of historical data Advances in Meteorology 3 sources, the conceptual lodging model, and the prediction of 2016). )ese speciﬁc datasets were selected based on the future climate scenarios. Section 3 then outlines the de- availability of long-term data (1987–2016) and proximity to velopment of wind and rainfall probability distributions regions where oats are commercially grown (i.e., mainly the from historical data and describes the possible future eastern and southern parts of Ireland and Eastern Scotland, changes in these distributions due to climate change eﬀects. as well as Western and Southern England [50] (RSK ADAS Section 4 then presents an analysis of lodging risk, both for Ltd, personal communication, 2016)). )ese data were the current situation and for the predicted future climate. analysed to ﬁnd rainfall and wind probability density func- Finally, the implications of the results are discussed in tions (PDFs) which will be described further in Section 3.1. Section 5. 2.2. Lodging Model and Risk Calculation. In this section, the 2. Methodology generalized lodging model [12] is described brieﬂy since it is 2.1. Historical Data. To evaluate historical meteorological a key to understanding the risk of changes in climate and is conditions during the last three decades, data from 38 based on wind and rainfall probability density functions. In stations were collected from Met Eireann (the Irish Mete- this model, the external bending moment that a plant ex- periences as a result of the wind is compared with the plant’s orological Service) [35], the United Kingdom’s Meteoro- logical Oﬃce Integrated Data Archive System (MIDAS) stem and anchorage resistance [8]. Accordingly, two failure Land and Marine Surface Stations [49], and the Meteoro- velocities for the stem and root failure can be deﬁned. )e logical Oﬃce National Meteorological Archive (Met Oﬃce stem failure criteria can be written in the format of a stem National Meteorological Archive, personal communication, failure (lodging) velocity (U ), i.e., Ls 0.5 2 3 ω (X/g) σπa /4 􏼁 􏼐(1 − ((a − t)/a)) 􏼑n ⎛ ⎝ ⎞ ⎠ (1) U � , Ls 0.5 2 2 2 1 + ω (X/g)􏼁 0.5ρA X􏼁 (cos(αx/l) − cot α sin(αx/l)) 1 + I 4g + g (π/4θ)􏼁􏼁 n CF MB MR where ω � 2πf , f is the natural frequency, is the radial oat canopies and to obtain required aerodynamic parameters n n n frequency, X is the height of the centre of mass of the for the model. Full details relating to these experiments can canopy, g is the gravity acceleration, σ is the stem yield be found in [9, 37, 52]. Furthermore, additional experiments stress, a is the stem radius, t is the stem wall thickness, n is were undertaken to identify the plant-related parameters the number of stems per plant, ρ is the air density, A is the grown under diﬀerent varieties/treatments and various soil CF plant shear area for a plant in a canopy, α is a dimensionless conditions. )ese ﬁeld experiments were mainly based on parameter, x is the distance up to the stem from the ground, l agronomic measurement protocols developed by Berry et al. is the length of stem, I is the turbulence intensity, and θ is [53]. the damping ratio. Additionally, g and g are the gust Figure 1 illustrates graphically how equations (1) and MB MR factor of broad-banded stem moment and the gust factor of (2) can be interpreted. In Figure 1, the vertical axis resonant stem moment, respectively [51]. represents the daily rainfall (i), and the horizontal axis is Similarly, the failure root velocity known as root lodging the hourly mean wind speed (U). Various regions have (U ) can be deﬁned as been deﬁned in Figure 1. For example, the curve LR 0.5 (equation (3)) deﬁnes the lodging/no-lodging boundary cSd U � , and illustrates the relative contributions of rainfall and 􏼠 􏼡 LR 2 2 1 + ω (X/g)􏼁/ω (x/g)􏼁 0.5ρA X 􏼁 1 + 2Ig 􏼁 n n CF MB wind speed required for lodging to occur. )e curve is (2) given by where S is the soil shear strength, d is the eﬀective root diameter, and c is a constant. As the stem and root lodging i � 1 − 􏼠 􏼡i , (3) LR velocities (equations (1) and (2)) are based on a variety of crop parameters (e.g., natural frequency and drag area), where i is the daily rainfall and i is the reference rainfall