Soil test interpretation and more: 4 seminars in Australia

I was in Sydney, Adelaide, and Brisbane this week to discuss the MLSN approach to soil test interpretation in four seminars organized by Living Turf.

In these seminars, I explained that the use of the MLSN guidelines is as simple as planning how many beers to buy for an upcoming party. And at this party, I want to ensure that I don't run out of beer to serve my friends.


This is a quick summary.

1: Soil test calibration involves establishing different levels of nutrients in the soil, growing a grass in those soils, and then evaluating the grass response to different levels of that nutrient. It quickly becomes apparent that these calibrations will be specific to the soil type, grass variety, and climate in which the calibration is done. Doug Soldat called these tests "expensive and time consuming." On a global scale, the word I use to describe this is impossible.

2: Because doing such extensive calibration is impossible, the conventional turfgrass guidelines were developed by adjusting the ranges from agricultural crops and soils:

"Traditionally, ranges for various nutrients are based on the past 60 years of fertility studies, particularly on forages, agronomic and horticultural crops, with adjustments made to fit perennial turfgrasses based on studies and the judgment of experienced university turfgrass scientists."

In addition to that, the conventional guidelines have in some cases been set deliberately high. That's not because grass performance would be improved by more nutrients, but because "the cost of fertilization was not considered of primary importance for turf." And that quote is right from the textbook.

3: The minimum levels for sustainable nutrition (MLSN) guidelines for interpreting soil tests take a different approach by focusing on the way turf is managed in the modern era, and considering grasses and the soil conditions used for high performance turfgrass today.

4: Use of the MLSN approach involves making an estimate of 100% of the nutrients that the grass can use, and increasing that by an additional amount to keep as reserve in the soil. One then compares the sum of the use estimate and the reserve quantity to the amount actually present, and the result of that comparison is the minimum fertilizer recommendation.

You can scroll through the slides below, or view or download them here.

After my seminar about MLSN, Daryl Sellar showed a demonstration of the TurfKeeper system. One can read about it at the website, and how it "becomes the home of all turf management planning, actions, and facility history." It starts with a job board and goes from there, with the tasks, costs, product usage, and application records all linked in a way that impresses me every time I see how TurfKeeper is used. I was recently listening to a podcast about turfgrass innovation. Dave Wilber and Kevin Hicks discussed the direction of the industry, and Kevin mentioned that there is a lot of data out there, and a lot of systems that do a good job of handling one aspect of the data. TurfKeeper puts it all together in a way that few others do.


The MLSN approach is suitable for any grass, soil, and use, because it involves both a site specific estimate of nutrient use plus a reserve amount to keep in the soil. I enjoyed seeing a range of turfgrass sites and grasses on this trip, and discussing with so many turfgrass managers the practical use of MLSN to interpret soil tests in those conditions.


That's kikuyugrass at Eagle Farm race course in Brisbane. For a good story about something that happened at Eagle Farm in 1984, read about Fine Cotton.


This is Legend green couch (bermudagrass) overseeded with perennial ryegrass at Suncorp Stadium.

Applying the grammar of greenkeeping

Over the past two weeks, I've had multiple conversations about the way I think of turfgrass management. It all starts with a definition of greenkeeping as managing the growth rate of the grass. I wrote about this in A Short Grammar of Greenkeeping. You can get your copy here.

Application of the grammar allows for easy communication among turfgrass managers about the work they are doing. I'll use the creeping bentgrass greens at Hazeltine National GC as an example. Volunteers from near and far were at Hazeltine during the Ryder Cup.

Let's say that I was from Madrid, or San Francisco, or Sydney, and I wanted to get green conditions that were more like those at Hazeltine. One of the ways I would try to do that would be to apply a similar quantity of nitrogen. But how to compare locations?

I would use the temperature-based growth potential (GP). For Minneapolis, the GP looks like this.


If I set the maximum monthly N at 3 g/m2, and multiply by the GP, I get a maximum annual N of 13.3 g/m2 for that location (Minneapolis). Now I'll make up a number, because I don't know exactly what it is, but let's say the actual quantity of N applied at Hazeltine was 9 g/m2.

I'll use the log percentage (L%) difference for consistency. The L% is the natural logarithm of the ratio of two numbers, multiplied by 100:

If 9 g N were applied at Hazeltine, and the value calculated using GP as described above is 13.3 g, that is a 39 L% reduction.

If I want to apply proportionally the same amount of N at another location, I can calculate the GP amount, which I'll call a standard value, and then take a 39 L% reduction.


