On March 23rd 2020 I was due to give a presentation with the above title to a Transportation Futures workshop at the University of Birmingham. Unfortunately the workshop has been cancelled because of the ongoing corona virus situation. Thus I am posting the slides I would have used here. In order that the file isn’t impossibly large for downloading, the slides are in handout form with the video clips removed. A brief commentary follows.
Slide 1 – Introduction
Slides 2 to 4 – these describe the Bridgewater Place incident in Leeds in 2013 in which a lorry blew over and killed a pedestrian that was the catalysts for much of the recent work that has been carried out. A report on the incident can be found here.
Slide 5 gives typical comfort and safety criteria – the red outline indicates the safety criterion of relevance here.
Slides 6 to 10 illustrate recent work on an EPSRC funded project entitled “The safety of pedestrians, cyclists and motor vehicles in highly turbulent urban wind flows” to investigate wind effects on people. This project involved wind tunnel testing, CFD analysis and the measurements on volunteers in windy conditions, which are reported here. Slide 7 shows a photo of Dr. Mike Jesson of the University of Birmingham who had responsibility for the work with volunteers. Measurements were made with shoe-mounted sensors to measure the volunteer’s walking pattern, and back-mounted sensors to measure acceleration. The results are shown in figures 8 and 9 and summarized in figure 10. The latter shows that at all gusts speeds above 6m/s stride “swing width” variation could be measured in some volunteers, where the volunteers subconsciously adjusted their stride to take account of crosswinds. The frequency of such events rose from around 40% at gust speeds of 6m/s to 100% at gust speeds of around 15m/s. Lateral accelerations of the torso first appeared at about 10m/s and reached a frequency of 100% at 17m/s. Actual instability of volunteers was only rarely recorded, but seemed to begin at gusts of around 15m/s. In general however, there was not enough data to draw firm conclusions. Perhaps typically for such measurements, the period of the project proved to be quite calm in wind terms overall.
Slide 11 is a re-iteration of the safety criteria – all work of the type described above needs ultimately to be expressed in very, very simple terms to be useful.
Slides 12 to 14 show the limited work that has been carried out on the effect of cross winds on cyclist safety – wind tunnel and CFD work supervised by Prof Mark Sterling and Dr Hassan Hemida whose pictures are shown in figure 3, to measure the aerodynamic forces on cyclists in cross winds, and some full scale work carried out under the EPSRC project, together with associated calculations of cyclist behavior. This work suffered even more than the pedestrian measurements from lack of suitable wind conditions and the results must be regarded as inconclusive.
Slides 15 and 16 begins the discussion of road vehicles in cross winds, with the latter showing the wind speed restrictions on Skye Bridge.
Slides 17 to 19 illustrate the various methodologies for determining crosswind forces on road vehicles – full scale, wind tunnel and CFD. The former were carried out by Dr. Andrew Quinn, whose photograph is shown on Slide 17. These results lead to the curves of accident wind speed against wind angle shown on slide 19, which can be used to develop wind speed restrictions.
Slides 21 to 24 summarise the study of bridge wind speed restrictions described in another post here. In finalizing restriction strategies operational conditions for specific bridges become very important, and in particular the ease or otherwise of restricting specific types of vehicle and not others.
Slides 25 to 29 briefly describe the wind effect on trains. Methods of determining the aerodynamic forces are illustrated in figure 27, where the University of Birmingham moving model TRAIN rigis shown. These results were obtained by Dr Dave Soper, whose photo is shown on the slide. These forces can be used to calculate the curve of accident wind speed against vehicle speed in slide 28. The practicalities of imposing speed restrictions are illustrated in slide 29.
The overall message of the presentation was that, although investigations to determine the underlying physical processes involved are very important, the translation of the results into practice needs to take account of the sometimes severe operation constraints.
Around the UK, there area number of relatively long and high bridges across river estuaries, that all operate some sort of traffic restriction protocol in high wind conditions, to limit the risk of vehicle accidents. In this post, I will attempt to collate publically available information on these traffic restriction protocols to assess their similarities and differences. It will be seen (surprisingly in my view) that this information is not at all easy to find and sometimes does not seem to be in the public domain. .