diﬀerent experiments were undertaken to investigate these corresponding to zero wind speed. It should be noted that parameters in two separate ﬁeld trials—one in 2017 and one Figure 1 is plotted for a sample data from oats, and thus, the in 2018. )e experimental setup designed to study the curve and dashed lines can be replotted for other oat sample turbulent ﬂow over plant canopies and the dynamic of plant data or other crops. movement of crops included two sonic anemometers (to record wind velocity above the canopy) and two video )e risk of lodging can be obtained using integration of cameras to observe the crop’s movement. )e acquired wind joint (wind and rain) probability density function in the and video data were later postprocessed through standard region where the risk of lodging exists. Baker et al. [12] used wind engineering methods to study the turbulent ﬂow over a Rayleigh distribution for wind PDF given by 4 Advances in Meteorology 0.8 0.7 20 Root lodging 0.6 0.5 Root and stem 0.4 lodging 0.3 0.2 No lodging 0.1 U U LS LR 0 2 4 6 8 10 12 14 16 18 20 0 510 Rainfall (mm) U (m/s) Bansha (Ireland) Haddington (Scotland) Figure 1: Lodging regions in the daily rainfall/hourly mean wind Hereford (England) speed plane for a sample oat plant. Figure 2: Rainfall probability density function for selected Irish, Scottish, and English meteorological stations in the period from 2 U − U /λ ( ) 1987 to 2016 for June and July [35, 49] (Met Oﬃce National p(U) � e , (4) 􏼒 􏼓􏼒 􏼓 λ λ Meteorological Archive, personal communication, 2016). where p(U) is the PDF for (U) and λ is a parameter used to characterize the wind climate. )e Rayleigh distribution was and Scotland. In Figure 2, the horizontal axis illustrates preferred rather than the Weibull distribution since it en- rainfall, and the vertical axis shows the correspondent abled an analytical form of the lodging risk to be calculated. probability. )ese are for the months of June and July, when For the rainfall PDF, an exponential function was used: lodging events are known to occur. To identify an appro- priate function, a curve is ﬁtted on each station using − (i/m) (5) p(i) � 􏼒 􏼓e , MATLAB, and a double exponential was found to be the best representative function: where m is the mean daily rainfall and i is the daily rainfall. − bi − di At the time, equation (5) was a convenient expression; P(i) � ae + ce , (6) however, Baker et al. [12] emphasised the necessity of ad- ditional research in order to establish a more appropriate where i is the amount of daily rainfall, P(i) is the probability, representation for the rainfall PDFs [12, 26]. and a, b, c, and d are site-dependent coeﬃcients. Despite the geographic variation of rainfall, it was found that the overall PDFs can be deﬁned at regional scales for Ireland, Scotland, 2.3. Future Scenario Projection. UKCP18 provides the most and England (Table 1). Furthermore, it was observed that, recent projections for future climate conditions in the through appropriate selection of the values of a, b, c, and d, coming decades based on a number of data sources and an overall curve could be obtained which represented all of emission scenarios for diﬀerent periods and locations [54]. the data irrespective of location to a reasonable degree of Emission scenarios in UKCP18 are deﬁned as Representative accuracy, i.e., 0.2%. It should be noted that the values of the Concentration Pathways (RCPs), which determine the aforementioned constants are not independent but have amount of greenhouse gases causing certain radiative been chosen to ensure that they are consistent with the forcing at the high altitude of the Earth’s atmosphere by cumulative density function (CDF) tending to unity as the 2100, in comparison to preindustrial levels [55]. Four forcing rainfall tends to inﬁnity. levels are used: 2.6, 4.5, 6.0, and 8.5 W/m , which are deﬁned A similar analysis was undertaken for the wind speed, as RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios [33]. and it was observed that a Weibull distribution, given by Land projections in the UKCP18 include probabilistic, k− 1 global, and regional outcomes. Probabilistic projections are k U − (U/λ) (7) P(U) � 􏼒 􏼓 e , designed to demonstrate the ranges of uncertainty in the λ λ outputs for a certain period, location (region), and diﬀerent emission scenarios. Global/regional projections both use best represented the data. Here, λ and k are parameters RCP 8.5 and illustrate 28/12 climate projections at a 60 km/ governing the scale and shape of the distribution, respec- 12 km grid resolution, respectively [56]. tively. Figure 3 illustrates the results of the analysis for 10 stations in Ireland and the UK together with the ﬁnal curve used to represent all data (λ � 4.4, k � 1.8). )e largest 3. Climate Data and Predictions diﬀerence between the actual data and the ﬁtted curve is 3.1. Wind and Rainfall PDFs. Figure 2 shows sample data ∼12% and occurs in low-speed conditions, i.e., conditions relating to PDFs for selected stations in Ireland, England, when lodging risk is minimum. i (mm/day) No lodging Probability Advances in Meteorology 5 Table 1: Coeﬃcients for regional and overall representative curves similar outcomes, only data corresponding to RCP 2.6 are for rainfall PDFs and corresponding curve diﬀerence with actual presented here. )e ﬁgure shows drier conditions for data. southern regions of England in June and July, while western regions of Scotland are projected to experience wetter cli- a b c d Mean-squared error mate in June. Ireland 0.60 0.75 0.02 0.01 0.002 A probabilistic projection tool was employed to analyse England 0.70 0.88 0.03 0.15 0.002 data at 16 stations across southern and western areas of Scotland 0.60 0.75 0.03 0.15 0.002 England, as well as eastern and southern regions of Scotland Overall 0.62 0.83 0.03 0.12 0.002 (areas where oats are commercially grown). Results illustrate that for all stations, diﬀerent emission scenarios have only a slight eﬀect on precipitation rate anomaly (%), although the 0.25 diﬀerence between emission scenario plots is larger in July. Figure 5 illustrates an example of a CDF for monthly rainfall 0.2 changes at a sample weather station (Herford, England) for diﬀerent emission scenarios. More details, regarding the th th anomaly ranges from 10 percentile to 90 percentile, are 0.15 presented in Table 2. As illustrated in Figure 5, diﬀerent RCPs result in diﬀerent CDFs, which are perhaps not too surprising given the complexity of the climate model and the 0.1 uncertainty associated with this particular area. 0.05 3.2.2. Global Projections (60 km Resolution). Global pro- jections are based on 28 climate models at 60 km grid resolution including 15 simulations of the Met Oﬃce Hadley 0 5 1015202530 Centre model (HadGEM3-GC3.05), and 13 other outputs Wind (m/s) are adopted from the Intergovernmental Panel on Climate Fitted curve South farnborough (England) Change’s 5th Assessment Report, CMIP5-13 [30]. Using Lossiemouth (Scotland) Cork airport (Ireland) these two series of climate models increases the range of Figure 3: Wind probability density function 1987–2016 for se- plausible futures. lected Irish, Scottish, and English meteorological stations [35, 49]. )e HadGEM3-GC3.05 is a coupled atmosphere-ocean conﬁguration, including diﬀerent levels of stratosphere, atmospheric chemistry, vegetation, and ocean biology [33]. 3.2. Future Climate Projection. Projections of UKCP18 show In each model’s output, all plausible variants perturbed in warmer, wetter winters and hotter, drier summers for the the given climate model conﬁguration, building a perturbed UK. All the regions of the UK are predicted to face higher parameter ensemble (PPE) [57]. )ese variants can be temperatures, and the increase is greater in summers rather classiﬁed as convection parameters, mountain eﬀects, at- than in winters. Perhaps not surprisingly, geographic and mospheric boundary layer conditions, cloud radiation and seasonal variation of precipitation is likely to continue to microphysics features, and aerosol parameters which can be exist in future. )is section discusses results from the found in [57]. Later, PPEs were ﬁltered to provide highest UKCP18 where probabilistic, global, and regional projec- plausibility and diversity of outputs, producing 15 simula- tions are presented in Sections 3.2.1–3.2.3. tions [57]. In order to add diversity