Roots, growth potential, and fertilizer

Last month Bhupendra Singh shared this photo of roots on a Tifdwarf putting green in New Delhi.

 

Growing roots! Tifdwarf at Peacock Course Greens, Delhi Golf Club.

A photo posted by Bhupendra Singh (@bhupendra.golf) on

I wondered how the grass had been managed for the past six months.

Here's the high and low temperatures in New Delhi from November 1 until April 9. Delhi_temperatures
Those temperatures, converted to a C4 growth potential (GP), show that the GP was low in winter and approached a maximum as the temperatures warmed in the spring. Delhi_gp
So was there anything extraordinary done to develop roots like this?

Roots1
R4
Bhupendra informs me (and sent along those photos to confirm the results) that the N sources have been ammonium sulfate, urea, and potassium nitrate. The P source is single super phosphate, and the K has come from potassium nitrate and potassium sulfate.

The application rates have roughly tracked the GP.

In the winter, N was applied at an average of 0.5 g N m-2 mo-1. In February and March, as the grass came out of dormancy and the GP approached 1, the N rate has been 3 to 4 g N m-2 mo-1. The P and K are applied in proportion to the amount of N applied, in the approximate ratios used by the grass.

The mower bench setting was 4.25 mm in early April when the photo was taken, with a 3 mm prism reading on the ground.

These are new greens, planted in autumn 2015.

Even for new greens, those are still pretty impressive results. Sure, one doesn't putt on the roots -- what really matters is the surface. But these photos demonstrate that supplying the grass with the nutrients it can use, at the time when net photosynthesis is at its highest, and given water and air in the soil, roots are going to grow.


Is this the most common oxymoron in turf?

An oxymoron is a contradiction in terms, and the one about nutrients being present but not available, or exchangeable but not available, or adequate but limited, is one I hear again and again.

Paul Walsh recently posed this common question:

I've tried previously to answer this in a lot of different ways:

Let me answer in a slightly different way this time.

First, it's an oxymoron to think of availability in terms of present but not available, or adequate but of limited availability. A clearer way to think of this, and the way that I try to express it now, is enough or not enough. For any element, is there enough to meet the grass requirements, or is there not enough? The MLSN guidelines provide an answer to this question.

Second, have you noticed what is missing in all this discussion about the semantics of availability? Turf performance. That's what is missing. I want to know if there is enough, or not enough, in order to produced the desired turfgrass surface. Sure, the solubility of different elements changes with pH. Sure, the ion exchange characteristics of the soil change with pH. But only when turfgrass performance is affected do we need to worry about this. So I suggest going back to the first point: is there enough or not enough?

Here is grass performing just fine across a range of pH from 3.7 to 9.5. Yes, the soil chemistry changes across that pH range. Yes, there will be differences in nutrient solubility. But the grass is fine. You'll see that there is enough in each of these soils.

Soil pH from 3.7 to 7.4

Atc2009

Soil pH 6.5

K2014

Soil pH 7.8

Ascc07

Soil pH 8.3

Cu2003

Soil pH 9.5

Ne15


Grass selection by normal temperature and sunshine hours

Plotting the normal temperatures and sunshine hours for a location places that location in a particular 2-dimensional space. I demonstrated that in these charts. @turfstuf suggested that a diagonal line might show a break point for classifying warm and cool-season grasses.

The idea is that the top right would be warm-season, the area around the line would be transition zone, and the area to the bottom left would be cool season. That chart looks like this.

Diagonal

I agree that different regions of the chart are indicative of over/under points for different grasses or growing conditions. I wouldn't separate by that diagonal line. Here's the break points I would use.

  • mean annual temperature less than 15°C, cool-season
  • mean annual temperature from 15 to 20°C, transition zone
  • mean annual temperature above 20°, warm-season

For those general breaks, one can estimate the annual mean from the monthly charts, or plot the locations by the mean annual temperature.

annual_cities_temperature_sunshine

Continuing with the breaks, specifically looking at which warm-season grasses will be suitable:

  • within warm-season, and more than 6 hours sunshine per day, bermudagrass
  • within warm-season, and less than 6 hours sunshine per day, zoysiagrass or other warm season grasses that are tolerant of low light conditions: bermudagrass will struggle
  • within transition zone, and less than 6 hours sunshine per day, if warm-season grasses are used, zoysiagrass or other warm season grasses that are tolerant of low light conditions: bermudagrass will struggle

A transition zone location like Atlanta looks like this when those points are marked on the plot.

