By Laurence Gale MSc
In my role as editor and consultant for Pitchcare I often come across many horror stories about poorly constructed and maintained pitches, which in the main arise from the fact that inappropriate surveys have been carried out to ascertain the existing soil physical properties and structure make up of the site, coupled with the fact that to save money there is often a compromise on imported materials. Particularly on lower budget projects where quite often sub standard local imported subsoil and topsoil materials are used.
The net result of not undertaking a proper survey of your soils or accurately knowing the composition of your imported materials, will no doubt end in disaster, ending with poorly draining, often uneven pitches that seldom encourage or promote any decent grass growth, and in many instances are contaminated with foreign materials brick and glass, ceramic deposits. Which often lay on or near the playing surface thus becoming hazard for players.
As part of my MSc course in Sports Surface Technology at Cranfield University I was able to carry out a review into soil mapping technologies, comparing tried and tested methods with Electromagnetic Induction techniques. Below is a summary of my Thesis work and findings with regard to soil mapping.
Accurate soil mapping is a very important aspect of land management especially in the sports turf industry, were knowledge of soil properties is essential for managing sport facilities. Understanding the classification of soils and soil water relationships will help ensure that the correct management practices are implemented to maintain the playability of sport pitches.
The principle methods for obtaining information about soil properties is by taking physical insitu soil samples from site and analysing them in laboratories which is often a time consuming and costly activity. Recent advances in soil mapping technologies has seen the introduction of Electromagnetic Induction (EMI) were soils electrical conductivity is measured (ECa) recorded and mapped using Global Positioning Systems (GPS) and Graphical Information System (GIS) software programme producing soil maps delineating soil texture classifications within soil profiles.
The aim of this research project was to show if there is a correlation between the two different soil mapping methods. The objectives of the project were to measure and analyse particle size distribution (PSD) soil bulk density and moisture content, soil pH and Organic matter (OM) on a natural turf football pitch in Bedfordshire, United Kingdom using traditional soil sampling methods and EMI soil mapping technology.
Knowledge and understanding of soil physical properties has always been important for professional turf managers when making decisions about maintenance operations and when carrying out reconstruction works. Precise soil knowledge, including information on boundaries between soil types, should result in more accurate use of fertilisers, pesticides and effective management practices, thus ensuring that the final managed outputs result in the production of economically safe, consistent, playable natural sports turf surfaces.
Traditional soil analysis involves taking soil samples and sending them away to specialist laboratories for analysis, often a time consuming and costly process and often not totally representative of the soil horizon of the playing facility. Recent advances in soil mapping techniques driven from precision farming technologies has seen the introduction of a number of rapid scanning sensors that can measure soil apparent electrical conductivity (ECa) by electromagnetic induction (EMI).
This project investigated whether EMI soil mapping techniques to measure apparent electrical conductivity (ECa) in soils can be accurately correlated to soil data from traditional soil mapping methods. The experiment focused on a single natural turf grass football pitch at Cranfield University, Silsoe, Bedfordshire, England
The experiment involved surveying, measuring and analysing the existing soil properties and characteristics of the football pitch, measuring the bulk density, organic matter (OM), soil texture classification, pH and soil strength and recording the locations of where data was collected. The football pitch was then scanned using an EMI sensor device that was driven over the pitch recording and plotting information using Global Positioning Systems (GPS) and Graphical Information Systems (GIS) technology.
Soils vary in many ways, both physically and chemically on a local or regional scale. Many factors, including original parent material source, climate, weathering processes, topography or history of the land use influence their properties (Adams & Gibbs, 2000). This soil variability gives rise to all the different soil types universally classified by soil texture composition. Soil composition (soil texture) is determined predominately by mineral particles and organic matter content and can be classified by the percentage of sand, silt and clay mineral particle content. The National Soil Research Institute (NSRI) in the United Kingdom classifies soils using mineral percentage contents to produce a soil classification triangle (Figure 1.1).
Figure 1.1 NSRI Texture Triangle
The size and shape of these mineral particles will have a major effect on the soil structure and will characterise how well water can infiltrate and be held available for plants by the soil profile. The distribution of the primary particles of sand, silt and clay and their association into aggregates determine the amount, size and distribution of pores in soil; these can be filled with air and water of varying amounts. These pores are very important for sustaining plant life and soil structure (Brady and Weil, 2000); they enable the availability and access of soil, air and water for plants.
However, obtaining and maintaining the optimum soil conditions is not always an easy task. Many physical factors affect this balance; soil type, OM content, climatic conditions, player usage and choice of maintenance operations. The answers often lie in the ability and knowledge of the professional turf manager to utilise soil analysis data to understand these soils / plant water relationships. The concept of utilising EMI sensors to map and measure soil properties may offer turf managers the opportunity to maximise their scarce resources more efficiently, especially in the public service sector environment.