to the projections, 13 CMIP5 3.2.1. Probabilistic Projections (25 km Resolution). models (CMIP5-13) are also provided simulating global and Probabilistic projections merge historical weather data with zonal mean temperatures in the Earth’s surface, global trend climate models and statistics at 25 km grid resolution to of sea surface temperature (SST) bias and Atlantic Merid- provide outputs for diﬀerent emission scenarios and are an ional Overturning Circulation (AMOC), as well as clima- appropriate tool to study the eﬀect of diﬀerent RCPs on tological conditions over the North Atlantic and Europe [57]. Table 3 shows models incorporating in CMIP5-13 and precipitation anomalies. However, the tool provides data only for UK areas and does not include projections for the associated modelling groups. Irish Republic. Figure 4 illustrates precipitation rate Figure 6 shows results of global projection from these 28 anomalies in June and July, respectively, in all the UK areas climate models at 60 km resolution. In addition to model using the 1981–2010 baseline and geographic variations in designations described in Table 3, 15 PPEs from HadGEM3- rainfall anomalies can be clearly observed. )e ﬁgure in- GC3.05 are presented as ﬁve-digit numbers. )ese numbers th th th cludes three panels for 10 , 50 , and 90 percentiles, and are allocated to name selected PPEs by UKCP18 designers each square indicates the range of change in the area. For and do not have any signiﬁcance (Met Oﬃce, personal example, a grid showing 10% precipitation anomaly rate in communication, 2019). )e results illustrate that in the most th 50 percentile represents 50% probability that monthly severe predictions, southern regions of Ireland might get rainfall will increase by less than 10% [54]. As all RCPs show 30–40% drier in June and July. However, some models Probability 6 Advances in Meteorology th th th 10 percentile 50 percentile 90 percentile –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (a) th th th 10 percentile 50 percentile 90 percentile –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (b) Figure 4: Monthly average precipitation rate anomaly (%) for RCP 2.6 from 2020 to 2049 using baseline 1981–2010 and scenario RCP 2.6 (a) in June and (b) in July. predict a diﬀerent trend suggesting an increase of precipi- With respect to England, the majority of the projections tation of up to 40% increase in precipitation. In general, the suggest that June will be 10% to 30% drier, although regions majority of the models show a predicted diﬀerence of ±20% in the South could experience up to 30% increase in rainfall. in June and July. In July, most models show drier conditions (up to 60% Advances in Meteorology 7 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 –60 –40 –20 0 20 40 60 –60 –40 –20 0 20 40 60 Precipitation rate anomaly (%) Precipitation rate anomaly (%) RCP 2.6 RCP 6.0 RCP 2.6 RCP 6.0 RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5 (a) (b) Figure 5: Cumulative distribution function for precipitation rate anomaly in Herford, England, for RCPs used in the UKCP18 in (a) June and (b) July. are presented for 2020–2049 and 2050–2079 periods. Prob- especially in southern parts) whilst the extreme cases suggest a 40% increase in rainfall. Finally, precipitation in Scotland is abilistic projections demonstrate anomaly ranges from the th th expected to experience ±30% and ±20% anomalies in June 10 percentile to 90 percentile, while regional and global and July, respectively. variations represent the largest anomaly projected. For the same periods (2020–2049 and 2050–2079), wind predictions show ±1 m/s change in all studied areas. )e Republic of 3.2.3. Regional Projections (12 km Resolution). Regional Ireland is mainly projected to face a reduction in average wind projections are based on HadGEM3-GC3.05 and use 12 PPEs speed except in a few areas on the northern and southern in a downscaled area in comparison to the global projection coasts. Results for the UK appear to be spatially variable. which enables the eﬀect of physiographic features including mountains, coasts, urban areas, lakes, and rivers being con- sidered. Figure 7 illustrates precipitation