Field day poster: 5 grass varieties, 3 levels of salinity, and a month to grow-in -- or not

This poster for today's field day describes what happened when we planted five grass varieties as stolons and then supplied irrigation with different amounts of salt in the water. I think two things might surprise you. First, some of the grass varieties, including a particular manilagrass (Zoysia matrella), can reach full coverage in about one month after planting. Second, irrigation with salty water, so long as enough is applied, doesn't slow down the growth of many warm-season grasses.

image from

When the grasses can grow like this, it shows that managing the problems with water quality are closely tied to the quantity of water supplied. I will be talking more about this tomorrow when we are back for another day of seminars.

After 28 days, grow-in and salinity differences


I've been growing grasses in a plastic house with a lot of help from colleagues at the Thailand Institute of Scientific and Technological Research (TISTR). The idea was to see how these grasses grow in after being planted as stolons, and to see what happens when salt is added in the irrigation water. I'll be discussing this experiment at the field day in Chonburi next week.

The picture above shows the grasses that receive the irrigation with 330 ppm total dissolved solids (TDS), 28 days after planting. The seashore paspalum looks the best, and the nuwan noi manilagrass has grown-in almost as fast. The hosoba korai, which is a beautiful grass once established, still hasn't covered much of the pot.

Another thing I've found interesting is measurements of salinity in the soil with the new TDR-350. All the pots are supplied with the same quantity of water. But different sets of pots get different amounts of salt in the water.


The soil salinity in these pots is changing depending on which irrigation water is applied. That's just as expected.

For more about the TDR-350, see this webinar.


Grow-in potential

These pictures were taken 28 days apart. Here's what the grasses looked like yesterday, on February 24. That was 4 weeks, exactly 28 days after planting.


On 27 January, five different grass varieties were planted from stolons. The grasses, shown from left to right, are:

  • manilagrass (nuwan noi)
  • tropical carpetgrass (yaa malay)
  • seashore paspalum (salam)
  • manilagrass (hosoba korai)
  • bermudagrass (Tifway 419)

For the first 10 days after planting, all the grasses were irrigated with 330 TDS (total dissolved solids, in units of ppm) water. For the next 18 days, the grasses shown above were irrigated with 4,500 TDS water.

The planting rates for the stolons ranged from 99 g/m2 for the nuwan noi to 312 g/m2 for the yaa malay. This is the mean mass for the stolons planted in the pots. We cut the stolons into 10 segments with 3 nodes each and then weighed them and planted them; each 0.02 m2 pot was planted with 30 nodes (1,500 nodes per square meter).

This is what the pots looked like immediately after planting, on January 27.


I think this is interesting for two reasons. One, this gives some indication of the grow-in rate (and relative rates) of various grass varieties. Second, this shows the tolerance or not of the grasses to different salt levels in the water.

One set of grasses is getting water with salt (TDS) at 330 ppm, the one pictured are getting 4,500 ppm, and another set are being irrigated with 9,000 ppm.

I'll be talking about this, and showing some of these grasses, at the upcoming Sustainable Turfgrass Management in Asia conference.

An extremely useful tool for the study of putting green speed variability

No, I'm not talking about the stimpmeter. And this tool will probably only be useful for a few people. In fact, the tool is probably not what you think it is. But this story may be of general interest.


I'm quite interested in putting green speed. I've written numerous articles about green speed and variability in the measurements for ゴルフ場セミナー. In English, you can read this report about some of the measurements I've made.

What I find more and more interesting is the variability in putting green speed. Not so much from course to course, or day to day, but more so from green to green on the same day, or from location to location on the same green.


When the average of 18 different greens is 11 feet, what is the range of measurements on individual greens? Is it from 9 feet to 13 feet? Or is it from 10 feet 8 inches to 11 feet 4 inches?

As usual when studying this topic, I found myself studying Thomas Nikolai's A superintendent's guide to putting green speed.


That led me to Radko et al. on A study of putting green variability.

So here is where the extremely useful tool comes in. I wanted to get the data from the article, to make some calculations of my own. So I turned to the WebPlotDigitizer. This has been an extremely useful tool for me in the past, and I was glad to use it again today to study variability of putting green speed.


With those data from the chart now in a file I can work with, I've been able to make a number of calculations.

And how about those green speeds in 1980?


High expectations


I've rarely been so excited to read an article. Last week when I saw Energy use and greenhouse gas emissions from turf management of two Swedish golf courses, by Tidåker et al., I immediately dropped what I was doing and read it.

If you've talked with me about turfgrass management sometime in the past 18 months, our conversation may have touched on differences in energy use, and the difference in carbon emissions, caused by differences in grass selection and maintenance practices. In fact, this is one of the topics Dave Wilber and I discussed as part of our wide-ranging conversation during episode 14 of the Turfgrass Zealot Project. I don't know how to make these calculations yet, but finally with this article I've read something that provides the calculations, and that I can study so I can figure out how to do this myself.

Gelernter et al. wrote in 2014 about quantifying sustainability on golf courses. We suggested measuring and tracking the annual:

  • quantity of fertilizers applied
  • quantity and toxicity of pesticides applied
  • quantity of water used
  • fuel volume
  • labor hours
  • electricity used

One can keep track of those quantities, together with the associated costs, and from that one can check the efficiency of the operation. These quantities also serve as some of the basic data requirements for the GEO OnCourse program.

But the quantities we wrote about in the GCM article are all different: kg of N, kg of fungicide, L of water, L of diesel, kWh of electricity. By expressing all the turf maintenance activities in units of greenhouse gas emissions (expressed as CO2 equivalents) or energy use, one then has a single number for the entire course, or for an area of the course, or per square meter, that can be used to compare to other courses next door or around the world. And the use extends well beyond comparisons to other golf courses; one can use these units to compare the maintenance of a golf course to anything that has greenhouse gas emissions or energy use.

I had high expectations for the article, and I wasn't disappointed. The authors described the fertilizer rates, topdressing rates, water use, mowing frequencies, and much more, for the two courses, and then expressed those units in GHG or energy use. N rates were up to 22 g/m2, as were K rates (I think the rates for golf course turf in Sweden should usually be less than reported in the article -- using precision fertilization, or temperature-based growth potential and MLSN, will lead to lower recommended amounts of fertilizer). Sand topdressing on greens was about 10 mm/year. Irrigation of greens was about 300 mm/year. Mowing of fairways was about 85 times/year, and greens were mown about 180 times/year.


I think this is fascinating because one can consider Sweden to have relatively low inputs. If you're familiar with golf course maintenance in a tropical environment, let's say in Phuket, you might expect fairways to be mown more than 150 times a year, greens more than 300 times, about double the fertilizer, and more than twice the water use. Now imagine what happens when comparing irrigated vs non-irrigated rough? Seashore paspalum wall-to-wall vs. manilagrass? A 60 ha sandcapped golf courses vs. one with drainage and 2 cm of sand topdressing? Overseeded vs. not? The differences in energy use and greenhouse gas emissions will be huge.

What did Tidåker et al. find in their analysis? The entire paper is worth a careful study, but in summary they found mowing was the most energy-consuming activity, and mowing together with the production and application of fertilizers (especially N) contributed the most to greenhouse gas emissions. They suggest:

Appropriate measures for reducing energy use and carbon footprint from lawn management are thus: i) reduced mowing frequency when applicable, ii) investment in electrified machinery, iii) lowering the mineral N fertiliser rate (especially on fairways) and iv) reducing the amount and transport of sand for dressing. Lowering the mineral fertiliser rate is of particular importance, since GHG emissions originate from both the manufacturing phase and from N turnover after application.

Jason Haines has some interesting reads about how turf condition can be improved while at the same time reducing inputs:

Fall potassium and winter traffic on a bentgrass green


I just finished reading Winston Mirmow's thesis on Fall potassium fertilization and winter traffic effects on a creeping bentgrass putting green. I downloaded this in September and read the abstract then, but only read the full thesis now.

As I've mentioned previously, I like reading theses, because they are the most recent research results presented in a detailed format. I can learn new things and correct my thinking if I've been in error about something. I really enjoyed this one. It was about two of my favorite topics: potassium (K) and winter traffic.

The thesis

You can read the thesis yourself if you like. Here are the three things I found most interesting.

First, the fertilizer. Adding supplemental K in the autumn had no effect on traffic damage over frozen turf in the winter. Where no K was added was the same as where K was added. The low rate was 0 K, and the high rate was 7.3 g K/m2 (1.5 lbs K/1000 ft2).

Second, traffic was applied at 8 a.m. to the creeping bentgrass green when the canopy temperature was below freezing. That worked out to be 19 traffic events in the first winter of the study, and 18 traffic events in the second winter of the study. Yes, traffic decreased the turfgrass quality. The trafficked turf was worse than the untrafficked turf from January 15 until March 15.