The bridges that will be considered are shown in Table 1, which gives name, location, construction type and length. Pictures of them are given in figure 1. It can be seen that, with the exceptions of the Cleddau Bridge in South Wales and the Skye Bridge in Scotland, these are all over a kilometer long. The construction types vary, from concrete boxes on large numbers of concrete piers to long span suspension and cable stay structures. Only two bridges in the table have protection for vehicles against cross winds – the Prince of Wales (Second Severn) Bridge and the Queensferry Bridge in Edinburgh. All the bridges in the table have Wikipedia entries, which give further details of planning, construction and operation.
The data for wind speed restrictions was found from a variety of sources – official documents, FOI releases, newspapers etc. The information that has been obtained is shown in Table 2. Most have a similar form, with different levels of restriction being used as the gust wind speed increases – vehicle speed limits, lane closures, restrictions to various classes of vehicles, and total closure. Most seem to base the wind speed values on local anemometers, although it is usually not clear where these are sited, and neither is the period of the gust given. Thus the values that are given are not strictly comparable with each other in absolute terms.
From table 2 it can be seen that no data could be obtained for the Kessock Bridge, the Humber Bridge or the Prince of Wales (Second Severn) Bridge. With regard to the latter, vehicles crossing the bridge are shielded by wind fences and the bridge has not had to impose restrictions on traffic during its lifetime. Kessock probably has the same sort of traffic restriction strategy as the other Scottish bridges, as Transport Scotland operates a common approach. From press reports it seems that Humber has some sort of vehicle speed limit and high-sided vehicle restriction strategy, although it has not been possible to determine the wind speeds at which the different measures are put into place. . Also note that Queensferry has much higher values of wind speed for restrictions than the other bridges, again due to the fact that vehicles are protected by wind fences.
For the other bridges, there seems to be a general consistency in the information shown, with vehicle speed limits of either 30mph or 40mph imposed when the wind gusts over 35 to 50mph. Vehicle restrictions begin at gusts of around 45mph to 60mph, with double deck buses and high sided vehicles being restricted at the lower gust speeds. Further restrictions may be imposed on vehicles of different types, before overall bridge closure at wind speeds of 65 to 80mph. Some bridges use different gust speeds for cross winds and for headwinds. Orwell Bridge for example applies the crosswind criterion if the wind gust direction is from a sixty degree segment centred on the direction normal to the bridge. The Queen Elizabeth II Bridge at Dartford uses similar strategies to inform speed limits, lane closures, vehicle restrictions and bridge closure.
The restriction strategies depend very much on the nature of the traffic over the bridge and its location. For example, if only some vehicles are to be restricted, then some method of filtering them out and diverting them is required, which needs to take place at some distance from the bridge. Such procedures are in operation at Severn, Erskine, Humber and the Queen Elizabeth Bridges amongst others. Clearly ease of identification of vulnerable vehicles is required – see figure 2 for the Humber Bridge. Other bridges simply base their protocols on vehicle height eg 1.9m for Cleddau and 2.1m for Severn.
Orwell Bridge operates a very simple strategy, with different gust speed triggers for crosswinds and headwinds, leading to complete closure, without any restrictions for, say, high sided vehicles at lower wind speeds. This arises because of the urban nature of its surroundings, which makes vehicle filtering difficult. This has led to a considerable number of closures in recent years, and much public concern. Recently both numerical and wind tunnel studies have been carried out to investigate ways in which this strategy can be modified, perhaps through the use of speed limits, lane restrictions or barriers. The details of these studies have not been released to date but may prove of some interest. Studies to relax the restrictions on Skye Bridge have also been recently carried out following frequent closures and public complaints.
As can be seen, the various restriction strategies are in general quite simple and easy to operate. This inevitably means that they are conservative and largely based on the most vulnerable vehicle – usually unladen high sided vehicles. There are in fact methods available for discriminating between vehicle types and vehicle weights – see the recent paper by Baker and Soper (2019) for example. This gives a method for determining a curve of accident wind speed against vehicle speed for specific vehicle type and weight, based on which restrictions strategies for any particular vehicle can be determined. However operational constraints make the full utilisation of such methods difficult. Until such time as vehicle type and vehicle weight can be automatically determined by (say) remote visualisation techniques and dynamic weight determination, and vulnerable vehicles can be suitably diverted, then the use of simple methods such as those currently adopted will remain the best that can be achieved.
