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August 2016
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September 2016

Dog's footprint and grass susceptibility to this disease

I don't like turf diseases. If there is any fun in them, for me, it lies in only two things. First, is it a particularly well-named disease? Second, how awful are the symptoms?

I enjoy learning disease names and finding those that have the most interesting names. Nothing against brown patch and yellow patch, but those are pretty bland. Dollar spot is more interesting, and elephant's footprint even more so.

Then there are the symptoms. All turf diseases, if left unchecked, can make some hideous symptoms. In their standard form, however, I find some to be more hideous than others. Yellow patch, anthracnose, red thread -- often present, but sometimes only visible to those actually looking for symptoms. Compare to a disease like large patch, which in its standard manifestation is monstrous.

Using those criteria of interesting names and hideous symptoms, one of my favorite diseases is inu no ashiato -- dog's footprint. The name is interesting, and the symptoms are moderately hideous. I was glad to see this new article by Tomaso-Peterson et al. about Curvularia malina sp. nov. inciting a new disease of warm-season turfgrasses in the southeastern United States. From the introduction:

A foliar disease of these warm-season turfgrasses is often observed following prolonged or significant precipitation events such as tropical storms and hurricanes. The disease manifests as distinct chocolate brown to black spots (2–15 cm diam) that appear on Cynodon dactylon or Zoysia matrella putting greens, fairways, and tee boxes. Under high disease pressure the dark spots may coalesce to form large, irregular areas of blighted turfgrass.

"Is this the same as dog's footprint," I wondered?

A Curvularia leaf blight affecting Zoysia spp. in Japan, referred to as dog footprint, shares symptomology to that observed on C. dactylon and Z. matrella in the southeastern United States ... Based on these reports, our hypothesis is that the sterile fungus associated with Curvularia blight and causing similar symptoms in stands of C. dactylon and Z. matrella in the southeastern United States is a novel species of Curvularia.

The species was identified as Curvularia malina.

To date, C. dactylon and Z. matrella are the only golf course grasses from which C. malina has been isolated. Disease epidemics on Z. matrella appear to be more severe than on C. dactylon based on visual field observations. The disease is most prevalent in the spring and fall, which are normally characterized by moderate temperatures and ample precipitation. Symptoms may persist into the summer if prevailing environmental conditions remain favorable and the turfgrass experiences stress from intensive management practices.

So far so good. Dog's footprint is more severe on Z. matrella in Asia than on C. dactylon. However, in Asia the disease is most prevalent in summer, or in conditions characterized by warm temperatures and ample precipitation.

Based on the results of our research, C. malina induces disease symptoms in warm-season turfgrasses similar to those associated with Curvularia leaf blight.

It seems dog's footprint is caused by C. malina. Manilagrass (Zoysia matrella) can get lots of diseases, but in a tropical environment, this species is infected by few diseases, with the most common being dog's footprint.

Here is dog's footprint on manilagrass at Hilo in March.

This is at Okinawa in August.

This is at Manila in August.

This is at Shizuoka in July.

Those are pretty typical symptoms. And they are all on a monostand of one type of manilagrass.

I've noticed that some manilagrass varieties are often showing dog's footprint symptoms, and other varieties rarely do. I usually see this at two different locations in the same town. For example, lots of dog's footprint at site X, and then an hour later at site Y, a slightly different type of manilagrass has no dog's footprint.

Last July, I saw this at one location, on a golf course fairway with a mixed stand of different Z. matrella (korai) varieties and with some patches of C. dactylon.

On one variety of korai, lots of dog's footprint. On the Cynodon and other variety of korai, none.

This disease is ubiquitous on susceptible varieties in East and Southeast Asia. Finding varieties that are less susceptible seems quite possible.

"Anyone who's played golf in Japan will know that many clubs have two greens on each hole"

Selection_101Fred Varcoe wrote about putting greens on Japanese golf courses in the August 2016 issue of Euro Biz Japan. The article, Know your greens (pdf, 3 MB), includes some quotes from me about bentgrass, korai, and how balls roll on putting greens.

For more about the two green system in Japan, see:

And kind of on this same topic, but of more general interest, see Paul Jansen's post on The Japanese Golf Experience.  You'll see more than just grass: breakfast beer, tiny hotel rooms, hot springs, cold springs, blue balls, green tea, and a volcanic eruption.

Shiny app shows the temperature and sunshine combination for 11 cities in Japan


I made a Shiny app with climatological normals data from the Japan Meteorological Agency to show the combination of sunshine and temperature at 11 locations.

@naturalgolf_D asked "What kind of situation is Japan?" With these data, I think it is interesting to compare different locations of interest, and a Shiny app is an easy way to do that.

Six more Shiny apps from ATC are here.

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.