If EMI sensor technology can be proved to be an accurate and reliable tool for producing soil maps, then the potential for using this EMI technology could be immense. There has been a number of research programmes carried out on the use of EMI especially in precision farming. Many scientists have claimed that this technology has the potential to measure various soil properties; determining soil electrical conductivity at depth (Rhoades, 1981), applications of soil electrical conductivity mapping, (Lund, 1999). Evaluation of non-intrusive sensors (King 2003) and sensing claypan soils using EMI (Sudduth, 1998).
All the above conveyed the efficiency of data collection compared with traditional soil analysis methods and also stated that the results of the scanning can be used to pinpoint areas of interest enabling traditional soil testing to be targeted more accurately. If there was a concern it was with regard to the interpretation of the data collected not always producing a straightforward picture of soil properties. Very little research has been carried out in using the EMI technology in measuring soil properties on sport fields, a different proposition compared with farming land where the soil is often been cultivated. Continued research may reveal the potential of EMI sensors for measuring and mapping soil of sport fields in the UK.
Traditional soil mapping
Traditional soil mapping involves a number of tasks combining visual inspections with ground surveying techniques, using appropriate surveying levelling equipment to map topography. This data is backed up with soil data collected from the site in the form of soil samples taken at various depths, usually a number of soil samples are taken from the playing pitch and sent to a laboratory for testing. The laboratory can perform a number of tests on the soil samples to find out about, soil pH, OM content, PSD and nutrient status.
This enables accurate identification of soil textural classification and soil condition, enabling managers to choose the correct practices to maximise the yield potential of their soils. Soil testing is more widespread in the agricultural industry compared with the sports ground industry, where only sports facility managers and very few LA's landscape managers carry out soil surveys. This is chiefly due to cost and time constraints.
While the results from soil sampling provide accurate information, it may not be representative of soils adjacent to the sampling point. Soil characteristics can change within metres, an important factor when managing playability on sport fields, as inconsistent playing surfaces can be detrimental to the sport being played. These soil surveys routinely depend on subjective above ground observations and intensive, intrusive soil sampling methods that may significantly alter the physical properties of both site and sample. For example multiple soil borings can distort the soil structure of the extracted sample, altering its bulk density and increasing surface damage. Extensive sampling results in high overhead costs for labour and time
The industry's requirement is for an accurate, fast, non- invasive and inexpensive method of producing soil maps at a level of resolution that is comparable with current and future technology. In recent years there have been a number of technical developments culminating in the production of Ground Penetrating Radar (GPR), Time Domain Reflectometer (TDR) and Electromagnetic Induction (EMI).
Ground Penetrating Radar (GPR)
GPR is a transmitter that generates and emits pulses (waves). If the path of the pulse (a target) is blocked, it will deflect, reflect or absorb the wave. If the wave is reflected, the pulse is picked up by the receiver and processed. GPR has been used to detect water. Vellidis et al.(1990) were able to identify wetting front movement in a sandy soil. In the right circumstances, the technology can be used to map soil features that influence water flow and soil water content. However, a drawback of GPR is that the method is data and labour intensive for comprehensive surveys of large areas (Yoder, 2000). GPR has difficulties when operating on clay soils readings tend to be difficult to obtain as the signals were reflected back before they had time to penetrate the soil profile and also hire costs are high, (King 2003).
Time Domain Reflectometer (TDR)
Time-domain reflectometer (TDR) determinations involve measuring the propagation of electromagnetic (EM) waves or signals. Propagation constants for EM waves in soil, such as velocity and attenuation, depend on soil properties, especially water content and electrical conductivity, (Nissen 2001). The dielectric constant, measured by TDR, provides a good measurement of the soil water content. This water content determination is essentially independent of soil texture, temperature, and salt content. This instrument can give very accurate data but its mode of use is very time consuming and costly, it also requires a direct contact with the soil surface.
EMI sensors for soil mapping
Electromagnetic induction technology was originally developed for the mining industry, and has been used in mineral, oil, and gas exploration, groundwater studies, and archaeology. Geophysical survey methods measuring the difference in electrical conductivity (ECa) in soils have been used readily since the 1940s studies by Archie (1942) and Rhoades, et al., (1976) both related Electrical Conductivity (ECa) with soil properties. There have been significant developments in the use of EMI sensors, the utilisation of GPS and advanced computer data logging systems (Lund, 1999) have enabled these devices to produce fast accurate data. That not only gives soil measurements but also defines topography and grid positioning in relation to national survey maps. This information can then be collected using GIS, providing soil maps which can be used to evaluate the site and make appropriate management decisions in relation to maintenance or future management of the land.
The advantage of the non-invasive EMI technology as used in the farming industry is that it enables quick and complete ECa soil mapping of the site soil properties. EMI scanners can be driven across areas of land at speeds of between 10-15 km/h recording and positioning soil measurements using GPS surveying technology. Developments in agriculture have seen the EMI sensor technology measuring and detecting spatial variability of salt and clay contents of soils (Williams and Hoey, 1987), soil water content (Kachanoski, 1988), soil salinity (Corwin, 1990; Rhoades, 1990), water holding capacity and drainage (Jaynes, 1996) and OM content (Jaynes et al., 1994).
There seems to be enough evidence to justify the use of employing EMI technology to map soil properties based on the reports within the precision farming industry. Doolittle et al (1994) and Sudduth et al (1995) showed that depth of topsoil over a claypan could be successfully estimated from single-height EM sensor readings and herbicide degradation and crop productivity was measured by Jaynes (1994). More recent studies by King, (2003) has been comparing EMI performance with GPR and Spectral Reflectance (SR) in producing accurate soil map data.
Advantages of EMI technology:
- can determine water content to a measured depth
- sensor configuration can vary in size so sphere of influence or measurement is adjustable
- relatively high level of precision when ionic concentration of the soil does not change
- speed of operation
- complete areas surveyed
- results are instantaneous and can be read by remote methods
- non invasive
- low operating costs in comparison to sport industry soil analysis costs
- calibration not stable with time and affected by ionic concentration
- cost of equipment to generate signal and readout system is high but could decrease with new solid-state technology
- results can be affected by external interferences, metal objects, tree cover, overgrowth effecting signals, GPS malfunction.
- interpretation of results
- calibrating soil samples and analysis is still necessary
EMI sensing is now becoming more widespread, especially with the development and reduced costs of GPS and GIS technology enabling a wider market for its use, the potential to use this system for mapping sports facilities could be immense.
Methods of Electromagnetic Induction (EMI)
The two primary EMI methods for measuring soil conductivity are by contact and non invasive methods (Figure 1.2 contact method). The Veris® 3100 (Veris Technologies, Salina Kansas America) measures ECa with a system of soil contacting coulters and (Figure 1.3 non invasive method) The non-invasive EM38 scanner (Geonics Limited, Mississauga, Ontario,Canada). See (Appendix .3) for description and mode of operation for both the Veris® 3100 and EM38 EMI scanners.
Figure 1.2 Veris® 3100 (Veris Tech)
Figure 1.3 EM38 (Geonics Ltd)
These two devices are able to measure the ECa of soils by the transmitting and receiving of electromagnetic current as the scanner moves over or through the soil. The conductivity is a measure of the ability of a material to conduct an electrical current. The usefulness of soil conductivity stems from the fact that sands have a low conductivity, silts have a medium conductivity and clays have a high conductivity (Figure 1.4). The ability of the EMI sensors to measure the conductivity of soil materials and the amount of moisture content within and around the soil particles allows accurate soil mapping measurement to be made.
Figure 1.4 Soil electrical conductivity depends on soil grain size and texture
(Veris technologies 2003).
The variables shown in the above figure are key components of soil structure and texture and have a significant effect on soil water relationships, nutrient capacity and soil stability; all-important factors when managing and maintaining natural turf sport surfaces.
The EM38 sensor with its non invasive method of collecting soil ECa data will be more suitable for measuring soil properties on sports facilities; having the ability not to damage the playing surface and being able to measure at speed. EMI surveys are currently being offered commercially by several companies for use in agriculture. The costs are negotiable, but around £20 per hectare can be considered as current guide price (King 2003). Cost for EMI surveys on sport fields are difficult to standardise. Only one company Turftrax currently offers this service to the sport surface industry.
Materials and Methods
A number of tasks were carried out to map and identify soil physical properties at Cranfield Universities football pitch. Firstly, a complete site survey was undertaken to plot and reference known landmarks, levels and orientation of the pitch. This was achieved using Global Positioning System (GPS) linked to Leica total station surveying equipment. All line markings and pitch features including thirty-six data sampling zones (measuring 20 x 8.33m) were recorded and downloaded into a Graphical Information System (ESRI ArcGis) to produce digitised maps of the site. After completion of mapping and surveying, soil sampling was carried out taking a representative soil sample from the 36 zones. These soil samples were collected and taken to the soil laboratory for analysis to obtain, soil pH, OM content, moisture content and PSD results. Also in situ soil tests using density rings were carried out on the football pitch to measure bulk density obtaining a sample from all zones. Soil strength was also measured using a Findlay Irving Penetrometer. Additional deeper soil core samples were also taken from the zones at 250mm and 500mm depths to calculate gravimetric water content of the soil profile along with some very deep cores taken in isolated spots within the pitch area to monitor changes of soil texture. Other data collected referred to visual inspections, recording vegetative cover and surface conditions, investigation into usage and current maintenance programmes and recording weather data for the site.
Site Selection and Location
The site was chosen for a number of reasons, its location at Cranfield University enabled direct access to soil laboratories and support services and accurate weather data was available from the University's Cassella Weather Station. The football pitch mirrored the physical and performance characteristics of a typical LA low standard playing pitch on a heavy clay soil with no primary or secondary drainage
The study site was a single 100 x 60m natural grass football pitch, located at Cranfield University (Silsoe),Bedfordshire, UK). (Grid reference TL080352). The pitch is orientated in the direction north-east, south-west. The topography of the site is open and level with minimum shade cast onto the pitch. (Figure 2.1).
Pitch History Maintenance and Usage
The football pitch was first constructed and laid down in the 1960s. The pitch was cut, filled and levelled with no drainage systems installed. It was seeded with rye grass and has been maintained for football use ever since. The maintenance regime for this pitch is very basic with only grass mowing and marking out of pitch lines being carried out, with occasional programmes of aeration using solid tines, carried out when conditions allow access. According to reports from ground staff, no fertilisers or pesticides have been applied during the last ten years.
Additional maintenance works carried out involved ground staff applying localised sand dressings in goal mouths and centre circle areas during wet spells to improve surface drainage and levels. In addition, renovation and seeding over of bare areas is carried out at the end of the season. This level of maintenance puts the Silsoe playing pitch into very similar categories of low standard LA football pitches. This facility is currently providing a recreational facility for the students and local community teams
Site location, aspect and the physical characteristics of the playing pitch backed up by the technical and scientific facilities on location have played a significant part in choosing this site, especially with regard to its comparison with LA football pitches.
Soil Survey and Sampling Strategy
Bulked soil samples were taken from the pitch zones, extracting 25 cores from a W pattern within each zone. This provided approximately 400g of soil for each of the 36 zones set out within the pitch area. The samples were labelled and taken to the soil laboratory for analysis. The sampling strategy involved accurate surveying and mapping of the site using GPS and GIS technology producing an accurate zone template of the pitch area, (Figure 2.2). The zones measured 8.33m x 20m in size, providing 36 sampling zones for soil analysis enabling effective correlation of soil physical property results with EMI scanning data. Testing for soil pH, OM content and PSD form the basic tests to establish soil type and condition. This was backed up with further tests to establish soil bulk density and penetrometer readings to identify areas of compaction. These results, when statistically analysed, may produce data that can be correlated to ECa data.
This method was chosen to ensure that spatial soil data taken from the pitch could be traced to a particular zone area. The diamond shape seen in figure 2.2 depicts the traditional wear area generally experienced on most football pitches where players have concentrated play. This diamond pattern may help explain some expected results. The expected results based on data collected from these zones may indicate areas of compaction and higher bulk densities and less grass cover expected in zones B01-B12 compared to A and C zones. Therefore the opportunity to take soil data from all zones will enable a more accurate analysis.
Figure 2.2 Playing pitch zone lay out depicting 36 zones and diamond wear pattern.
Sample Handling and Storage
All Samples were obtained, handled and labelled carefully throughout the collection and analysis processes. The final data results were recorded and stored in a database.
Visual inspection of the playing surface vegetation was carried out utilising a 1 x 1m square quadrant to identify grass sward, weed species and bare soil percentages in random selected zones, (In accordance with BS 7370: P3.A3 BSI 1991). This method also meets guidelines set by performance quality standards (PQS) published by the Institute of Groundmanship (IOG).
The Football pitch was divided into 36 zones (figure 2.2). Each zone measuring 8.33m x 20m in size. 25 soil core samples were taken from each soil zone to a depth of 150mm using a 15mm diameter soil corer complying to (BS 7370: P3 A2 (BSI 1991). The total air-dried weight of zone samples averaged 400g providing enough soil for laboratory analysis for particle size distribution (PSD), pH and organic matter content. A further 42 soil samples were taken in situ for bulk density measurements using density rings.
Soil Texture and Particle Size Distribution
A total of 48 soil samples were analysed using particle size analysis by pipette method in accordance with British Standards Institute (Sedimentation by pipette method, BS 1377: part 2: 1990). Analysis was carried out in the soil laboratory at Cranfield University, Silsoe UK.). The samples collected represented a soil from each of the 36 zones of the pitch with a further 12 samples tested to measure for error of sampling and measurement.
Method 32 MAFF RB427 (1986) was used to obtain pH results. Measurements were taken using a pH meter calibrated between pH 4 and pH 7. A 10 ± 0.1g air-dried sample of soil (sieved to pass a 2mm screen) was placed in a glass beaker with 25ml of distilled water added and stirred (dilution1:2:5). The pH electrode was inserted into the soil solution and the pH was recorded.
Organic Matter Content
Two main methods are available for measuring OM, firstly Loss- on- Ignition and secondly the Walkley-Black method. The loss on ignition (LOI) method is advantageous because it is inexpensive, easy to perform with a minimal amount of equipment, and uses no chemicals. The Walkley Black method is more expensive and involves using hazardous chemicals. LOI was the method used in this study.
Bulk density was measured using in situ soil samples, density rings were used to obtain 42 samples taken from zones in the football pitch on the 12th May 2003. The method involved taking out the top 90mm (turf depth) using a golf hole cutter, enabling the density ring to make direct contact into the soil profile. A density ring was hammered into the soil at the base of the excavated hole to collect the sample. This was then removed and any access soil was cut away leaving a known soil sample of known volume in the density ring. These samples were then weighed, oven dried, re-weighed and calculations made to determine dry bulk density, (Rowell, 1994).
Volumetric moisture content was collated from soil samples taken at 100mm depth at which the dry bulk density rings were filled, weighed and oven dried. The gravimetric moisture content was measured from core samples taken at 250mm and 500mm depths. Moisture content was calculated from the weighing of the wet soil in its tin, oven drying at 105 °C for 48 hours, re-weighing the dry soil sample and tin and subtracting dry weight from wet weight to give water weight in grams. The weight of the tin was then deducted from the dry sample and tin to give weight of dry soil sample. The weight of water was then divided by weight of dry soil and multiplied by 100 to give moisture content percentage.