Atlanta

Two warm-season locations, one where bermuda thrives (Honolulu) and another where bermuda is overgrown by more shade tolerant grasses (Hilo), are shown here.

Hnl_hilo

In the next plots I show some other locations: cool-season, warm-season, and transition zone. The break points I use seem to agree pretty well with grass distribution and performance around the world.

Bangkok_boston_dubai

Indianapolis_tokyo

Sydney

Cairns_hk_syd

Knoxville_tokyo


Visualizing climate differences

Of the factors that influence plant growth, turfgrass managers are able to modify in some way the plant water status and the nitrogen supply to the grass, but they can do little to adjust the temperature and the light. As a consequence, both the grass adaptation to a particular environment, and the management requirements for the grass, will be influenced or controlled by the combination of light and temperature.

I spoke about this at a conference in 2012 and shared this handout. From the start of the handout:

The weather, and specifically the temperature and the amount of sunshine, has a major influence on the growth of grass and therefore on the suitability of certain grasses for certain climates. By plotting the climatological normal weather data with temperature on the horizontal axis (x-axis) and sunshine hours on the vertical axis (y-axis), we can see which locations are similar in these parameters, and thus likely to be suitable for the same grasses, and to similar maintenance practices for grasses. Many locations in East, South, and Southeast Asia are distinguished by relatively low sunshine duration as compared with locations of similar temperature in North America, Oceania, Africa, and Europe. For additional information about the use of these charts, see www.climate.asianturfgrass.com.

The idea is that when temperature and sunshine are the same (or similar) at two or more locations, the growing conditions, and the energy available for grass growth, are the same (or similar). When the temperature and sunshine are different, with no overlap, then the growing conditions are clearly different.

I think this is interesting and informative because such an approach can help to identify places that we might think are similar, but are in fact different, and vice versa. The implications for maintenance requirements, grass selection, and location to location comparisons are also evident from such representations of climate data. I've made some more plots to illustrate this.

MiamiI start with Miami. The normal monthly mean temperature is shown on the x-axis and the mean daily sunshine hours for that month are shown on the y-axis. The polygon defined by each of the 12 months of the year expresses what the normal growing environment is like at Miami. Places that are similar to Miami should have overlap in light and temperature with Miami. Places that are different should have little or no overlap.

Moscow, for example, has no overlap with Miami. I don't think anyone would expect it to.

Miami_moscow

The hottest months of the year at Moscow are cooler (with more sunshine) than the coldest months at Miami. There is no overlap between these locations.

New York City has some overlap with both Miami and Moscow. If I plot New York on this chart, I can see which months at New York are similar to Miami or Moscow.
Miami_moscow_newYorkJune in New York is similar in temperature and sunshine to March in Miami, September in New York is almost the same as January in Miami, and July and August in New York are between March and October conditions in Miami. One can also see the seasonal overlap between New York and Moscow conditions.

How about another warm season location like Miami? This plot adds Singapore conditions.

3plusSingapore

There is no overlap between Singapore and Miami, even though both are warm-season locations. There is more overlap between New York and Miami (about 3 months) than there is between Singapore and Miami (0 months). This has implications for grass selection and management. That is, the grasses the work in Singapore may not do very well in Miami, and vice versa.

Some places are predictably similar. Portland, and Seattle, for example, have almost complete overlap.

Portland_seattle

Other locations that one might expect to be similar have no overlap at all. I often use Honolulu and Hilo as an example. And sure enough, one finds different grass species growing at these two locations.

Hilo_honolulu

This video discusses Hilo and Honolulu.

One can also look at transition zone locations, like Atlanta, where both warm and cool-season grasses are grown.

AtlantaHow does a cool-season location like London compare to Atlanta?

Atl_lonJune and July in London are similar to March and November in Atlanta. July and August in London have similar temperatures as October in Atlanta, but with less sunshine.

Melbourne is another transition zone location, with golf course fairways and sports fields usually planted to warm-season grasses, and golf course putting greens usually planted to cool-season grasses.

Atl_lon_melMelbourne has some overlap with both Atlanta and London.

 


Monthly Turfgrass Roundup: March 2016

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

Handout and slides, Beijing and Bangkok, playability and sustainability.

China Golf Show report.

Weed ID tool with lots of photos.

Video report from the R&A sustainability seminar.

Jason Haines with these presentation slides on 4 years of MLSN.

Driving range before and after:

Dave Wilber and Jason Haines had a fascinating conversation.