maps of anomalies 4. Lodging Risk Assessment for regional projections for the RCP 8.5 scenario. )ese 4.1. Lodging Risk in Current Climate Conditions. To inves- outputs are generated by 12 projections from the Met Oﬃce tigate the risk of lodging in autumn-sown oats in current Hadley Centre model at 12 km scale resolution. In general, conditions, a database of 1,000 synthetic plants was gen- most models show the UK and Ireland will tend to experience erated based on mean values and standard deviations of drier condition in June and July with variations corre- plant parameters including panicle area, stem radius, stem sponding to Ireland between ±20% and up to 50% reduction wall thickness, centre of gravity, eﬀective root diameter, in the monthly rainfall in southern regions. Furthermore, the anchorage depth of the rooting system, and the number of majority of the models implies southern and western regions stems per plant provided in Table 4 (in keeping with the of England will become drier in June and July while variation approach of [11]), i.e., for each synthetic sample, the plant of projections in eastern parts is from 40% drier to 50% wetter parameters were randomly generated assuming a corre- condition. Finally, Scottish areas are projected to experience sponding normal distribution (see [11]). Experience has mainly ±30% anomaly in precipitation. shown that 1,000 samples are signiﬁcant to ensure that the Figure 8 shows the monthly average wind speed anomaly results are statistically independent, and thus, relevant at 10m above the ground from 2020 to 2049 for June and conclusions can be drawn. In order to provide the input to July. )is ﬁgure illustrates the wind speed change in both the database, plant data (mean and standard deviations) England and Ireland is±1 m/s, i.e., a relatively small change. were obtained from ﬁeld experiments undertaken in 2016- A slight increase is observed in Scotland, but again, this 2017 at Knockbeg, County Laois, the Republic of Ireland predicted increase is small and from a lodging perspective is ° ° (52.86 N, 6.94 E, 54 MSL). Plants were raised from two unlikely to be signiﬁcant. varieties (an oat variety susceptible to lodging (Barra) and an oat variety with moderate resistance to lodging (Husky)) 3.2.4. Summary of Projections. UKCP18 outputs produced grown under diﬀerent combinations of agronomic treat- by diﬀerent models are summarized in Table 2, where results ments designed to create a range of lodging pressures. )us, Probability of being less than (%) Probability of being less than (%) 8 Advances in Meteorology − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − Table 2: Monthly average precipitation rate anomaly (%) using baseline 1981–2010 in June and July. 2020–2049 2050–2079 Month Region Projection RCP 2.6 RCP 4.5 RCP 6 RCP 8.5 RCP 2.6 RCP 4.5 RCP 6 RCP 8.5 Probabilistic 26% to +17% 24% to +19% 24% to +19% 27% to +19% 37% to +3% 42% to 2% 43% to +2% 50% to +4% South England Regional 50% to +50% 70% to +20% Global 40% to +40% 70% to +20% Probabilistic 31% to +22% 30% to +22% 29% to +22% 32% to +21% 38% to +4% 43% to +3% 44% to +3% 51% to +4% West England Regional 50% to +30% 70% to +10% June Global 40% to +40% 70% to +20% Probabilistic 18% to +22% 17% to +22% 17% to +23% 17% to +22% 25% to +14% 27% to +14% 27% to +14% 29% to +14% Scotland Regional 30% to +60% 40% to +30% Global 40% to +40% 50% to +30% Regional 50% to +20% 70% to 0% Ireland Global 40% to +30% 60% to +20% Probabilistic 43% to +16% 44% to +16% 44% to +17% 47% to +14% 44% to +12% 49% to +9% 49% to 10% 56% to +7% South England Regional 40% to +50% 60% to +10% Global 50% to +50% 70% to +30% Probabilistic 27% to +19% 29% to +19% 28% to +19% 31% to +18% 37% to +9% 41% to +8% 40% to +8% 47% to +5% West England Regional 40% to +20% 60% to +10% July Global 30% to +50% 60% to +30% Probabilistic 28% to +22% 28% to +22% 28% to +22% 30% to +21% 38% to +15% 40% to +17% 40% to +17% 47% to +19% Scotland Regional 40% to +30% 50% to +10% Global 30% to +40% 50% to +20% Regional 50% to +30% 60% to 10% Ireland Global 40% to +40% 60% to +40% Advances in Meteorology 9 Table 3: )e CMIP5-13 models used in UKCP18 under the RCP wind speed and rainfall in