Soil strength / compaction was measured using a Findlay Irving soil Penetrometer. This device can measure the soils resistance to vertical penetration (soil strength as measured in kPa by the Penetrometer) down to a 500mm depth in 333mm increments producing 15 depth readings. The penetrometer was pushed into the ground five times per zone; the readings were recorded and downloaded into a Windows Microsoft Excel spreadsheet for evaluation. A mean average was taken of the five readings taken per zone. These results were then transferred into GIS Arcmap software programme to produce soil map data for analysis.
Electromagnetic Induction EMI
The football pitch was scanned using a Geonics duel coil EM38 electromagnetic induction sensor. Figure 2.3 shows the setting up of the towing vehicle a (John Deere 4 wheel Gator towing vehicle) and the cart that holds the scanner in place, this cart is made from non-metallic materials (to prevent interference with the instrument). It is towed at a minimum 3m distance, so that the EMI scanner measurements are not influenced by the Gators engine noise and metallic construction. Figure 2.3 setting up the EM38 sensor.
Setting up the EM38 involved a series of operations. The battery power was checked; low power can affect conductivity readings. A fifteen-minute warm up period was allowed to prevent sensor drift. Prior to calibration, operators need to remove any metallic objects, rings, coins, watches and jewellery, even metal toe capped boots, as these items will interfere with the instrument. It is also important to check that the GPS has located enough satellites to get an accurate fix for data logging usually 4-5 is enough.
Calibration of EM38 Sensor
Calibrating the EM38 (Figure 2.4) involves the operator taking the EM38 and placing it down on the ground at a known point (line marking on pitch) and calibrating the instrument to get an established reading at that point which may be referred to in the future. If the sensor requires additional re-calibration the EM38 must be calibrated back at the original calibration spot. Calibration was carried out to manufacturer's instructions in both vertical and horizontal dipole positions.
Figure 2.4 Calibration of EM38 set at known reference point (pitch lines)
Figure 2.5 DGPS base station set in centre of playing pitch (Silsoe)
Operating EM38 Sensor
It is important to correctly set up the Differential GPS (DGPS) and radio transmitters and receivers both at the ground base station (Figure 2.5) and fitted on board the Gator and cart. (Figure 2.7). These are responsible for pin pointing the exact locations of EMI readings in relation to longitude and latitude reference points located by the GPS satellites. DGPS cancels out man made errors that normally creep into normal GPS measurements. This is achieved by transmitting corrections through the radio link from the base station to the Gator, hence the setting up of both systems.
Figure 2.6 Setting up on board data logging computer
Figure 2.7 EMI scanning of the football pitch Cranfield University Silsoe (7 5 03)
(Figure 2.7) show the EMI scanner being driven up and down the pitch at speeds of 10-15 km/h turning at each end of the pitch. The data logging equipment (Figure 2.6) was logging data from the GPS and EM38 at five points per second, capturing well over 6,000 lines of information in the 20 minutes it took to scan the pitch.
Other points to check when using the EM38 sensor is air temperature, Temperatures above 25ºC can effect the calibration of the EM38 Geonics scanner. When working in tempretures above 25ºC the EM sensor should be periodically checked every hour to ensure it has remained calibrated to the site setting. Vibration and extreme cold may also affect the scanner and sometimes human beings own static electricity field may influence readings. Local knowledge about the site being tested is also useful, anomalies such as water courses and ditches may have an affect or be attributed to the nature of the readings recorded. Buried material or recent chemical and pesticide applications can have an affect on the EMI readings. Jaynes et al. (1994) were able to estimate herbicide partition coefficient Kd for atrazine using EMI measurements.
Data Processing of the EMI Data
All EMI results collected were downloaded instantly into the onboard data logger during the scanning process, this information was then transferred to computer software to configure it into picture format images of the site depicting, Vertical and Horizontal dipole results, including a topography plan of the site.
Weather data for air temperature, relative humidity, sunlight hours, wind, Evapotranspiration (ET) and rainfall was obtained from the Cassella Weather station at Cranfield University at Silsoe. It is important to note the weather conditions when taking samples. Results may be influenced by weather conditions.
Figure 3.24 Horizontal ECa,(mS/m) 20 classification contour soil map
Figure 3.25 Vertical ECa,(mS/m) 20 classification contour soil map
Horizontal and vertical ECa (mS/m) results have been plotted to form two soil conductivity maps of the pitch Figure 3.22 and 3.23 The data is represented in a clustered form showing six cluster of data to produce the images seen. Figures 3.24 and 3.25 detail data interpolated into 20 classifications producing a contour image map of ECa data.