5 things I don't do anymore, and why, also by Jason Haines.

Presentations and photos from Thailand conference.

Maximum photosynthetically active radiation (PAR) in summer.

A rule of thumb for the cloud effect on PAR.

Presentation slides and animated PAR charts for every day of the year.

Optimum playing conditions with minimum inputs.

This new book: A Short Grammar of Greenkeeping.

Three years of potassium and more snow mold.

Soil tests should result in less fertilizer application, not more.

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.


That's not the way it is supposed to work

Of the many interesting things in the report by Gelernter et al. on the GCSAA's second nutrient use survey, I was especially intrigued by the part about soil testing.

First, a little background. If one has no idea how much of any mineral element is in the soil, then the logical amount to apply as fertilizer is just a little bit more than the grass can use. This guarantees that the grass will be supplied with all of each element that it can use. That's like an estimate of the maximum amount of fertilizer to apply.

Why soil test? Because soil testing allows for more efficient application of fertilizer. After finding out how much is in the soil, one can often reduce the quantity of fertilizer applied, because one knows that the soil can supply some portion of the plant's requirements.

With no soil testing, it makes sense to apply all that the grass could use. With soil testing, it makes sense to apply only the amount that the grass could use that can't be supplied by the soil. It's evident that the maximum amount of fertilizer should be applied when one doesn't know the nutrient content of the soil, and that in the most infertile soils, the quantity of nutrients required as fertilizer will be close to the maximum, with the quanity required as fertilizer decreasing as soon as the soils have some quantity of nutrients.

In the last chapter of A Short Grammar of Greenkeeping, I wrote that "I'd recommend soil testing, because in most soils the correct interpretation of soil tests can reduce the quantity of fertilizer that is applied."

You may have heard me say that soil testing is broken. For more background:

Now back to the GCSAA nutrient use survey results. Here's what the survey says:

"Despite the fact that respondents said that they used soil tests to reduce reliance on fertilizers, higher use rates were observed for respondents who conducted soil tests (Table 7). This apparent contradiction may be due to some of the turf fertility guidelines currently in use, which target higher nutrient levels than are required for acceptable turf growth ...

As a result, those who conduct soil tests with the belief that it will help them to reduce fertilizer inputs may end up unintentionally increasing fertilizer instead, likely because the guidelines used to evaluate their results may be higher than necessary."

That's not the way it is supposed to work. For more, check out the fertilizer and soil categories on the blog.


Every spring when the snow melts ...

I look forward to some photos from Doug Soldat. For the past three years, he's had some fascinating photos to share of snow mold on creeping bentgrass. And each year, there was more snow mold where potassium fertilizer was applied, and less snow mold where potassium wasn't applied.

Spring of 2014

In the spring of 2014, there was more snow mold where K was applied.

Spring of 2015

Last year, there was also more snow mold where K was applied.

Spring of 2016

This year, it happened again. There was more snow mold where K was applied.

Doug will be talking about K in a TurfNet webinar in April: Is Your Potassium Program Hurting or Helping Your Turf?


On those creeping bentgrass plots in Wisconsin, adding K increases snow mold. No K had less snow mold.

At Rutgers, annual bluegrass plots deficient in K have had more anthracnose in summer and more winter injury. Eliminating the deficiency reduced those problems.

Then there is the MLSN guideline for K of 37 ppm. I recommend keeping the soil K above 37 ppm (Mehlich 3 extractant).

And there are hundreds of other studies about K. Some show a benefit from adding K, and some don't. I haven't read all of them, but I have read a lot of them. This sounds like it could be pretty complicated.

Actually, I don't think it is. Here's what seems to be the case, for both warm-season and cool-season grasses:

Ensuring the grass is supplied with all the K it can use will provide all the benefits associated with K. Adding more than that usually has no effect, other than wasting time and money, but sometimes has a negative effect.

As a turfgrass manager, all one has to do is ensure the grass is supplied with all the K it can use. This can be accomplished in 2 ways. One is by keeping the soil K above the MLSN guideline. A second is by applying N:K in a 2:1 ratio for cool-season grasses, a 1:1 ratio for seashore paspalum, and a 3:2 ratio for other warm-season grasses. I wrote about that in the final chapter of A Short Grammar of Greenkeeping and in The (New) Fundamentals of Turfgrass Nutrition.

Note that I do not recommend tissue testing for K (or any other element).

If you want to read more about K specifically, and about how the benefits of K come from correcting a deficiency, I recommend: