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August 2016

Clipping volume variation from green to green

Ryo Ishikawa won the KBC Augusta tournament at Keya GC in Fukuoka this week. Before the tournament started, he was so struck by the green conditions that he wrote about it on his website.


During the tournament, he putted well, with 27 putts Thursday, 26 Friday, 24 Saturday, and 26 Sunday. He had no three putts and 41 one putts on these korai greens during the tournament.

The greenkeeping staff at Keya GC measure the volume of clippings from 12 greens when the greens are mown. I shared some photos of this process, and some of the results during the tournament this year, in these tweets.

I wondered how the clipping volume at Keya GC during the tournament this year compared to other courses. I also wondered if the variation in clipping volume from green to green during the tournament was different from clipping volume variability during a regular week.

To do that, I looked at clipping volume from 7 consecutive days in which greens were mown. Data from Keya during tournament week in 2016 are in the chart below, along with data from the last 7 mowing days at Keya during July 2016, and data from earlier this year from two different courses with cool-season grass.


As far as consistency in the volume of clippings, the tournament data looks impressive. I would expect that this consistency in clipping volume would result in more consistent ball roll on the greens during a tournament compared to everyday play.

I wanted to look also at the variability in clipping volume from green to green on a particular day. Is the variability in clipping volume from green to green lower during the tournament maintenance? To do that, I calculated the coefficient of variation (cv) for these same data. The cv is the standard deviation (σ) divided by the mean (μ).


I like that the cv during the tournament week was on a downward trend. I don't see a huge difference in the overall cv -- the mean cv for these dates is 0.31 for C3 grass #1, 0.37 for C3 grass #2, 0.32 for Keya at KBC Augusta 2016, and 0.32 for Keya during the last 7 mows of July.

One might speculate that greens with the same growing environment and the same soil and the same grass would have a lower cv. The cv shown here may represent some indication of the microclimate effect on growth across a property.

Something you don't see every day

Next week is the KBC Augusta (KBCオーガスタ) tournament at Keya Golf Club in Japan.

This is a rare event -- a professional golf tournament played on korai (Zoysia matrella) greens.

For more about this grass and these type of greens, see:

I may share a few photos and observations from the tournament. If I do, I'll use the #KBCオーガスタ hashtag. You can also find out more about this grass and its maintenance at the Keya Golf Club Turfgrass Maintenance page or by following Keya GC superintendent Andrew McDaniel.

More about the most sustainable grass hypothesis

I've written about a hypothesis for identifying the most sustainable grass.

Paul Johnson wrote with some feedback on this hypothesis, and a few questions. From Paul:

Some more to consider:
  • What is sustainable really depends on each situation, and that involves temperature and light but also what is expected from the turf area. That’s probably what you mean by location. Here, fine fescue in some cases is most sustainable because it needs the fewest inputs, generally, but that only holds true in lower use areas. Kentucky bluegrass, while needing more water and somewhat more N, its best adapted when the turf area gets a lot of traffic.
  • Not sure what you mean by the assumption of one that doesn’t die when N and H2O are reduced. Do you mean temporarily? Some species here can tolerate the dry summer by going dormant, but only so long or maybe one year.
  • Sustainability for turf area should also consider social aspects too. Would the social need for recreation and green plants (psychological aspects) be included in the sustainability equation, offsetting the inputs of some nitrogen, water, and labor? I think so.

This is a good opportunity for me to clarify and elaborate.

Paul points out that one needs to consider "what is expected from the turf area. That's probably what you mean by location." Yes, embedded in my use of location is the assumption that one is considering only grasses that could produce the desired surface at that location. If the grass cannot produce the desired surface at that location, no matter the inputs, then I omit it from the comparison, or only include it as an extreme case. For example, Tifeagle in Siberia is an extreme case that I sometimes use.

Paul asks what I mean by N and H2O reduction. He asks if I mean temporarily. I don't have a good answer for that one. I'm thinking long term, something like 5 to 10 years, and imagining which species disappear (die) over that time period when N and H2O are supplied in low amounts. The "low amounts" I refer to must be enough to produce the desired surface for at least one species, but will not be enough for the species that die. For some background on this, see the seashore paspalum that was naturally replaced by manilagrass in South China. I recognize that this may appear a bit convoluted, and I will work on a better and more specific explanation of this part of the hypothesis.

For the third point, the social aspects, I'm going to classify this consideration as an important one that I agree with, but that is outside the bounds of what my sustainable grass hypothesis is attempting to predict. I'm trying to compare species. I'm not trying to compare a grass surface vs. alternative surfaces.