A combination of soil mapping technologies using both traditional soil sampling and EMI sensor mapping of the football pitch at Cranfield have revealed a detailed amount of information that can be interpreted to portray a picture of the present condition and structure of the pitch. Surface levels and fall of the pitch (1/125 m) are acceptable levels for local football matches, with pitch levels sloping down across the line of play towards the open ditch, which aides surface water drainage. Vegetation cover totals 92.7 % and is maintained at an optimal cutting height of 35mm during the season. The sward is mowed weekly to produce an even level surface for play, providing teams with a facility to use during the playing season. However the pitch becomes unplayable during periods of wet weather, generally during the winter period December to March, with deterioration in surface conditions resulting in wear and loss of grass cover. This is predominately due to the soil texture of the pitch a clay soil, that does not drain freely and in addition has no primary or secondary drainage systems to remove surface water. The pitches poor drainage capacity is also compounded by areas of compaction, found mainly down the centre of the pitch and particularly in the goal mouth areas, increasing the problems of water movement through the soil profile.
The following results and findings provide evidence to substantiate the problems outlined above. Mean PSD analysis of the soil found (clay 42%, Silt 27%, sand 31%) identified the soil to be a clay soil. Mean dry bulk density (MDBD) and penetration results show areas of compaction with the highest bulk density results of 1.01-1.49 g/cm 3 consistently down the centre of the pitch (B01-B12). The analysis of variance (ANOVA) results for MDBD show that zone B is significantly different (f pr 0.001) to zones A and C this is supported by the penetrometer results shown in Figures 3.16, 3.17, and 3.18, which indicate a consolidated layer of soil at 0.1-0.2 m depth in the main wear areas of the pitch; generally the goal mouth and centre circle in the NE half of the pitch.
This compacted layer was also obvious when taking the soil samples, when greater resistance was experienced compared to samples taken from the wing areas outside the wear area. Another indication of compaction is seen by the presence of Plantain weeds (Plantago major) found in the wear areas; (B01-B12) they are able to colonise in compacted soils especially when soil pH is above 5.5. (Adams and Gibbs 1994). The slope on the pitch may be contributing to this wear, in that all play and training tends to be concentrated on the lowest area (north east goal mouth area) and that this area is near to the changing rooms, and the shortest access route to the pitch. However when mean penetrometer results for three depths (0-167mm, 168-335mm and 336-500mm) were plotted (Figures 3.19-3.21) they did not show the expected wear diamond pattern for compaction on the pitch.
Sand content was found to influence the soil classification of zones B01-B04 and C3 (Figure 3.2) influencing a clay and a sandy clay loam respectively. This is due to the fact that the ground staff top dress these high wear areas with sand. The ANOVA results show that zones B01-B04 are significantly different (F pr 0.001) to the rest of the pitch (Appendix 8). These heavy sand dressings will also have an affect on other physical and chemical factors within the soil profile, OM content will be reduced by the amelioration of the sand dressings, which can be seen in Figure 3.12 where the percentage of OM is low (between 7-9%) in B01-B03, and is confirmed by the ANOVA results data (F pr 0.037) for OM (Appendix 10). Wear from play and training has reduced vegetation cover, contributing to low OM values in these areas. The sand dressings may also be the reason for the higher pH results (6.31-6.40) found in zones B01-B03 respectively (Figure 3.10) especially if calcium based sand is used. This was also confirmed by ANOVA results (Appendix 9) showing a significant difference (F pr 9.158).
Moisture content results measured at three depths show a consistent moisture content through the soil profile (Table 3.13) for all zones 23. % to 30.% water content.
EMI data was measured at two depths, horizontal mode (EMh) at 0.75m depth and vertical mode (EMv) at 1.2m depth. The information was plotted into GIS Arcmap to produce images of ECa data portrayed in two different classifications, Figures 3.22 and 3.23 were clustered into 6 classifications and Figures 3.24 and 3.25.classified into 20 block kriging to produce a contour map of the data. The two sets of data, EMI and soil sampling results, were analysed in various formats, with the aim of finding a correlation between the two. A number of scatter graphs were produced plotting traditional soil data information against EMI data to obtain a favourable linear regression value (R2 = 0.85 or above) for the data. Correlation between clay and ECa results were expected as Rhoades et al, (1989), Suddeth (1995) and Williams and Hoey (1987) obtained significant relationships in linear regressions between ECa and clay content. Correlation was also expected with moisture content as shown by Kachanoski (1988).
However, none of the graphs plotted (Figures 3.28-3.45) comparing EMI ECa results for both EMh and EMv modes against clay, silt, sand, moisture content and soil strength obtained any R2 results that met the critical value R2 (R2 = 0.85), denoting no correlation between properties measured. Frogbrook (2003) also found no correlation between soil properties and ECa particularly sand and bulk density and even though there were positive relations for clay and water content the strength of the relation ship appears complex. This was not expected but may be explained by a number of factors:
Timing of sampling
Soil samples were collected in late April 2003 (28th-30th) for PSD, pH and OM analysis. The EMI scan was carried out on the 7th May, with further soil sampling, (pentrometer and moisture content and bulk density) being measured on 12th -16th May. It may well be that during this period the soil moisture contents may have changed and influenced the results. Hartsock (2000) states that electrical conductivity in soil is controlled by ionic concentration, clay and soil water. These relationships can vary depending on soil series and timing of data collection.