the future. We will use this range 8.5 scenario [57]. of variations in the analysis of risk that follows. Using the range of values of rainfall and wind speed Model Modelling group calculated above, revised PDFs for these variables can be designation determined, corresponding to likely future climate condi- Centro Euro-Mediterraneo per I Cambiamenti CMCC-CM tions. )ese were calculated by calculating cumulative dis- Climatici tribution functions through integrating the PDFs for current Beijing Climate Centre, China Meteorological BCC-CSM1 conditions, applying the predicted rainfall and wind Administration Canadian Centre for Climate Modelling and anomalies to these CDFs and then diﬀerentiating them to CanESM2 Analysis obtain PDFs relevant to future conditions. Typical values are Commonwealth Scientiﬁc and Industrial shown in Figure 9. ACCESS1-3 Research Organization (CSIRO) and Bureau of Risk calculation for oats was then carried out using the Meteorology (BOM), Australia lodging model described in Section 2.2. Using the new wind CESM1-BGC Community Earth System Model Contributors and rainfall PDFs and averaged agronomic values (Table 4), Centre National de Recherches the failure probability in each anomaly range is obtained. Met ´ eorologiques/Centre ´ Europeen ´ de CNRM-CM5 Figure 10 shows the risk contour for Irish conditions, which Recherche et Formation Avanc´ee en Calcul is similar to what is found for England and Scotland. )e Scientiﬁque ﬁgure shows the lodging risk is more aﬀected by changes in EC-EARTH EC-EARTH consortium NOAA Geophysical Fluid Dynamics the wind speed compared to the rainfall. )us, if the wind GFDL-ESM2G Laboratory conditions do not change considerably in the future, then the HadGEM2-ES Met Oﬃce Hadley Centre risk of lodging is unlikely to vary signiﬁcantly from present IPSL-CM5A- conditions. Institute Pierre-Simon Laplace MR MPI-ESM-MR Max-Planck-Institut fur ¨ Meteorologie 5. Discussion MRI-CGCM3 Meteorological Research Institute CCSM4 National Centre for Atmospheric Research )e above analysis has illustrated that the probability of adverse weather occurrence is a key factor in determining four diﬀerent synthetic databases were generated corre- lodging risk. )is paper suggests that a double exponential curve can suﬃciently represent wind and rainfall PDFs to an sponding to variety/seed rate combinations outlined in Table 5, as well as a database is generated based on Table 4 acceptable level of accuracy. Although these functions were evaluated for the months of June and July, it is noted that a associated with natural variations of oat parameters. As illustrated in equations (1) and (2), in addition to the slight change in the studied period would not signiﬁcantly alter the obtained PDFs. agronomic parameters (stem strength etc.), the lodging model also relies on soil and aerodynamic parameters, including drag Monthly precipitation projections elucidated a dramatic change especially in the second half of the current century, coeﬃcient, air density, natural frequency, damping ratio, and turbulence intensity, provided in Table 4. )e aerodynamic which can reach up to 70%, while monthly average wind speed anomalies were expected to vary by only ±1 m/s. parameters were evaluated using standard wind engineering methods [9], while soil shear strength was measured in the Although such projections are always associated with larger th th studied site. Table 5 shows the 10 uncertainties in modelling longer periods, it was found that –90 percentile range as th even such a sharp rainfall variation would aﬀect the lodging well as the 50 percentile lodging risks in the generated synthetic databases. A relatively large spread of risk in each risk by less than 5%, while 1 m/s reduction/increase in mean hourly wind speed could change the failure risk more than sample variety/seed rate can be observed and illustrates that diﬀerent husbandry treatments/varieties can result in con- 10%. )us, demonstrating that in general, the wind speed is the governing parameter for lodging. siderable diﬀerences in failure probabilities—this is an im- portant result which will be discussed in the following sections. )e lodging risk was evaluated for