- 2 similar approaches to fertilization, with 1 notable difference
- "As clear as mud"
- Silica and green speed
- "Beware! These topics are misleading and irrelevant"
- Two short articles on simplifying fertilization and soil test interpretation
- How windy was it in St. Andrews yesterday?
- "No more than one third of the total leaf surface ...
- Course maintenance photos from the U.S. Open at Chambers Bay
- Concerning the availability of nutrients in soil
- Seminar questions: availability (again) and foliar applications in the context of soil guidelines
Tomorrow I'll list the 10 posts with the highest pageviews this year. Today, a quick summary of all 139 posts so far in 2015, and my pick of 10 posts that I really liked but that did not make the top 10 in pageviews. Here's a histogram of pageviews for all posts in 2015.
This violin plot shows all the posts.
- Maximum wind speed for each of the past 609 July days at RAF Leuchars, which shows just how extraordinary the wind speed was when play was suspended at the Open Championship.
- Morning and afternoon shade with PPFD capped at 1000, exploring the question of whether morning shade or afternoon shade is more detrimental to cool-season grasses.
- What happened on January 16, or how to increase interest on what you tweet by more than 75%: everyone is busy, no need to clutter their feeds with junk. Getting rid of automated tweets can increase interaction too!
- This is what PAR looks like, showing photosynthetically active radiation (PAR) every 5 minutes on a cloudy day, sunny day, a week, a month, and a year.
- Tournament week clipping volume, in which I make a case for routine measurement of clipping volume as being a useful piece of information.
- A chart of PPFD at two locations this year from January 1 through last Friday, the most intricate chart I made this year.
- Estimating turfgrass nutrient use, which is pretty much the key thing to estimate when determining how much fertilizer to apply.
- Surprises, conservatism, and what one can learn from soil testing, part 1: describes an experiment in Thailand, and some extended thoughts on these topics.
- Nonsense, facts that aren't facts, and turf in 3 dimensions: I even got blocked by Steve Keating's account after this post!
- Botanizing in Bangkok, about tropical grasses and the largest grass collection I've seen in Southeast Asia.
A similar report on the pageviews from 2014 is available here.
Bill Kreuser asked how many in the turf industry are on Twitter. As of this week, it seems to be a little more than 20,000.
John Kaminski suggested looking at accounts with a large number of followers to get an estimate, which is about 9,000.
I pointed out that one should not just count followers, but should make sure they are from real accounts. Some fake accounts have distinctive characteristics in the follower to following ratio, and in the number of tweets, that make them easy to identify.
John Smart suggested that we should look at The IOG and BIGGA accounts too, rather than only US-based accounts.
Taking this approach, one would want to find all the followers of the accounts of interest, and then count the unique followers, since one account may follow for example both @TurfDiseases and @BIGGALtd.
Using the twitteR package in R, I got the follower lists for these accounts:
I then combined the lists, which gives a total (as of follower lists obtained during the week of Nov 30) of 48,203 followers. But they are not unique, because many followers follow more than one of the above accounts. Selecting only the unique followers, one finds this:
- There are 23,928 unique accounts following those turf accounts listed above.
- There are 22,595 unique accounts that have sent out at least one tweet. That is, their statuses count is > 0.
A lot of those accounts will be rarely used or inactive, but to make those estimates, I leave as an exercise to the reader.
The code I used is here. You'll need to put in your own API key and access token information.
I've been writing many of the posts on this site in advance, and scheduling them to post at a later date. I would like as many people to see these as possible. So which days, recently, get the most visitors? In the chart above, I show a boxplot for each day of the week, with the y-axis showing the total daily visits to the site from 1 January 2014 to 2 May 2015.
These are days as they are in Bangkok -- a visit from New York at 17:00 on a Sunday registers as Monday -- and the range for any day is wide, from a minimum of about 25 visits to a maximum of more than 300. Monday gets the most, then Tuesday, then Thursday, then Wednesday. The days with the fewest visits are Friday, then Saturday, then Sunday. I'm going to adjust the schedule for new posts based on these data.
You may have noticed a few changes in the layout of this site. I made the changes this week to improve the content display across all devices. When I started this blog in 2009, more than 99% of the visits were made from a computer with a browser, coded as "Desktop" in this chart.
That has changed a lot over the past six years, and in the 12 months ending today, less than 50% of the visits are made from a computer. And Google searches from mobile devices are now using mobile-friendliness as a ranking signal.
It was well past time for this site to be updated. I hope you like it.
In response to many questions about these data, I have made a few more charts (11 actually) and have included some explanatory text and links in this new document: twitter follower analysis.