I'll rephrase the hypothesis here, hoping I don't veer too much from the original.

Let's say we require a turfgrass surface with characteristics defined as X.

The geographic location we can call G. At G, there will be a list from 1:n species that can possibly produce X. I'll call that list of species S. What I want to do is find the one species from the list S that will require the fewest inputs to produce X at G.

I'm proposing that the one species I'm trying to find can be identified at G by finding the species with the most growth per unit of N and unit of H2O applied. That's what I mean by this hypothesis:

The most sustainable grass for a given location is the one that has the most growth per unit of N and per unit of H2O applied.

Monthly Turfgrass Roundup: July 2016

Here's a roundup of turfgrass articles and links from the past month:

A lot of rain in a short time. This is what the southwest monsoon looks like.

This video from STRI about putting green performance measurements:

UMass announces schedules for certificate programs.

Michael Breuer showed bunker sculpting at Bandon Dunes:

Shade and photosynthetic light in Sydney.

New turf disease publications from Purdue.

Soil temperature and fairway management in a classic article by Oscar Miles.

Jason Haines on why he uses growth potential scheduling for fertilizer.

Reasor et al. on the variability of Cynodon on putting greens.

Reasor collecting data on putting greens in Thailand:

A hypothesis about the most sustainable grass.

For more about turfgrass management, browse articles available for download on the ATC Turfgrass Information page, subscribe to this blog by e-mail or with an RSS reader - I use Feedly, or follow asianturfgrass on Twitter. Link and article roundups from previous months are here.

Animated chart of potential and actual PAR at Batesville for the first 214 days of 2016

The photosynthetically active radiation (PAR) changes at a location by time of year and by cloud cover. This animated chart shows what the average photosynthetic photon flux density (PPFD) would be for every 5 minute interval in clear sky conditions, and also shows what the actual PPFD was. Adding up the PPFD from every second gives the total PAR for a day -- the daily light integral (DLI). This chart shows potential DLI under clear sky conditions, as well as actual DLI.


Is it normal to be cloudy like this?

2016-07-17 10.23.40

On July 17, I was in the Tokyo area with Jim Brosnan. The daily light integral (DLI) in Tokyo on July 17 was 14.2 mol/m2. Jim asked me if it was exceptionally cloudy that day. Not really, I answered. I told him that the such cloudiness was normal.

Now that July 2016 is over, I looked at the DLI for every day in July at Tokyo and also at Batesville, Arkansas. Both are at about 35.7°N latitude, so the day lengths will be identical.

The lowest DLI at Batesville in July was 22.8 mol/m2 on July 29. In Tokyo, there were 10 days in July with a DLI less than 22.8 mol/m2, including 5 days with a DLI less than 10 mol/m2. In that context, the cloudiness on July 17 was not exceptional.

To see more, check out the average hourly PPFD and DLI values for Tokyo in this chart and for Batesville in this one.

Warm-season turfgrass growth rates and competition at 35°N

Mike Richardson pointed out that the growth rate of zoysia is less than bermuda, so by implication there must be something other than growth rate that allows zoysia to invade bermuda. That is, in the situations when bermuda and zoysia are growing together -- competing -- when zoysia appears to grow faster, Mike suggests it may be a factor such as turf density that allows such a result, because bermuda grows faster than zoysia.

I've outlined a hypothesis about grass growth rates and their required inputs, and have more to write about that later. In that hypothesis, I mention location, and in my recent discussion with Mike about the growth rate I said that there is a variety by climate interaction. By climate, I mean the same as location. I'll use these words interchangeably.

Let me try to explain what I mean by an interaction by climate. I'll use data from Tokyo, and from Batesville (2016 data) and Fort Smith (climatological normals data). These locations are all about 35°N.

Light, temperature, plant water status, and leaf nitrogen content all influence growth. In turfgrass management, light and temperature generally can't be controlled; plant water status and leaf nitrogen content can be modified by turfgrass managers. We can imagine that bermuda and zoysia are growing side by side, or together, and then think of what may happen with modifications to these growth-influencing factors.

On average, this is the part of the climate that can't be controlled, at Fort Smith and at Tokyo, shown in 2-dimensional space.


That's a similar temperature range but different amounts of sunshine. Thus, there is no overlap during the months when warm-season grasses are growing. I focus on light and temperature because the water and the nitrogen can be adjusted by the turf manager.