But guess what? The untrafficked turf in mid-winter was rated at less than acceptable quality too, from February 1 to March 15. The trafficked turf was worse, and the untrafficked turf wasn't very good either.

And then when the temperatures warmed up in the spring? "When the weather warmed to the point where no more traffic treatments were applied, there were no significant differences in turfgrass quality ratings." That is, by April 1, all the plots were the same, whether they had been protected from traffic when frozen, or whether they had received 8 a.m. traffic over frozen turf.

Third, the soil K was below the MLSN guideline and the grass did not respond to K fertilizer. That's not at all what this experiment was about, but it shows something that I often explain, and will do so here again.

Even though the MLSN guidelines are lower than conventional soil nutrient guidelines, they are still set to be conservative. By that I mean the quantity of fertilizer recommended when using the MLSN guidelines is deliberately meant to err in one direction -- on the side of too much fertilizer, rather than too little. Of course the MLSN approach will in most cases recommend less fertilizer than will conventional guidelines, but at the same time there is a built in margin of error to make sure there is no deficiency.

Results like those presented by Mirmow, where the soil is below the MLSN level (Mirmow's results, converted to Mehlich 3 units, had values in the 20 to 30 ppm range), but the grass does not respond to applications of the element, indicate that the element was not deficient. Thus, more confidence that the MLSN guidelines are conservative.

Winter traffic on bentgrass

I used to be horrified of traffic on frozen bentgrass. I still kind of am, but I have also seen all kinds of traffic on frosted or frozen or snow-covered bentgrass.


And my observations of what happens are similar to Mirmow's research. The traffic makes the grass worse than where no traffic occurred, but in early spring everything is back to normal.

These are the practice greens at Habu CC near Tokyo in mid-March. This is after a winter of play on frosted greens, of snow removal by any means possible so that golf could be played, and by mid-March these greens were already recovered from the damage.


A request to authors

I was pleasantly surprised to see the soil test and tissue test data shared in their entirety in this thesis. That's great.

However, for the soil test data, the methods section doesn't give the sampling depth. And the methods section only says which lab the analyses were conducted at, but not which testing method was performed. Because both the sampling depth and the testing method affect the soil test data, authors of technical papers should include that information. I looked up the lab in this case, and can infer that the test was Mehlich 1.

I've reviewed a paper recently with the same issues. Essentially, it said soil samples were sent to X lab for analysis and the results are in Table Y. That's fine, but one can't understand the numbers unless some details of the analysis are given.

The relationships between golf and health, with multifunctional golf facilities thrown in just for fun

Golf and health

Yesterday I saw the new paper by Murray et al. in the British Journal of Sports Medicine on The relationships between golf and health: a scoping review. The reviewers identified 301 studies on this topic that met their search criteria, and then they summarized the results in terms of:

  • participation
  • golf and physical activity
  • golf and longevity
  • golf and physical health
  • cardiovascular system
  • respiratory system
  • metabolic health
  • cancer risk
  • musculoskeletal health
  • golf and injury
  • golf and mental health/wellness
  • mental health
  • mental wellness

It's a comprehensive review, and if you are interested in this topic, I suggest you read the paper. From the golf and physical activity section, here's the calorie burn and walking distance:

Studies assessing calorie expenditure during golf typically classify golf as a moderate intensity physical activity with energy expenditure of 3.3—8.15 kcal/min, 264—450 kcal/hour, and a total energy expenditure of 531—2467 kcal/18 holes. Golfers walking 18 holes take between 11,245 and 16,667 steps, walking 4—8 miles, while those playing and riding a golf cart accrue 6280 steps or just under 4 miles.

This ties in well with something I've written about before, which is golf and health and multifunctional facilities. In the words of Don Mahaffey, "golf is good for you" but this aspect of golf is often overlooked.

See these posts for more from Don Mahaffey, and from info about STERF's research into multifunctional golf facilities:

A few examples of multifunctional facilities


Weddings and banquets, of course, are common at many facilities. This is a chapel at Club de Golf Escorpión in Valencia.


Use of a practice tee for sports training, at Escorpión.


A football field at El Saler, just to the right of the 8th and 9th holes. This has been used by the Spanish national team and by Valencia CF, among many others.


Birdwatching is a common activity at El Saler, and this sign near the clubhouse shows many of the species one can find in this area.


Many golf facilities have trees or hedges with fruits or nuts. At Golf Costa Brava, the cork oaks are harvested.