This post is intended to start a discussion – and ideally identify what data might be available to address this problem further. The analysis presented is preliminary in nature, and could almost certainly be refined. I would really value a discussion of this with colleagues who read it.
In studies of road and rail vehicles in cross winds, some estimate of the risk of an accident is often required. If the critical accident wind speed for a particular vehicle is known, then my approach in the past has been to use the probability distribution for the hourly mean wind speed (assumed to be a Weibull distribution) and the probability distribution for the turbulence fluctuations around this average (assumed to be a normal distribution) to calculate the percentage of time that this critical value is exceeded, through a convolution of the two distributions. Additionally, when wind-warning systems are being developed, the question often arises as to what would be an appropriate mean wind speed at which to limit vehicle movements. This can be derived by calculating the percentage of time that the critical wind speed is exceeded from the probability distributions for turbulence fluctuations, for a range of mean wind speeds, and then choosing a value that has an acceptable level of risk.
In some recent work that I have carried out for a particular client, it has become clear to me that this approach is not really adequate – an example of practical reality not always conforming with attractive theoretical approaches! Both road and rail vehicles require a gust to be above the critical value for a specific period of timebefore an accident occurs. This period of time is usually between 0.5s and 3s, the time it takes for a vehicle to actually blow over. Thus in determining the risk of an accident what is really required is some idea of the number of times the critical wind speed is exceeded, N, for more than (say) T seconds for a particular mean wind speed U. This is not the same as the proportion of time for which the critical wind speed is exceeded, as some these exceedances will often last for less than T seconds. If the probability of N for any particular U is known, then this can be convoluted with the probability distribution for U to calculate the overall risk, or used to determine an appropriate value of U for wind warning systems.
To the best of my knowledge, the specification of the number of gusts N lasting greater than a specific time T for a particular mean wind speed has not been investigated in the past – but if any reader knows of such work, I would be glad to hear of it. In this post, I present the results of a preliminary investigation into this problem.
In what follows, I will use two experimental wind datasets as follows.
Data from that late 1990s obtained at the Wind Engineering field site at Silsoe Research Institute, and in particular two one-hour datasets (Silsoe 1 and Silsoe 2) with wind velocities measured at 10Hz at 3, 6 and 10m above the ground, for 10m wind speeds of 9.7 and 10.5m/s.
Data from Storm Ophelia in 2017, obtained from measurements at the top of the Muirhead Tower at the University of Birmingham, 72m above the ground, measured at 10Hz, for mean hourly wind speeds of 10.4, 12.5 and 13.8m/s (Birmingham 1, Birmingham 2 and Birmingham 3). With thanks to Dr Mike Jesson of the University of Birmingham for making this data available
The basic statistics for each hour of data is given in table 1.
From this table it can be seen that the Silsoe site has a surface roughness length (determined from velocity profiles) typical of smooth rural environments (0.005m), with turbulence intensities (standard deviation / mean values) that are consistent with such an environment and which fall slightly with height. The Birmingham data was obtained at one point high above a suburban environments, and thus the surface roughness length cannot be determined from a velocity profile, but can be expected to be an order of magnitude or more higher than at the Silsoe site. The turbulence intensity is similar to that measured at Silsoe, although the measurements were made at a much greater height above the ground. For the Silsoe data the probability distributions of the data all show a positive skew, whilst the Birmingham data show both positive and negative skew values that are much closer to zero. Typical examples of such distributions are shown in figure 1. The Silsoe near-ground distribution has a significantly longer upper tail, than the Birmingham values high above the ground, i.e. a significant skew towards the higher velocities. This may well be because of individual sweep events in the atmospheric boundary layer being more significant near to ground level. The normal distribution, which I have assumed in the past for my calculations, does not fit either dataset particularly well.
Analysis of exceedances
The approach to using this data has been to find, for each dataset, the number of exceedances N for T= 0.5s, 1s and 3s gusts above a range of velocity levels above the mean. To enable comparison between the different datasets, these velocities are expressed in terms of standard deviations above the mean, denoted by X. The results are shown in figure 2 for the Silsoe data and figure 3 for the University of Birmingham data. The following comments can be made.
N falls as T increases, which is only to be expected.