Depth of samples collected
Soil samples were collected at topsoil depths between 100-200mm, and moisture content was measured at 250mm and 500mm depths. Soil properties or objects at greater depths may have influenced the EM38 sensor.
Size of site
The pitch area is relatively small (6000m2) and has a consistent soil classification of clay which may have influenced the ECa readings by averaging them out by depth. Thus not detecting changes in soil texture but other properties, such as water content, this might explain the images seen on Figure 3.46 where in area (A) lower ECa was measured which may indicate that water may be draining into the ditch. The reason why we did not find a correlation with moisture content may be due to the fact that the EMI picked up moisture content at different depths to the ones sampled.
Figure 3.46 EMI soil map measuring Horizontal ECa (mS/m) at 0-0.75m depth
Areas (B,C and D) may be indicating compaction found in goal mouth and centre circle areas as the EM38 sensor picked up higher readings of ECa (mS/m) where clay soil might have become consolidated trapping and holding water within the soil profile. These three areas correspond with the wear pattern usually experienced on football fields (Adams and Gibbs 1994). Area (E) shows another area of compaction that may have been caused by foot traffic to and from the changing rooms.
The Vertical ECa soil map which measured ECa from 0-1.2m depth (Figure 3.47) portrays similar features to the previous soil map but more significantly has shown a distinct area of low ECa in one area of the pitch. Local knowledge and soil survey maps indicate that it may be an area of sand. To confirm this further soil samples were taken in the area and found that at 2.2m depth the clay soil begins to become gritty and sandy, revealing that the EM38 may have recorded data at greater depths.
Figure 3.47 EMI soil map measuring Horizontal ECa (mS/m) at 0-1.2m depth
Even though no correlation was found between the data, the EMI sensor mapped a number of characteristics that may be interpreted as areas of compaction, water content and change in soil texture. Soil sampling and penetration testing also identified compaction in the same areas. It may be that further studies need to be continued to establish a correlation between these two methods of mapping soil properties. However a number of studies (Frogbrook, 2003, and King, 2003) conclude that that the use of EMI EC mapping is not a substitute for soil sampling but can be used to delineate management zones were soil sampling can be targeted. With this concept in mind the attraction of combining these two methods of soil mapping may enable sports turf managers to maximise the advantages of both methods Table 3.16.
Table 3.16 Advantages and disadvantages of EMI and traditional soil sampling methods.
· Accurate information
· Definite values produced
· Measures soil properties that EMI technology can not measure , Soil pH, OM, PSD and soil strength.
· Visual inspection of a trial pit can reveal a lot of information about a site
· Time consuming
· Destructive analysis methods may affect surface playability.
· Soil samples may not be representative of entire site.
EMI Sensor mapping
· Fast mode of operation
· Non invasive
· Cost effective
· Instant information
· Data can be difficult to interpret
· Data can be influenced by unknown objects
· Does not measure soil pH, OM Bulk density or soil strength.
· Needs to be operated by specialist
Recommendation for future work in this area would be to extend the size of the site to encompass four football pitches with additional soil sampling methods Analysing the four pitches may provide enough variance in the data to find a correlation between the two methods. Samples should be measured at three depths (150mm, 500mm and 1000mm), measuring for particle size distribution (PSD) soil bulk density, moisture content (MC),( volumetric and gravimetric MC) organic matter (OM), soil pH, and soil strength. Ensuring that the site is EMI scanned prior to and after sampling at predetermined heights. The opportunity to carry out further research will help validate the potential of EMI sensor technology for use in the sport surface industry.
I learned a substantial amount of information about the pitch from carrying out these surveys, both methods of measuring soil physical properties have enabled me to accumulate enough information to be able to put together a strategy for its future maintenance and capitol investment programme to improve the performance of this pitch facility.
Without information you can not make a judgment on what your needs may be or what problems you need to resolve when managing sports turf facilities. If there is one thing I have learned from this experiment is that by generating and collecting data is an essential process of sports turf management.
I hope the information shown in this report will encourage groundsmen and greenkeepers to further develop their skills and knowledge in assessing and collecting essential data about their pitch facilities. Understanding the science and relationships of soil / plants and water are core principles in the management of sports turf management.
See Link MSc courses for further information about Sports Surface technology courses at Cranfield University
Adams W.A and Gibbs R.J(2000) Natural Turf for Sport and Amenity: Science and Practice. CAB International
Archie, G E (1942). The electrical resisitivity log as an aid in detremininig some resivoir characteristics. Trans. Am. Inst Min Metall. Pet.Eng. 146:54-62.
Baret F., Jacquemoud S. & Hanocq J.F. 1993. The soil line concept in remote sensing. Remote Sensing
Review, 7; 65-82.
Brady, N.C. and Weil, R.R. (2002) Nature and Property of Soils (13 th Edition). New Jersey: Prentice Hall.
Corwin, D. L.,and Rhoades, J.D. (1990). Establishing soil electrical conductivity-depth relations from above ground electromagnetic measurements. Communications in soil science plant anal.21 (11&12):861-901.