The standard using these calculations comes to 16.7 g at Madrid, 20.1 at San Francisco, and 28.9 at Sydney. Knowing that there was a 39 L% reduction at Hazeltine, my starting point for Madrid, after applying the same reduction, would be 11.3 g N/m2. At San Francisco, the N would go from the standard calculation of 20.1 down to 13.6 g, and at Sydney the 39 L% reduction takes N from 28.9 to 19.6.

This grammar facilitates the rapid sharing of relative inputs used to produce turf surfaces all over the world. Let's say we know there are amazing bentgrass greens in Sydney with N inputs of 10 g/m2/year. A corresponding quantity of N in Minneapolis would be 4.6 g.

This same approach can be applied for the quantity of water supplied in comparison to evapotranspiration (ET), to frequency of mowing, to evaluation of the growth rate, to assessment of the photosynthetic light, and so on. I find this approach quite useful in rapid implementation of maintenance practices that work well at location A, applied to location B. One then has a site specific starting point that can be further adjusted at location B, based on turfgrass response at that location.

Turfgrass and shade: daily light integral (DLI) in Sydney

The Australian Government Bureau of Meteorology (BOM) provide satellite-derived global solar radiation data. I downloaded 2015 and 2016 data for station number 66120 (Gordon Golf Club). The data are in energy units of megajoules per square meter per day. I multiplied by 2.04 to convert to daily light integral (DLI) units of moles per square meter per day.


This is the DLI in full sun, adjusted for clouds. Any tree or structural shade will result in a lower DLI.

Looking at monthly summaries of DLI, one can see the median and the normal range in each month since January 2015.



I downloaded temperature data from the Sydney Airport (SYD) and used those to calculate an estimated DLI using the Hargreaves equation, as described in Estimating daily light integral in 4 Tennessee cities. I did not make any corrections to the estimate from the Hargreaves equation, and SYD is about 25 km south of Gordon. Still, the uncorrected Hargreaves equation gives a decent estimate of DLI.


GP and GDD: are they comparable?

Someone asked me at the Northern Green Expo if the temperature-based growth potential (GP) and temperature-based growing degree days (GDD) are comparable. They sort of are, with a couple of exceptions. Comparable, yeah, kind of. But they are not interchangeable.

I downloaded the weather data for every day in 2015 from the international airports at London (Heathrow), Minneapolis, Sydney, and Tokyo (Haneda). Then I calculated the GP, and the GDD, and I made the charts shown in this post. The script to download the data and produce the charts is here . I’ll try to explain this, but I think it is easiest to see how GP and GDD are similar, and how they differ, by making some comparisons yourself.

First, here are the mean daily temperatures in 2015. The points are daily mean temperatures for each day of the year, and the lines are a moving average. Sydney and Tokyo are both hot in the summer, Minneapolis is coldest in the winter and hotter than London in the summer, and London is coolest in the summer but has winter temperatures close to those of Tokyo.

4 cities, temperature in 2015

Those cities have fairly diverse temperature ranges and variation in temperature from winter to summer. One expects a different growing environment in each. The GP3 is a value with a minimum of 0 and a maximum of 1, showing the expected limitation (or potential) of temperature on growth.

4 cities, GP in 2015

What do we see there? It’s a bit different than the temperature. Looking at the moving average for each city, we see Tokyo has a big drop in mid-summer because it is too hot, and Sydney has a substantial drop too, and Minneapolis has a slight drop in GP during the hottest summer temperatures, and London has peak GP in mid-summer because the average temperature rarely exceeds the optimum growth temperatures.

In the winter, the GP3 drops to almost 0 at Minneapolis, London, and Tokyo, but at Sydney it drops just below 0.5 in mid-winter, indicating that C3 grasses should still be able to grow, albeit slowly.

That was GP through the year. Now we can look at GDD0 . That is, for each day with an average temperature above 0°C (32°F), I take that temperature and call that the GDD. In this case, I would be using a base temperature of 0°C. This is the basis for the growing degree day model of Kreuser for the reapplication of plant growth regulators.

4 cities, GDD0 in 2015

That’s not exactly like the GP plot above. It is like zooming in on the temperature chart, but only showing the portion of the chart with temperatures above 0°C. Compared to the GP chart, one notices that with GDD 0 there is no drop in mid-summer when it is too hot, and the GDD0 does not drop all the way to 0 in winter at Tokyo and London.

So far it seems the GP and GDD are sort of the same, and sort of different. Both are based on temperature. But GDD is a measure of heat accumulation. GP is generating a value with a minimum of 0 when temperatures are far from an optimum for photosynthesis, and then generating a value that gets closer to 1 as the temperatures get progressively closer to 1.