Temperatures for 2016 are pretty similar through July 30. I express temperature here as the cumulative sum of growing degree days.


Ok, so temperatures are similar. If it were only temperature that influences growth, one would expect the grasses to perform pretty much the same at these locations. If bermuda does have an inherently faster growth rate than zoysia, then in this side-by-side comparison, with the same temperature, then bermuda should grow faster at both locations.

I downloaded the global solar radiation data also and then converted it into photosynthetic radiation units. This is Batesville for the first 7 months of 2016.

2016 Batesville DLI and PPFD through July 31

This is Tokyo for the first 7 months of 2016.

2016 Tokyo DLI and PPFD through July 31

In 2016, there has been more photosynthetic light at Batesville than at Tokyo.


The DLI was pretty much the same from January to March, but since the start of April Batesville has jumped ahead by about 1,000 moles/m2. In the past 4 months, Tokyo has accumulated about 4,000 mol/m2 and Batesville has accumulated about 5,000 mol/m2. That's a log percentage difference of 22%. The difference has been especially pronounced in June and July -- the hottest months of the year so far.

Imagine growing bermuda and zoysia in 10% shade at the same temperature. Bermuda may grow faster than zoysia. Now imagine 20% shade. Probably the same result. How about 30, 40, and 50% shade? 60% or 70% shade? At some point, the growth rate of zoysia will be greater than the growth rate of bermuda. The bermuda will die in shade under which the zoysia can still produce a turf.

Consider now that there are varying growth rates among bermudagrass varieties, and also among zoysia varieties. That's what I mean by the location (or climate) by variety interaction. Take an inherently faster-growing zoysia, mix it with bermuda, grow it in a climate with high temperatures combined with lower DLI, mow the grass and make sure plenty of water is applied during the dry season, and see which one grows faster. It's not bermuda.

Yes, with a high DLI, plenty of fertilizer, moderate water supply, and high temperatures, bermuda grows faster than zoysia. Here's a photo of the ATC research facility putting green during grow-in. It's easy to tell which plots are zoysia -- those closest to the camera.

grow-in 22 dec

But if one thinks of growth as something that happens over years, at a location, with the grasses maintained as turf, then one can find the growth rate of zoysia can be higher than that of bermuda.

I find it useful to look at growth rate in those terms, rather than trying to explain it as a response to density or as competition for some other factor.

Air temperature and surface temperature

I read a Greencast Tech Note that said:

"Given adequate moisture is present, and no air movement, the turf canopy temperature is 15 F higher than the air temperature in full sun."

I was surprised by that, because I've been measuring air temperature and surface temperature occasionally since 2014 and I have not measured a single turf canopy in which there was a 15 F difference. I make my measurements in the C scale, and 15 F is 8.3°C. The highest increase I've measured in canopy temperature over air temperature is 6.6°C.

Here's the data I'm working with from multiple days in 2014, 2015, and 2016. These are 50 measurements from putting greens in Thailand and Japan, sometimes in full sun and sometimes cloudy, with these temperatures, humidity, and wind. Grasses are bermuda, seashore paspalum, and korai.


The range in air temperatures is 25.9 to 35.1°C (78.6 to 95.2°F).


The range in humidity is 44.3 to 81.9%.


The range in surface temperature is 24.3 to 39.5°C (75.7 to 103.1°F).


For the data I've measured, it doesn't seem like wind speed has a huge effect. The wind has ranged from 0 to 4 m/s (0 to 8.9 mph). I've only got wind speed for 23 of the 50 measurements.


Here's the difference between air temperature and surface temperature.


These aren't all in full sun, because there was sometimes cloud cover, but many are in full sun, and there is nothing that reaches an 8.3°C difference, which is what one would expect if the Tech Note was correct that "the turf canopy temperature is 15 F higher than the air temperature in full sun."

After I asked "Really?" about this difference, Turf-Vu sent me data from their Hawk-Eye system and suggested a certain day to look at as being sunny and low wind.

I looked at the example air and surface temperature from Turf-Vu for July 28.


Then I plotted the difference.


On a celsius scale, compared to my data by time of day, this shows that almost all my measurements have a smaller difference between surface and air temperature.


Either my thermometers are off by a few degrees, or there are often situations in which the turf canopy temperature is less than 15 F higher than the air temperature in full sun.

Species effect, perhaps? Or cool-season versus warm-season grass?

This video from PACE Turf has more info about temperature, air movement, and cool-season grasses.