The value of X at which N falls to zero falls as T increases, as again is to be expected. This value is around 3 to 3.5 for the Silsoe data, and 2.5 to 3 for the Birmingham data, reflecting the form of the tail of the probability distributions discussed above.
For the Silsoe data, the results for the two datasets are very similar and there is an indication that N varies with height above the ground.
The Birmingham datasets also have similar results, and there is no discernable effect of wind speed in the data when plotted in this way.
Clearly the distributions of N have an upper limit. This can be characterized in two ways.
By the value of X for which the probability of the wind speed exceed T/3600, X1
By the highest value of X for which N>0, X2
Both these values of X are shown in table 2 for the various datasets. It can be seen that there is some variability in the results, which is inevitable as we are dealing with the tails of the distribution where data becomes discontinuous. In general the values for X1 are higher than those for X2, particularly for the near ground Silsoe data, suggesting that the use of simple probabilities rather than gust numbers may well significantly overestimate vehicle overturning risk. Both values fall as the time period T increases as would be expected, and the values for the Silsoe data are significantly higher than for the Birmingham data, which again follows from the difference in probability distributions. The equivalent values for X1 for a normal probability distribution are 3.64, 3.45 and 3.14, for T= 0.5, 1 and 3s respectively. It can thus be seen from Table 2 that the Silsoe values lie above the normal distribution values, and the Birmingham values lie significantly below them.
The data from figures 2 and 3 thus appears to be consistent and sensible, but the question then arises as to how this data can be parameterized to enable it to be used easily in calculations. After some trial and error analysis it was found that all the data for each site could be made to collapse around a single curve by plotting the combined variables NT and (X1-X)/X1 against each other. These variables seem sensible, as both are dimensionless, with the former giving a normalised value of number of exceedances, and the latter describing being the difference between specific gust velocities, and the value at which N must be zero. The results are shown in figures 4 and 5 for the Silsoe and Birmingham data respectively, using the measured values of X1 for each dataset. It can be seen there is much scatter, but the data collapse is reasonably good. The two sets of data do not however coincide, indicating the effects of the underlying shape of the probability distribution, and in particular the upper tails.
The region of most practical interest on these data collation is for a low number of events, since these represent conditions where the risk might be tolerable. Thus figures 6 and 7 thus show expanded versions of figures 4 and 5 for NT<50. It would quite possible to fit lines or curves to this data, although the best fit values would be different between the Birmingham and Silsoe datasets.
It would seem that if this method is to become useful in a predictive, rather more detailed information on near ground probability distributions is required for a variety of ground roughness conditions / heights above the ground etc., so that the variation in the exceedance curves of figures 4 to 7 can be more fully understood and an overall data collation be achieved. If any reader knows of systematic data for wind probability distributions, please let me know.
In 2017 I delivered the UK Wind Engineering Society’s bi-annual Scruton Lecture, which was instituted in 1991 in honour of Kit Scruton of the National Physical Laboratory, who carried out significant work on dynamic structures. My lecture was entitled Wind Engineering for Serviceability and Resilience and described in rather a broad way my wind engineering work in this area over the previous three decades. The slides can be found here. Note that the file is very large and may take some time to download.
A blog from a previous version of this website – written in 2017
At the time this blog was written, Storm Friederike has just passed over Western Europe and has resulted in a number of deaths and considerable traffic chaos. The BBC reported that
Deutsche Bahn had already suspended rail traffic in North Rhine-Westphalia (NRW), neighbouring Rhineland-Palatinate state and Lower Saxony, when it announced a Germany-wide suspension of long-distance trains. Any regional trains still running have cut their speed because of the strong winds.
A spokesman said it was the “right decision” due to the risk of trees falling on overhead wires and on tracks.
The Dutch Railways (NS) and operator ProRail said overhead power lines had been damaged by the wind, as well as some railway tracks. An alert on the NS website said that “at most, only a few trains” would run throughout the evening.
Trains do occasionally blow over. The first recorded incident was on the Leven viaductin south Cumbria in 1904 when a wooden bodied train blew over on the embankment on the approach to a viaduct. A number of other incidents have occurred around the world in recent years, the latest being in Switzerland where a video has been posted online of a train in the process of being blown over – here and here. Clearly as accidents of this type can have potentially very severe consequences they need to be in some was taken into account by train builders and railway operators in design and practice.