There are various ways to calculate the heat accumulation through growing degree days. The GDD10 only counts the degrees on those days when the average temperature is above 10°C (50°F). This is GDD with a base temperature of 10°C. That makes sense for some things, and this chart of GDD10 is similar to the GDD0 chart in that it is as if the temperature chart were cropped to omit all values less than 10°C.

4 cities, GDD10 in 2015

That’s what GDD10 is showing. Now we are looking only at the days in the year when the average temperature was above 10°C, and we can see how much heat accumulation there would be each day.

The big difference between GP and GDD is evident, because Sydney and Tokyo are peaking in GDD when temperatures are at their hottest, but GP would produce a value less than 1 when GDD was highest in those places, because the temperatures are considered too hot for optimum growth of C3 grass.

GDD is heat accumulation. GP is optimum growth temperature accumulation. Let’s look at the accumulation explicitly, by adding together the GP for every day of the year in order to get these lines showing the cumulative sum of GP in 2015.

4 cities, cumulative sum of GP in 2015

Sydney with the year-round growth, although with a dip in winter and also a dip in summer, has the highest sum of GP. Then Tokyo, and Minneapolis and London are similar.

We can do the same type of chart for GDD0 .

4 cities, cumulative sum of GDD0 in 2015

Sydney still has the highest sum, then Tokyo, but there is less of a distance between these two cities than with GP, because GDD is using all of Tokyo’s hot summer days, but GP in Tokyo drops when it is hot. London and Minneapolis are similar again, but notice that Minneapolis accumulates almost all its GDD0 from April to October, while the milder winter in London allows the GDD0 to accumulate slowly year-round.

The cumulative sum of GDD50 is just a little different.

4 cities,cumulative sum of GDD10 in 2015

Now Tokyo catches and exceeds Sydney in the northern hemisphere autumn, but Sydney catches up quickly as summer approaches. And when counting the heat accumulation now only above 10°C, Minneapolis now has a lot more of that than does London.

The GP and GDD with various base temperatures (0 and 10°C are two of the standard ones) can be used for different things. GDD is good for things that are heat dependent. Growth regulators, insects, diseases, weeds – certainly the growth of certain plants in the range of temperatures from the minimum temperature required for growth up to the optimum temperature for growth. The GP is formulated in a different way, where it decreases when it is too cold or too hot for optimum growth.

We can look at that a little more closely. Now let’s just look at 2 cities to reduce the overlap: London and Minneapolis. Here is the GDD0 for every day of 2015.

2 cities, temperature vs GDD0 2015

That’s a linear increase in GDD0 for every increase in temperature above 0°C. If we would plot GDD10 , there would also be a linear increase with temperature, but the line would start going up at a mean daily temperature of 10 rather than at 0.

The GP for that same range of temperatures at London and Minneapolis looks completely different.

4 cities, temperature vs GP in 2015

That’s because the GP has a minimum of 1 and a maximum of 0, and the value is dependent on how close the temperature is to the optimum temperatures for photosynthesis.

Now to get back to the original question, after all those examples, are GDD and GP comparable? For them to be comparable, there would have to be a linear relationship (or almost linear relationship) between the accumulated GP and the accumulated GDD through the year.

Here I’ve plotted just that; the cumulative sum of GP for 2015 is on the x-axis, and the cumulative sum of GDD0 is on the y-axis.

4 cities, gp vs GDD0 in 2015

Well, that is sort of linear, but has a few weird curves or shifts. London’s GDD goes up in winter when the GP is still low, and Tokyo’s GDD goes way up in mid-summer when the accumulated GP is increasing slowly. And the line for Sydney looks pretty straight by comparison, but we can take a closer look at that by checking GP vs GDD10 .

4 cities, gp vs GDD10 2015

Now we are looking at how GP accumulates through the year, and comparing that to how GDD10 accumulates. At some times of the year it is linear, but as temperatures get low or high, the slope of that line changes.

If you would make these calculations for data at your location, I think you would see the same thing and would see how GP and GDD are similar and how they are different.

For more information, see:

Is it getting more difficult to grow bentgrass in Sydney?

Yes. Since 1859, the temperatures have increased, the temperature in the hottest month of the year (January) has increased, and the number of days each year with a low temperature above 21°C (70°F) has increased.

Although the temperature at Sydney during much of the year is fine for creeping bentgrass, the amount of heat stress on the grass in the summer seems to be, in an average summer of today, more than in an average summer many years ago.

All these data are from the Australian BOM Observatory Hill station in Sydney, and code to make the charts is here.

First, the average annual temperatures since 1859.

Then, a look at the trend in average temperatures during the hottest month of summer -- January. 