Monthly Turfgrass Roundup: August 2016

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

Travis Russell took some great photos of bermudagrass under different amounts of shade:

Air temperature and surface temperature, is there really a 15°F difference?

GEO released their Sustainable Golf Development Standard:

Gordon Kauffman spoke with John Kaminski on the Tom and John show:

Warm-season grass growth rates and competition at 35°N.

Mike Richardson showed shade effects on bentgrass from experiments in Arkansas:

Is it normal to be cloudy like this?

"an entire year’s worth of aeration in one streamlined operation"

Tom Caliguire keeping bermuda out of paspalum:

From Penn State's Center for Turfgrass Science: Factors affecting green speed.

Animated chart of PPFD and DLI at Batesville, Arkansas.

More about the most sustainable grass.

Golf El Saler from PAELLA PRODUCTIONS on Vimeo.

Thompson and Kao-Kiffin report on reduced nitrate leaching and increased productivity from multiple species compared to monostands.

How much does the quantity of grass clippings vary from green to green?

Tomaso-Peterson et al. wrote about Curvularia malina disease on warm-season turfgrasses in the southeastern USA.

Zoysia putting greens are something you don't see every day.

Frank Rossi and Roch Gaussoin spoke on Turfnet Radio about organic matter, coring, and topdressing:

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.

Fast release fertilizer, fertilizer burn, and root growth

I gave a seminar in July in which I discussed how much one can expect grass to grow.

I said something like "grass can always grow more, but turfgrass managers restrict the growth rate by supplying less nitrogen fertilizer than the grass can use. For example, I could apply 100 g/m2 of 10-10-10, and the grass would grow more rapidly than if I applied only 10 g/m2."

Someone in the audience disagreed with me. "You can't apply 100 g/m2 of 10-10-10," he said. "That much will burn the grass."

I wondered about that, so I went shopping for 10-10-10. I didn't find a 10-10-10 with suitable particle size. The closest analysis with a particle size suitable for application to turfgrass was 14-14-14. I bought a bag.

Then I marked out plots on a korai (Zoysia matrella) nursery. Each plot was 1 m by 1 m, and there were seven plots in total.

This is what the plots looked like right after the fertilizer application, before turning on the irrigation, on 31 July 2016.


I had measured out the 14-14-14 fertilizer and applied it to these plots. One plot received no fertilizer, and the other plots had 14-14-14 applied at rates from 2.5 up to 15 g N/m2 in 2.5 g increments (that's an N rate of 0 to 3 lbs N/1000 ft2 in 0.5 pound increments).

This is what the plot receiving the 15 g N/m2 rate looked like after I spread the fertilizer and before irrigation was applied.


I wanted to check three things with these fertilizer treatments.

First, I wanted to see if this much fast release fertilizer would burn the grass. In the seminar, I'd said that 100 g/m2 of 10-10-10 could be applied, but no one would do that, because it would make the grass grow too fast. In this test with 14-14-14, I included N rates all the way up to 15 g/m2, equivalent to 150 g/m2 of 10-10-10.

Second, I wondered what would happen with root growth at different rates of fertilizer.

Third, I wondered how long a color or growth response would last. For example, when the grass starts going dormant in the autumn, would the effects of a 31 July fertilizer application still be visible?

Before I applied the fertilizer, the roots were like this. These roots are from the plot receiving the highest rate of 14-14-14, before any fertilizer was applied.


The fertilizer was watered in and there was no burn. Maybe just a little bit where a few particles didn't dissolve completely, but the overall effect was to make the grass greener. A week after the fertilizer application, the plots looked like this. In the foreground is the plot with no 14-14-14 applied, and each plot after that received an increasing 2.5 g N/m2 increment of 14-14-14.


This plot received 15 g N/m2. A week after the application, it was greener than the surrounding grass that didn't receive fertilizer. If there was any burn, one might pick out a few leaves here. They didn't last long.


 I came back a month later and had a look at the plots on 30 August.


I also looked at the roots for each of the fertilizer treatments. I had expected that adding some 14-14-14 would cause an increase in roots. All the plots showed an increase in roots by 30 August compared to the roots I looked at on 31 July. But I don't see any increase in roots with fertilizer application. If anything, the root system was largest in the control plot that received no fertilizer.


The soil on this nursery is similar to the soil on the course. The nursery soil wasn't tested, but the course soil was, and in May 2016 the median pH was 6.4. Using the Mehlich 3 extractant, the mean K, P, Ca, and Mg were 59, 172, 1304, and 57 ppm.

All these elements were present at adequate amounts in the soil, so adding more K and P in the 14-14-14 didn't make the roots grow more. I had expected more N (up to a point) would cause an increase in root growth, but after one month, that's not apparent at all.

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.