Davis, J. Glenn, Newell R. Kitchen, Kenneth A Sudduth, and Scott Drummond
1997 Using Electromagnetic Induction to Characterize Soils. Better Crops and Plant Foods No. 4, Published by the Potash and Phosphate Institute.
Doolittle,J. A., Suddeth, K. A., Kitchen, N. R., Indorante, S. J.(1994) Estimating depths of clay pans using electromagnetic induction methods. Journal of soil water Conservation.46.(6):572-575.
ETL. (2003) Services and Pricing [www document]. <http://www.etl-ltd.com/ETL-MainPage.htm> (accessed 10th July 2003).
Football Foundation (2002) [www document]
<http://www.footballfoundation.org.uk/flashsite/index.asp>(accessed May 20th 2003).
Hartstock, N. J. ,Mueller, T. G., Thomas, G. W., Barnhisel, R. I., Wells, K. L. and Shearer, S.A.,(2000). Soil Electrical Variability. In. P.C. Robert et al.(ed.) Proceedings 5th International Conference on Precision Agriculture. ASA. Misc. Publ.,ASA /CSSA/SSA madison,WI
Jaynes, D.B. (1996) Improved Soil Mapping Using Electromagnetic Induction Surveys.
Proceedings of the 3rd International Conference on Precision Agriculture,
Jaynes, D.B., Novak, J.M., Moorman, T.B., Cambardella,C.A. (1994) Estimating
Herbicide Partition Coefficients from Electromagnetic Induction Measurements.
Journal of Environmental Quality, 24, 36-41.
Jaynes,.Dan.B.,(1996). Improved soil mapping using electromagnetic induction. Site specific management for Agricultural Systems, proceedings of 2nd international conference, Minneapolis, Minnesota, ASA/CSSA/SSSA.
Jowell T (2002) Strategy Unit [www document].
<http://www.strategy.gov.uk/2002/sport/report/fw2.htm> (accessed May 19th.2003).
Kananoski, R .G., Gregorich, E. G. Van-Wesenbeeck, I.J.(1988) Estimating spatial variations of soil water content using noncontact electromagnetic inductive methods. Canadian Journal of Soil Science 68:715-722.
King, J.A, Dampney, P.M.R., Lark, M., Mayr, T. R., and Bradley, R.I (2001) Sensing soil spatial variability by electro-magnetic induction (EMI): Its potential in precision farming. In: Proceedings of Third European Conference on Precision Agriculture, Volume 1,(editors G Grenier and S Blackmore), Agro, Montpelleir,419-424.
King, J.A, Dampney, P.M.R., Lark, M., Wheeler,H.C., Mayr, T. R., Bradley, R.I. and Russil,N (2003) Evaluation of Non-Intrusive Sensors For Measuring Soil Physical Properties. HGCA Project Report 302
Lund, E.D,Christy, C.D and Drummond, P.E. (1999) Practical applications of Soil Electrical Conductivity Mapping, published in the proceedings of the 2nd European Conference on precision Agriculture Odense Denmark July 1999.
Mcneill, J. D. (1980a) Electrical Conductivity of soils and Rocks. Technical note 5.
Geonics Limited. Canada
New Opportunity Fund(2003)[www document]
http://www.nof.org.uk/index.cfm?loc=news&inc=presstemp&prnumber=595&grantlink=no accessed June 3rd 2003).
Rhoades, J.D., Raats, P.A.C., Prather, R.J., (1976). Effects of liquid phase electricity conductivity, water content and surface conductivity. on bulk soil electrical conductivity. Soil science Society America. Journal. 40: 651-655.
Rhoades, J.D.; Shouse, P. J.; Nahid, A.; Manteghi & Lesch, S. M. (1990) Determining Soil Salinity. from soils Electrical Conductivity Using Different Models and Estimates. -Soil Science of America J. 54, 46-54.
Rhoades, , J.D.; Manteghi, N.A., Shouse, P. J. and Alves.W. J. (1989) Soil electrical conductivity and soil salinity: new formulations and calibrations. Soil Science Society of America Journal 53: 433-439.
Sudduth, K.A.; Kitchen,N. R.&Drummond, S. T.(1999): Soil conductivity Sensing on Claypan Soils: Comparison of Electromagnetic Induction and Direct methods. In;proceedings 4th International Conference on Precision Agriculture,pp979-990.ASA,CSSA,and SSSA, Madison, WI
Willaims, B.G., and Hoey, D (1987) The use of electromagnetic induction to detect the spatial variability of the salt and clay contents of soils. Australian, J. Soil Res 25:21-27
Veris Technologies (2003) Veris 3100 Soil EC Mapping System, Operating Instructions (v 1.76) [www.document].< http://pasture.ecn.purdue.edu/~gem/class/asm322/soils/OPM%20draftv1.pdf> (accessed 19th May 2003).
Sport England. (2003) Lottery Fund [www document]. <http://www.sportengland.org/lottery/lottery_1.htm> (accessed 19th May 2003).