Other documents I have made using R Markdown include:
- Putting green nutrient use and requirements (GCSAA webcast handout)
- Minimum nutrient requirements, Ca & K, and Park Grass (Eastern Pennsylvania Turfgrass Conference handout)
- Monthly turfgrass roundups
Have you ever wondered about Twitter followers and which accounts stand out? Or how the #GIS14_Turfbowl tweet rally was counted? I think it is interesting to look at some of these things, and with the Twitter API and streaming API, it is possible to obtain and study a tremendous amount of data.
I've been looking into this in my spare time this week, trying to explore some of the available data, and I made these four charts that show a few things about the accounts that follow @asianturfgrass; these data were obtained using the ScraperWiki Twitter tools, and the charts were made using ggplot2 in R.
As of 15 February 2014, there were 1350 accounts following @asianturfgrass, and the greatest number were created in 2011. Incidentally, the @asianturfgrass account was started on 1 January 2011. For the most part, we can expect that twitter accounts following @asianturfgrass will be interested in research, advice, and teaching about turfgrass -- in short, interested in turfgrass information.
I had thought that there may be an increase in accounts opened in 2012 and 2013 from 2011, as it seems that more turfgrass managers and turf companies are using Twitter. However, for my account, the plurality of follower accounts were created in 2011.
Of the accounts following me, I looked at their follower to following ratio. That, I thought, might give some indication of just how influential or famous that person or company is.
The chart above shows the 15 accounts following me with the highest follower:following ratio as of today. And it is certainly a who's who of the turfgrass industry, from tournament courses to turfgrass researchers to famous consultants and superintendents.
For the two charts below, you will want to click on the image to see at a larger size. They both show the same thing, the number of tweets from an account on the x-axis, and the number of followers of that account on the y-axis. The first one shows all of the accounts following @asianturfgrass as of today, and the second one shows only those with less than 20000 tweets and less than 7500 followers. That is, it zooms in on the data.
We can see that the vast majority of accounts are bunched in the region with less than 2500 tweets and with less than 1000 followers. The accounts that stand out, then, must be exceptional in some way. For an account to stand out from the pack, using this scatterplot, either the account sends a lot of tweets, has a relatively large number of followers, or both.
For an interesting look at the entire scope of Twitter accounts, see Tweets Loud and Quiet by Jon Bruner.
On March 28 (or March 29 in Asia), Episode 23 of the Turf Diseases Turf Chat will be on the subject of Soil Nutrients and Weed Management. This promises to be an interesting discussion, led by Dr. Scott McElroy from Auburn University and hosted by Dr. Larry Stowell of PACE Turf.
It is a well-known phenomenon that application of nitrogen favors grasses and reduces species diversity (i.e. reduces the number of weedy species), while increasing the soil pH through addition of lime, and adding potassium fertilizers, as an example, can increase the prevalence of weeds. In a brief exchange on twitter last week, we discussed this, and decided to make this subject the focus of the upcoming Turf Chat.
Dr. McElroy says that this phenomenon is already known, while I would argue that despite it being noticed more than 150 years ago, and the mechanisms of this worked out more recently, among turfgrass managers, there is not universal knowledge of these principles.
That was the subject of an article I wrote with Dr. Frank Rossi from Cornell University about the Park Grass Experiment and some of the results from this classic experiment, especially the noted absence of dandelions from plots to which potassium fertilizer is withheld.
For more detail about this, please read our article about Park Grass from the Green Section Record, and you can find a list of references at the end of that article for additional reading. Of particular interest may be this one, by Silvertown et al., The Park Grass Experiment 1856-2006: its contribution to ecology.
I've made a new moving bubble chart, this one focusing not exclusively on warm-season areas, but also including a wide range of cities where cool-season grasses are grown: Beijing, Berlin, Boston, Budapest, Chicago ... Madison, Minneapolis, Moscow ... Topeka, Vancouver, Washington, D.C.. In total, 52 cities, including a few of my favorite warm-season and transition-zone cities such as Atlanta, Bangkok, Hong Kong, Rio de Janeiro, Shanghai, Singapore, and Tokyo.
I've also written this short document that explains, perhaps in excruciating detail, exactly how I've gathered the data and produced this particular chart. I've included links to the data sources and software I use and provided formatted data files and the few lines of R code necessary to make the chart yourself. You may find it interesting to create charts in this format but with different data that changes with time. Perhaps it would be data from your on-site weather station? Perhaps soil test data that change with time, or labor hours to do different jobs on the course at different times of the year, or budget amounts over time, for different line items?
Click here to go to the climate.asianturfgrass.com page to see the chart at a large size, or adjust the chart at a small size here. Almost every aspect of the chart can be adjusted to find the view of the data that is most useful for you.