The effect of cross winds on trains (and lorries to some extent) is a research topic that has stayed with me throughout my career. My first involvement with the issue was when I worked for BR Research in the early 1980s in looking at the effects of high winds on the Advanced Passenger Train. The issue arose again when the Channel Tunnel was opened as the very light lorry carrying vehicles were found to be at risk of blowing over in ports. Then, with the advent of high speed trains in the 1990s, considerable effort has been devoted to developing a methodology to ensure that cross wind effects are taken into account in both design and operation – in Europe, Japan, Korea, and most recently in China. There is broad agreement on the methodology that is to be used. It consists of three parts.
An assessment of the aerodynamic loads on the train – usually in the form of graphs of aerodynamic forces and moments against wind angle.
The use of this aerodynamic data in some sort of mathematical model of the effects of wind on the vehicle under consideration to determine a graph of wind speed that will cause and accident against vehicle speed – usually referred to as a Cross Wind Characteristic or Characteristic Wind Curve (CWC).
The use of this CWC together with weather, route and operating data to determine the risk that the train will blow over on the route under consideration.
The design of trains usually considers only the first two steps and the CWC that is obtained is compared with reference CWCs in the train certification process. Train operators clearly need to know the output of the third stage, so they can design suitable risk alleviation systems – eg. slowing trains down, providing protection such as wind breaks etc.
Each of the above steps can have varying levels of complexity.
The assessment of aerodynamic loads can involve physical model tests of different types – using standard low turbulence wind tunnel tests, wind tunnel tests with a simulation of atmospheric turbulence or moving model tests. Embankments and bridges may or may not be modeled. Alternatively the loads can be determined by CFD calculations, again of varying levels of complexity, from simple RANS calculations, through to complex (and resource hungry) DES and LES calculations.
The calculation CWCs needs a simulation of the wind – that can either be the specification of a simple gust velocity, the specification of a spatially and temporally varying ideal gust, or the full specification of a wind time history; and also a simulation of the vehicle system – either a simple one, two or three mass model or the specification of the suspension system with varying levels of complexity. More recently some authors have even used calculations that are coupled with the track dynamics and with the dynamics of a bridge that the train passes over.
Finally the determination of the risk requires detailed wind statistical information that is not always easily available, together with route topographical information – embankment heights, bridge geometries etc.
To my mind one of the most important things about this three part process, and one that is not always appreciated, is that each component has a very different level of precision. The aerodynamic forces and moments can probably be specified to within 5%. The calculation methodology for CWCs, given specific values of the forces and moments, is highly accurate (say to within 1%), whilst the calculation of risk has massive inbuilt uncertainties because of the uncertainties in the meteorological information. Thus usually the risk of a wind induced accident can only be specified to within an order of magnitude i.e. 10-8or 10-9. Thus highly accurate determinations of CWCs are really pointless when the uncertainties in the risk calculations are considered.
Having spent the last 40 years involved with this problem to some degree or other, I would thus like to make the following reflections.
The different aspects of the subject – fluid mechanics, vehicle dynamics, meteorology etc. – make for a fascinating intellectual mix, and have led to the development of a range of complex modeling and analytical techniques. For an academic these challenges are fascinating – but these intellectual challenges can sometimes result in the end points of the process (train certification and risk specification) to be forgotten. I am as guilty of this as anyone of course. As an academic I can argue that my work in this area has enabled progress in other research areas, as indeed it has, but the end goal shouldn’t be forgotten.
The current train certification methodology in the CEN code is essentially a comparative one with CWCs for particular trains being compared with CWCs for trains that are considered safe. As such, accurate values of accident wind velocities are not required, as long as they were derived in the same way as for the reference safe vehicle. The CEN code sensibly goes down this route, and specifies a simple type of wind tunnel test to obtain the force coefficients. However it requires a full multi-body dynamic simulation with an artificial gust simulation, with a complexity that seems inconsistent with the accuracy with which the aerodynamic forces and moments can be specified.
The above multi-body simulation technique can, and has in the recent past, result in game playing that has no relevance to train operation or safety – by marginally changing the suspension parameters in an arbitrary way in the dynamic calculations so that the CWC is above the reference value and thus allowing the train to be certified. There must be doubts about any methodology that allows such things to happen.