Walking, hiking, or biking the Cami Ral will take one right through PGA Catalunya.


Hiking paths at Domaine de Falgos in the Pyrénées start at the golf clubhouse.


The driving range fairway at Domaine de Falgos doubles as a rugby field.


Thailand putting green performance in July: a summary


While Eric Reasor was collecting the data on ball roll dispersion in Thailand -- read yesterday's post for more about that -- I collected data on the the same greens. The data summary shown here are the data I collected from 19 greens on 19 different courses. The grasses on these greens included various bermudagrass varieties, seashore paspalum, and manilagrass.

I took 3 stimpmeter readings per green, measured 9 locations per green with a 500 g Clegg soil impact tester, and used a TDR-300 with 7.5 cm rods to measure soil water content at those same 9 locations. I also made some measurements of soil temperature, surface temperature, and air temperature.

I showed the distribution of air temperature (median was 31.8°C) and heat index (median was 38.9°C) in a previous post.

Here's the summary of soil and surface temperature from these greens.



Putting greens in Thailand tend to be relatively soft, and the measurements in July were consistent with previous measurements.


This is the distribution of soil water content.


This is the distribution of green speed.


I measured the speed on each green in 3 different locations. With that, I get some idea not only of the green speed, but also about the variation in green speed. I express the variation in green speed within a green as the coefficient of variation (cv), which is the standard deviation of the measurements on a green divided by the mean of the measurements on a green.

Then I compared the distribution of cv for the 19 greens measured in Thailand with the cv for 26 greens measured during the recent KBC Augusta tournament in Japan. Under tournament conditions, there was slightly less variation in green speed. But many of the greens in Thailand had variation the same as measured during a tournament.


For more summaries of putting green measurements, and of measurements from greens in Thailand, see this post on playing with numbers. There are links to handouts and other data sources there. Or look at the charts in these slides:

Bangkok is a long way from Knoxville


When Eric Reasor came to Thailand in July, he brought along measuring tools to assess how golf balls roll across putting greens.


He visited 22 golf courses in 5 days. Here's a map with the locations visited marked as an orange .


The primary measurement he made was rolling balls using a customized Perfect Putter, so that all balls were launched on their roll at the same line and with the same pace.

Each ball was marked where it stopped.


Then the width and length of the dispersion area was recorded. Sometimes the balls dispersed a lot before they stopped.


On other rolls, or other greens, the dispersion was relatively small.


The purpose of the project is to study what factors influence the dispersion of the ball as it rolls across the green. Is it the grass species? Is it the mowing height? Do off-type grasses affect the dispersion? Is it something else? This is all part of his research about bermudagrass off-types. For an overview of this problem, see Reasor et al. on the genetic and phenotypic variability of interspecific hybrid bermudagrasses (Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt-Davy) used on golf course putting greens.

As we traveled around central Thailand, we got to see all the major species used as turfgrass in this region. For more about that, see What grasses are growing on golf courses in Thailand? Here's a few notes about what we saw in July.

Seashore paspalum must be maintained with a relatively rapid growth rate in this climate. If paspalum is not kept growing, it will be overtaken by other grasses. Therefore, a lot of work is required to keep paspalum surfaces in a playable condition, and we saw verticutting on paspalum fairways to manage the organic matter.


There are lots of birds on Thailand golf courses. These are Asian openbill and a little egret.


I haven't identified this bird yet.


Bermuda greens and seashore paspalum fairways are pretty common around Bangkok.


Manilagrass (Zoysia matrella) is even more common. Let's call it ubiquitous. You can find it at the airport, along the expressways, in lawns, on golf courses, and on football fields and tennis courts.


This is bermuda on greens with the nuwan noi variety of manilagrass on fairways. The fairway would have been planted to bermudagrass, but over time the nuwan noi comes to dominate the sward.


In parks, palace lawns, and temples, one tends to find tropical carpetgrass (Axonopus compressus) under the trees and nuwan noi manilagrass in full sun. For more about the grasses on lawns, see this post about climate and this one about botanizing in Bangkok.


We were lucky with the weather for that time of year. With 22 golf courses visited, we got rained out zero times. Normal weather in July at Bangkok will have 155 mm of rain and 13 rainy days.

We saw a bit of rain, but not enough to interfere with our work.


It was plenty warm. These are temperatures and heat indices at the time I collected data at 19 of the courses. It was only less than 30°C twice. What a great place for a tropical holiday! Or in this case, for 5 days of intensive data collection.



MLSN and the probability of a response to fertilizer application

Travis Shaddox shared an impressive list of quotes about BCSR. Some key words from those quotes include irrelevant, inefficient, pseudoscience, should not be used, and NOT recommended.

Instead of BCSR, Allan Dewald asked about MLSN, and Travis mentioned that MLSN doesn't provide a response probability, but maybe that is coming.

We won't provide a response probability for MLSN because it is not a fertilizer calibration. These are two different things.

MLSN is a method for interpreting turfgrass soil test results, based on an analysis of thousands of soil samples in which turfgrass is producing a good surface. MLSN is developed from thousands of soil test results in which turfgrass performance was fine.

This is a paraphrase of what we wrote in the MLSN preprint:

Traditional soil test calibration for turfgrass is impossible and will never be done. It is impossible because turfgrass is a global crop, with many species and varieties used, in thousands of soil types, in every possible climate, across a range of turfgrass performance requirements. MLSN is a method which allows turf managers to ensure their turf is always supplied with enough nutrients.

For the full details of what MLSN is, and to see the data supporting it, please read the paper.

Now to elaborate on why I think probability of a fertilizer response is not the right way to do calibration for turfgrass, and why MLSN works even though it is not a traditional fertilizer calibration.

Calibration is based on classifying soils with different levels of nutrients into categories based on probability of yield response. For full details, see Chapter 14 by Douglas Beegle, Interpretation of Soil Testing Results, in Recommended Soil Testing Procedures for the Northeastern United States.

The classification into probability of response, according to Beegle, involves three categories. In the below optimum category, also called very low, low, or medium, "the nutrient is considered deficient and will probably limit crop yield. There is a high to moderate probability of an economic crop yield response to additions of the nutrient."

In the optimum category, also called sufficient or adequate, "the nutrient is considered adequate and will probably not limit crop growth. There is a low probability of an economic crop yield response to additions of the nutrient."

The third category is above optimum, also called high, very high, or excessive. In this category, "the nutrient is considered more than adequate and will not limit crop yield. There is a very low probability of an economic crop yield response to additions of the nutrient."

In turfgrass management, one is not trying to maximize economic crop yield. Rather, one is trying to produce the desired surface performance, and does that by modifying the growth rate of the grass.


I wrote about this in A Short Grammar of Greenkeeping.

When modifying the growth rate of the grass, one is often trying to minimize the growth rate. Let's try anyway to apply these probabilities of response to turfgrass.

If we take the categories for probability of response, as defined by Beegle, we notice that he has referred to crop yield and to an economic crop yield response. Let's drop that and substitute turfgrass performance and turfgrass performance response. Now we have a definition that makes sense for turfgrass.

Below optimum: The nutrient is considered deficient and will probably limit turfgrass performance. There is a high to moderate probability of a turfgrass performance response to additions of the nutrient.

Optimum: The nutrient is considered adequate and will probably not limit turfgrass performance. There is a low probability of a turfgrass performance response to additions of the nutrient.

Above optimum: The nutrient is considered more than adequate and will not limit turfgrass performance. There is a very low probability of a turfgrass performance response to additions of the nutrient.

I think it is impossible to do soil test calibrations for every grass, climate, soil, and turfgrass use combination. So how can we figure out some way to interpret turfgrass soil tests and assign the results into one of those three categories? That's where MLSN comes in.

What we did with MLSN was look at thousands of soil test results from the optimum and above optimum categories. The turf was performing well at the time the sample was collected, so the soil was unlikely to be in the below optimum category.

Then we studied the distribution of the nutrient levels in those soils, threw away the bottom 10% to make sure we have some buffer against being too low, and identified the MLSN guideline as the level in the soil that we don't want to drop below. Nutrient recommendations are then made to ensure the soil doesn't drop below that minimum.

Because the soils used to identify the MLSN guidelines were already in the optimum or above optimum category, there is an implied probability in the MLSN recommendations. That is, keeping the soil from dropping below the MLSN guideline is the same as keeping the soil at a level with a low to very low probability of a turfgrass performance response to additions of the nutrient.

Although such probabilities are implicit in the MLSN approach, we do not use those terms or classify soils into categories. With MLSN, we have just two categories -- enough, and not enough. Turfgrass is a perennial crop, and soil nutrient levels go down as the turf harvests nutrients from the soil. The MLSN fertilizer recommendations consider how much of an element is in the soil now, how much of that element the grass will use over time, and then makes a recommendation to provide enough of that element to meet all the grass requirements.