the whole range of wind and rainfall variation, with the majority of climate )e risk of lodging might also change in diﬀerent sites due to diﬀerences in meteorological conditions, especially models indicating a decline in the average wind speed in Ireland. Hence, it is reasonable to assume that on average, wind PDF (Figure 3). Accordingly, the lodging risk was the risk of oat lodging is likely to reduce. However, the same assessed for average agronomic values (Table 4) for 9 studied conclusion cannot be drawn for the UK. Although these stations in England, Scotland, and Ireland (3 stations in each conclusions are made based on variations in monthly av- region). )e risk assessment was undertaken using the representative (overall) rainfall PDF (Table 1) and site- erages, it is expected that extreme wind events (e.g., storms) will not aﬀect the aforementioned risk assessment as a re- speciﬁc wind PDFs. Results show the lodging risk range in English stations is 10–17%, in Irish stations is 26–27%, and lation between climate change and summer storminess in the UK which has not been established [33]. Moreover, in Scottish stations is 18–27% stations. North Atlantic cyclones are expected to reduce by 10%, the number of extreme storms is projected to be rare, and the 4.2. Lodging Risk in Future Climate Conditions. Based on the majority of such adverse weather conditions is expected to data of Table 2, Table 6 illustrates the possible variation in happen in winter [32]; all implying UK and Ireland will not 10 Advances in Meteorology 02491 02305 01649 bcc-csm1 02868 EC-EARTH HadGEM2-ES CMCC-CM ACCESS1-3 01843 01935 IPSL-CM5A-MR 02832 02335 CESM1-BGC 00834 01554 MPI-ESM-MR 02123 00000 02242 GFDL-ESM2G 01113 MRI-CGCM3 CCSM4 CanESM2 CNRM-CM5 00605 –80 –70 –60 –50 –40 –30 –20 –10 0 102030405060 Precipitation rate anomaly (%) (a) 02491 01649 ACCESS1-3 02832 00000 02123 01554 bcc-csm1 02868 01935 CESM1-BGC 00834 CCSM4 01113 02242 02305 HadGEM2-ES 02335 MPI-ESM-MR CMCC-CM IPSL-CM5A-MR 01843 00605 GFDL-ESM2G MRI-CGCM3 EC-EARTH CanESM2 CNRM-CM5 –80 –70 –60 –50 –40 –30 –20 –10 0 102030405060 Precipitation rate anomaly (%) (b) Figure 6: Monthly average precipitation rate anomaly (%) from 2020 to 2049 using baseline 1981–2010 and scenario RCP 8.5 for (a) June and (b) July. ()e four-digit number/letters above the projections correspond to the relevant models used for the projections). face more frequent summer storms in comparison to the metrological conditions can be described by representative current conditions. function which were found accurate enough to be used in )is research quantiﬁed the oat-lodging risk for the ﬁrst the British and Irish farmlands, more studies are recom- time based on experimental data obtained from experi- mended to investigate if the plant properties change in ments undertaken in Carlow, Ireland. Although the diﬀerent sites/years. Moreover, temperature, sunshine, Advances in Meteorology 11 02491 01649 02305 01935 02868 00000 01843 01554 02335 02123 02242 01113 –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (a) 02491 00000 01649 02123 01554 01935 01113 02868 01843 02242 02305 02335 –80 –70 –60 –50 –40 –30 –20 –10 0 10 20 30 40 50 60 Precipitation rate anomaly (%) (b) Figure 7: Monthly average precipitation rate anomaly (%) in the period from 2020 to 2049 using baseline 1981–2010 and scenario RCP 8.5 for (a) June and (b) July. ()e ﬁve-digit numbers above each map indicate the PPE model used for the projection). weather and soil moisture, plant diseases, pest, and other these parameters and the potential eﬀect of climate change environmental parameters can aﬀect attributes of plants on these factors can also be a point of interest for future associated with lodging [8, 25]. )e natural variation of research studies. 