Taking the above considerations a little further, there is an increasing tendency in published papers in this area to include as many complications as possible – multiple degrees of freedom of train, track and (if appropriate) bridge; coupling of train movement with the aerodynamic coefficients; very high resolution (and resource usage) CFD calculations. In my view the proper way to use such techniques is to carry out studies to determine the effects of such complex methodologies on overall aerodynamic forces and CWCs (almost always second order) and then to develop a much similar methodology that allows for them in an approximate way that is consistent with the accuracy of the overall process. Just because it is possible, using modern numerical techniques, to make complex calculations, it is not always sensible, or a proper use of resources, to do so.
Finally there is the effect of operation that needs to be taken into account, which brings us back to where we began. In the recent storms, the German and Dutch railway authorities simply stopped train movement, because of worries about debris on the track or trees falling onto overhead wires – not because of worries about trains overturning, as the wind speeds were much too low for that. The same happens in the UK. When high winds are forecast Network Rail and the TOCs first impose a blanket regional 50mph speed limit, mainly so that trains have some hope of reducing speed when debris is blown onto the track. A major problem in this regard seems to be trampolines at the moment – see figure 2 below – and at higher predicted gust speeds of around 65mph, train operation is stopped completely. Also, very often, train movements are blocked by tree fall onto the overhead. Operational reality takes precedence over all the wind tunnel tests, CFD calculations and MDS modeling we can conceive of doing.
The study of the effects of high winds on trains is fascinating and alluring academically, and allows the use of a whole range of fun physical model experiments and challenging computational techniques. But a sense of perspective is required I think – to keep the various methodologies simple enough for reasonably routine use in train certification and route risk assessment; and not to forget the overriding importance of train operational considerations.
In this blog post I want to introduce the work that I, together with a number of colleagues, are carrying out on the phenomenon known as crop lodging. First I guess it is actually necessary to define what the word “lodging” means. In simple terms, lodging is the failure of crops due to stem breakage or uprooting during periods of high winds and/ or heavy rainfall. I need to make the point very firmly right at the start that it has got absolutely nothing at all to do with crop circles! It does however have significant economic consequences, with yield losses in winter wheat resulting in costs to growers of the order of £100m in the UK in a high lodging season. Some pictures of lodging are given in figure 1 below.
Figure 2. Lodging in coral crops
Our work on this issue goes back in one form or another over a period of 30 years. It all began in in 1987 when I was an academic at the University of Nottingham. After the Great Storm of that year wreaked havoc with the tree stock in the south of the country, I still remember a colleague (Andrew Dawson) putting his head around the door of my office and saying “I have an idea for a research grant….”. This led to a grant from the Science Research Council to investigate the aerodynamics of urban trees – and we thoroughly enjoyed ourselves making measurements of the mechanical and aerodynamic properties of trees on and around the University campus, evolving an experimental technique that we named tree-twanging – pulling trees with a winch and then releasing them to measure the frequency of the oscillations. One of the less successful parts of that work was the initial development of a mechanical model of trees in high winds, which tried to represent trees in engineering terms. At the time this didn’t progress very far, but a few years later, in the early 1990s, I was approached by a colleague from the University School of Agriculture at the Sutton Bonnington Campus (Prof. Keith Scott) to help with a project that was investigating the lodging of winter wheat, and in particular to help supervise the PhD research of two students – John Griffin and Pete Berry. Perhaps the most challenging part of this work, both for me and staff and students at Sutton Bonnington was the need to learn to speak the vocabulary of anther discipline. This collaboration led to me doing some serious work on analytical model development that produced a reasonably robust description of the mechanical behavior of plants, and in particular winter wheat, in high winds and heavy rainfall.