What really puts stress on creeping bentgrass is high nighttime temperatures, and this can be assessed by counting the days with a low temperature higher than 21°C. Since about 1960, the trend is to have more days with low temperatures above 21°C.

I've also made charts with the same data on Plotly. These interactive charts use the D3.js library and the values are displayed on the screen when one hovers the cursor over that point on the chart.

Sydney mean Annual Temperatures on Plotly:

Sydney mean January temperatures on Plotly:

Sydney duration of summer heat stress -- days with low temperature above 21°C:


Turfgrass Growth Potential: 4 cities, 472 days

The temperature-based turfgrass growth potential predicts how grass growth can respond to temperature. This growth potential (GP) was developed by PACE Turf and has been put to many uses such as predicting the time of overseeding, estimating turfgrass nitrogen requirements, assessing turfgrass stress, and evaluating growth and optimum times for various maintenance practices.

When the temperatures are not conducive to growth for cool-season (C3) or warm-season (C4) grasses, there isn't much one can do to force the growth. Extra nitrogen can be added, but it really doesn't have its full effect until the temperatures get into an optimum range for growth.

After reading that tweet, I looked up the temperature data for Tulsa. Sure enough, the growth potential for C3 grass has been pretty low for the past month. Growth potential of C3 and C4 grasses at Tulsa are plotted in this chart, with data included for the past 472 days, from 1 January 2013 until 17 April 2014.

I find the growth potential useful in a lot of ways. On golf courses with both cool and warm season grass, as at Bristol Hill Golf Club near Kisarazu, it can be useful to study the growth potential when planning maintenance activities.

Cool season (C3) and warm season (C4) grass at Bristol Hill Golf Club near Kisarazu, Japan

The GP at Kisarazu for the past 472 days is show below. Ideally, disruptive maintenance practices such as scarifying or core aerification will be done at times when the turf has a high growth potential. This allows for a rapid recovery time and minimizes disruption to play.

I looked up the data for a couple other cites. At Sydney (data from the Sydney Airport), the temperatures are milder.

At Dubai, the temperatures are more extreme.

 With all the variation in temperature from place to place, there is also a big difference in the way grass will respond. The growth potential puts a numerical value to this. This can then be used in maintenance planning, in useful comparisons to other locations, or in explanations of why the turf is responding in a certain way.

Webcast from the Australian Turfgrass Conference: Micah Woods on grass selection in Asia

The Australian Golf Course Superintendents' Association provide a great service by making so many of the presentations from their annual conference available for online viewing

At the 27th Australian Turfgrass Conference in Adelaide, I gave presentations about turfgrass nutrient requirements, about grass selection, and about managing turf in microclimates.

In this presentation about grass selection, I spoke primarily about manilagrass (Zoysia matrella), bermudagrass or green couch (Cynodon), and seashore paspalum (Paspalum vaginatum). You'll see in this video representative photos of the different grasses and will learn how manilagrass persists and provides a fine turf even with minimal maintenance. In Southeast Asia, seashore paspalum tends to be overtaken by bermudagrass, you will see, and bermudagrass tends to be overtaken by manilagrass. This short video makes a case for using more manilagrass and less bermudagrass and seashore paspalum.

Webcast from the Australian Turfgrass Conference: Micah Woods on turfgrass nutrient requirements

The Australian Golf Course Superintendents' Association provide a great service by making so many of the presentations from their annual conference available for online viewing

At the 27th Australian Turfgrass Conference in Adelaide, I gave presentations about turfgrass nutrient requirements, about grass selection, and about managing turf in microclimates.

The presentation on nutrient requirements highlighted the importance of nitrogen in comparison to the other mineral elements and explained why, when we think about fertilizer, the most important thing for a turfgrass manager to consider is how much nitrogen is being supplied to the plant. The topics discussed in this presentation are related to the MLSN guidelines developed with PACE Turf and are similar to those explained in the seven page handout Understanding Turfgrass Nutrient Requirements.

Webcast from the Australian Turfgrass Conference: Micah Woods on modifying the turf growing environment


The Australian Golf Course Superintendents' Association provide a great service by making so many of the presentations from their annual conference available for online viewing

At the 27th Australian Turfgrass Conference in Adelaide, I gave presentations about turfgrass nutrient requirements, about grass selection, and about managing turf in microclimates.

The talk on microclimates is really about turfgrass management in general, explaining how turfgrass managers can think about the growing environment of the grass and modify the environment to create the desired playing conditions. Watch the video at this link – my presentation starts at the 24:15 mark so to skip ahead to that just move the slider up to that time.