12 Advances in Meteorology 02491 01935 02868 00000 01843 02335 02305 01649 01554 02242 02123 01113 –2 –1 012 –1 Wind speed anomaly at 10 m (ms ) (a) 00000 01843 02305 01649 01554 02335 02123 02868 01935 02491 02242 01113 –2 –1 012 –1 Wind speed anomaly at 10 m (ms ) (b) Figure 8: Monthly average wind speed anomaly at 10 m from 2020 to 2049 using baseline 1981–2010 and scenario RCP 8.5 for (a) June and (b) July. Importantly, from a grower’s perspective, this analysis a result of plant variations and growing practices—this is an has demonstrated that the eﬀect of climate change on interesting result and enables the potential change in lodging lodging risk is similar to the variation in risk which occurs as to be appreciated at farm scale. Advances in Meteorology 13 Table 4: Agronomic, aerodynamic, and soil parameters (letter abbreviation for parameters used can be found in Section 2.2). Agronomic parameters Mean Standard deviation n 2.02 0.7 a (mm) 3.2 0.6 t (mm) 0.9 0.3 L (m) 1.4 0.3 X (m) 0.64 0.081 d (mm) 58 9.6 l (mm) 91.9 10 Aerodynamic/soil parameters ρ (Kg/m ) 1.2 — A (m ) 0.016 0.002 CF θ 0.1 0.04 f (Hz) 1.1 0.03 I 0.5 0.16 S (K Pa) 35 5.1 Table 5: Lodging risk variation in diﬀerent treatments and seed rates. 2 th th th Variety Seed rate (m ) Risk range (10 –90 percentile) 50 percentile risk Susceptible 200 seeds 0.06–0.60 0.21 Susceptible 500 seeds 0.11–0.62 0.32 Moderate resistance 200 seeds 0.03–0.46 0.20 Moderate resistance 500 seeds 0.04–0.60 0.26 Table 6: Monthly average wind and rainfall rate anomaly (%) generated by most of the models using baseline 1981–2010 in June and July. Most likely monthly anomaly to happen Region 2020–2049 2050–2079 Rain Wind Rain Wind South England − 30% to 10% ±1 m/s − 40% to 0% − 1 m/s to 0 West England − 30% to 10% ±1 m/s − 40% to 0% ±1 m/s Ireland ±20 − 1 m/s to 0 − 40% to 0% − 1 m/s to 0 Scotland ±20 ±1 m/s − 40% to 10% ±1 m/s 0.9 0.3 0.8 0.25 0.7 0.6 0.2 0.5 0.15 0.4 0.3 0.1 0.2 0.05 0.1 0 0 0 510 15 20 0 5 101520 Rainfall (mm) Wind speed (m/s) Current conditions Current condition 70% reduction (extreme anomaly) +1 m/s 30% reduction (most likely anomaly) –1 m/s 10% increase (most likely anomaly) 50% increase (extreme anomaly) (a) (b) Figure 9: (a) Rainfall and (b) wind PDFs in the current and future climate conditions for Haslemere station (South England). Probability Probability 14 Advances in Meteorology 1 ceda.ac.uk for the UK. Additionally, some meteorological data for the UK are provided by Meteorological Oﬃce National Meteorological Archive. Requests to access this archive should be made to Meteorological oﬃce, enquiries@ metoﬃce.gov.uk. Climate change projection data are available from http://ukclimateprojections.metoﬃce.gov.uk. Conflicts of Interest )e authors declare that there are no conﬂicts of interest –1 regarding the publication of this paper. 30 70 0 110 150 Rainfall changes (%) Acknowledgments 0–10 20–30 )is paper was dedicated to one of the authors, John Finnan, 10–20 30–40 who died in an aircraft accident during the course of the Figure 10: Lodging risk based on wind and rainfall anomalies. research. John’s expertise and input was instrumental for the agricultural elements of the research. However, John also had an uncanny knack of delivering a constructive and well- 6. Concluding Remarks timed challenge on the meteorological aspects of the re- search, thereby ensuring that those authors who claim to )is paper has examined the impact that climate change profess such expertise reﬂected long and hard on how their could have on lodging in oats. )e following conclusions can message could be delivered more appropriately—something be made: which is key to ensuring successful transdisciplinary re- search. Perhaps, this is something that we all might like to (i) A double exponential PDF can be used to represent reﬂect on as subject boundaries become less opaque—he rainfall with an acceptable degree of accuracy. would have liked that. )anks are expressed to Walsh (ii) )e risk of oat lodging occurring within a speciﬁed Fellowship which funded the ﬁrst author and to those or- period of time (typically June-July) is a complex and ganisations mentioned in the paper that provided their data nonlinear interaction of wind and rain. free of charge. 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The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

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The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

The Potential Impact of Climate Change on Oat Lodging in the UK and Republic of Ireland

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