The next phase of this work began in 1998, when we (myself and colleagues at Sutton Bonnington and ADAS) obtained a grant from the Biology and Biotechology Research Council (BBSRC) to investigate lodging of winter wheat in some detail, to identify those plant characteristics that resulted in an increase in lodging risk. This date also coincided with my move from Nottingham to the University of Birmingham. This work involved an extensive series of field trials at ADAS to measure characteristics of plants relevant to the lodging process, and we at Birmingham were responsible for developing a model of the lodging process and for carrying out experiments to calibrate the model. By this time (Dr.) Pete Berry was working for ADAS, so the collaboration with him thus continued. The Research Fellow appointed at Birmingham for this work was Dr. Mark Sterling, who had recently graduated from there with a PhD in open channel flow. We built the lodging model on the basis of the earlier modeling work, but needed a variety of aerodynamic information to calibrate this. Normally in engineering terms, this would have been obtained through wind tunnel tests – it is however not easy to put a representative section of a wheat field into a wind tunnel. The solution was to take a wind tunnel into the field – see the picture below. This proved to be more than a little challenging, but during the course of the experiments we were able to obtain the very first video footage of lodging actually taking place – this usually occurs in high winds and heavy rain and more often than not in the middle of the night, so the use of a portable wind tunnel, difficult as it was, was actually the most straightforward way of doing this. I am told by Mark that fixing strain gauges to wheat stems in the field to measure the displacement was one of the most entertaining tasks that he has ever been faced with.
Overall the project was very successful and enabled us to learn a great deal about the mechanics of root and stem lodging, to provide solid scientific information that cut through much of the hearsay that was around in the industry at the time about lodging, and to provide robust agronomical advice for farmers for techniques to avoid lodging. The collaboration between the University and ADAS was vital in this regard.
Over the next few years, work continued at a lower level, with the production of a few collaborative review papers, and the application of the lodging model to barley. However by the start of the current decade it was becoming clear that the model as it stood, whilst perfectly acceptable for wheat crops where the plants were essentially isolated throughout the growing season, was not really applicable to a range of crops for which, late in their growing season, individual plants interlocked to produce a much denser canopy. Thus we (myself, Pete Berry and Mark Sterling, by now the Head of Civil Engineering at Birmingham and thus my boss) began work on the development of a generalized lodging model that could allow for plant interlocking. Whilst the modeling was quite complex, it resulted in the relatively simple pictorial representation shown below in figure 2, where regions of stem lodging and root lodging were defined in terms of the daily rainfall rate and the hourly wind speed. The various velocities and rainfalls shown on this figure are all (rather complex) functions of plant and soil parameters and can, once the model is calibrated, be fairly easily specified. In principle this graph can be used with a representation of wind and rainfall probabilities to determine the risk of lodging occurring for any set of plant and soil parameters, and mitigation methods taken if this risk is deemed to be too high. In the peer review process, one of the reviewers of the paper acknowledged the elegance of the model, but made the comment that it would never find a practical outcome. We were to prove him wrong!
Figure 2. Lodging regions in the rainfall / windspeed plane
Over the last few years the work on lodging has grown very significantly, and we now have three projects underway. The first was funded by Teagasc in Ireland, to investigate methods to reduce oat lodging. We used the model described above and the work included a series of experiments in Ireland to measure, the behavior of oats in high winds.. The second project was funded by BBSRC under the SARIC (Sustainable Agriculture Research and Innovation Club) scheme, with myself and Mark, working again with Pete at ADAS. This used the same set of techniques to investigate the lodging of Oil Seed Rape. The unique aspect of this project however was a collaboration with Dr. Alan Blackburn and his colleagues at the University of Lancaster who are experts in Earth Observation and Remote Sensing, and the local modeling of lodging is being embedded in a much wider scheme to integrate spatial, topographic and meteorological data sets to predict the risk of lodging for individual crops and fields, and to identify those soil, plant and weather characteristics that cause lodging. The final project was also funded by BBSRC, but this time through the Global Challenges Research Fund which directs research funds to the problems of developing countries. We used a similar approach to the SARIC project, but this time directed towards maize and rice, working again with ADAS and Lancaster University, and also with colleagues at the Chinese Agricultural University and with CIMYTT in Mexico, who work in a large range of countries in the developing world. The potential significance of this project is huge – lodging causes yield losses of up to 40% in rice and maize, reduces grain quality, increases time to harvest, increases grain drying costs and increases health damaging micro-organisms on grain. It is estimated that lodging in rice and maize reduces crop production in China and Mexico alone by $1500 million per year.
All this research has all developed from a chance conversation and some early blue-sky research on trees over 30 years ago – and now has the possibility of producing results that will have a major effect on crop productivity around the world. In these days when funding for such fundamental research is under increasing pressure, this is perhaps worth remembering. But for now, these are exciting times – watch this space for future updates.