After all, its only grass, isn’t it?!
particularly wet area, a walk on/off or access point. Choose an average spot which represents the usual state of the turf. Always take your measurements from the same places.
Replicates - Sample from at least 3 different places to make sure any pattern seen is genuine and not just a chance occurrence and to be able to calculate an average (add the results up and divide by the number of samples).
2. Experiments
Once you’ve got regular monitoring underway you might want to investigate the effect of a change in management or a new product, for example. This requires comparing treated areas to non- treated areas (known as the control). Keep the experiment simple - only compare one factor eg for mowing height - cut one area at your normal height of cut and one area at a higher/lower height and record the effect on grass cover and root depth. It is important to keep the two areas the same as far as possible except for the chosen treatment, in this instance the cutting height. Therefore shading, amount of fertiliser and water and amount of wear should all be the same on both treatment and control areas so that you can see the effect of cutting height alone. Make sure the testing areas are a reasonable size to take several readings from. Setting aside a dedicated area for trials is not always possible and so for most turf managers it will be most practical to quietly incorporate the trial into their playing area eg leaving half of each goalmouth 5 mm longer. However, if the treatment will cause obvious variation on the turf which is unacceptable to the players, then an out of play area will have to be found. In this case, plots could be marked out to compare treatments.
3. Converting your data into facts and figures
To impress the Club financers you should convert your trial data into statistics - not as difficult as it sounds. Firstly you need to have taken sufficient samples so for instance, you may have taken a grass cover reading once a week for a period of 12 weeks. From this data, a graph can be drawn (either simply by hand on graph paper or with the aid of a computer and software such as Excel) to easily visualise any changes that have occurred. The graph will clearly show a trend – that the longer cutting height shows consistently higher grass cover after 3 weeks. Now you need to perform some simple statistics on the data to see if this is a true effect or just a small change caused by chance. For simplicity it is best to compare 2 time points so we shall look at the grass cover from the start of the trial compared to the final reading 3 months later.
Green speed
Standard Deviation - Next calculate the standard deviation; this is how much variation there is in the data from the average. The easiest way to calculate this is to use the Std Dev button on a calculator (denoted by the Greek letter sigma: ó) or use computer software such as Excel. For those who don’t have access to either of these things- the long-hand version is as follows:- Standard deviation is the square root of the variance. To obtain the variance subtract the average grass cover from each data reading and square it (multiply by itself). Next calculate the average of these
squared-deviations (add them up and divide by 2). These figures will suggest the results
are accurate and not just down to chance. The smaller the value for standard deviation, the better and the more accurate the results are likely to be. A standard deviation that is greater than half of the mean (average) would suggest inaccurate results and either the experiment needs to be repeated or more samples taken.
Standard Error (SE) - Finally, calculate the standard error which measures the reliability of the data - whether it can be trusted or whether a longer study/more samples would be required. Divide the standard deviation by the square root of the number of replicates taken or the number of measurements being compared (in our case 2 - week 1 and week 12) equals 5.3%. This means that for the 25 mm cutting height, the average cover was 47.5% + or - 5.3% (i.e.
Measurement Compaction
/hardness Grass cover Equipment required
Penetrometer – available from turf suppliers
it ranged from 42.2% to 52.8 %). For the 30 mm height, the average cover was 62.5% + or - 8.8% which ranges from 53.7 to 71.3%. If the upper limit for the 25 mm treatment was within the SE range for the 30 mm treatment then this would suggest that these two figures are not statistically different i.e. there has been no effect of increasing the cutting height by 5 mm. This is more easily seen when drawn on the graph as standard error bars - there is a statistical difference as the standard error bars for each cutting height do not overlap. The table below gives some ideas for data to collect which is easy to read on site or can be sent for a simple laboratory test.
The STRI laboratory provides a
comprehensive analysis service including all those mentioned in the table. In addition, the Journal of Turfgrass Science published by the STRI since 1929 reports the results of many relevant studies which could be presented to add weight to your own data.
Using science, whilst not essential in turf management, is a useful tool to confirm what you know already, assess new methods of working and/or simply to boost enthusiasm and confidence in your job.
Stella Rixon BSc (Hons) MBPR STRI Agronomist T: 01372 270342 Mob: 07870 203916 email:
stella.rixon@stri.co.uk
The full article, with further graphs and tables, can be read on the Pitchcare website
Useful for:
drought/water-logging. Measure against root depth, thatch/OM, grass cover and/or soil moisture
Visual assessment or make Assessing wear. Graph it against a 0.5 m square of wire, throw on to test area and record grass cover in the box
number of matches, number of golfers, amount of rainfall
Stimp meter available from turf suppliers
Organic matter Soil corer - send samples to lab for ignition test
pH – soil pH - water
Root depth Soil moisture Worm casts
pH meter or send sample to lab
Corer and ruler
Hand-held moisture probe available from turf suppliers
number of casts in an area treatments, sand dressings, removing clippings etc.
Visual assessment. Count
Monitoring effect of cutting height, rolling, grooming, rainfall, fertiliser
against green speed, soil moisture, root depth, hardness
acidifying fertilisers, irrigaiton water
Checking pH is suitable, effect of
General turf health check. Graph it against cutting height, wear, irrigation and/or fertiliser input
it against root depth, thatch, hardness and/or green speed
Checking irrigation efficiency. Early warning of dry patch. Graph
Monitoring effect of acidifying 19 Checking thatch build-up. Graph it Assessing effect of wear, rolling,
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