PhotosynQ at Feed the Future Legume Innovation Lab (LIL) Conference in Burkina Faso August 13-18, 2017

Following the PhotosynQ Workshop (see Dan’s post), we had moved to the LIL conference site at Laico Ouaga 2000, a high security hotel/conference venue outside of Ouagadougou city.  “Feed the Future” is a program funded by USAID under the US government’s Global Hunger and Food Security Initiative.  This program has been engaging many universities, institutions and private organizations in the US, Africa and Central/South America to improve the quality and management of legume, and contributing to the well-beings of local people. Michigan State University (http://legumelab.msu.edu/) is one of the leading institutions contributing researches and new technologies to the world.

One of the designated official languages being French, we had a simultaneous translation through headphone at this conference. The last time when I had to use French in daily basis was almost 20 years ago. Listening to the scientific talks was manageable, but my speaking ability was quite embarrassing. Another challenge was internet connectivity. As Dan mentioned, we had to manage the workshop with almost no internet connection. We were hoping to have a better connection at this best hotel in Burkina Faso, but unfortunately, it seemed the system could not handle a large traffic at once. The conference participants expressed that they had never experienced this in the past anywhere in Africa. It seems it was an isolated incidence, but we came up with some better solutions for the future.

  PhotosynQ booth (From right: Dan, Frank and Atsuko)

 Presentation by Dr. Irvin Widders, Director of Legume Innovation Lab, MSU. PhotosynQ was mentioned as one of the highlights of the ‘Feed the Future’ program.

At the last LIL conference held at Livingston, Zambia, Dave Kramer and Dan TerAvest presented the PhotosynQ project using MultispeQ Beta. This year in Burkina Faso, not only the people from Kramer Lab (Dave, Dan, Donghee Hoh, Isaac Osei-Bonsu and me), but also our PhotosynQ collaborators (Dr. Isaac Dramadri in Uganda, Dr. James Kelly with Dr. Jesse Traub and Dr. Wayne Loescher of MSU, and Dr. Kelvin Kamfwa of U of Zambia) presented more detailed and sophisticated data showing the correlations among photosynthesis, plant responses and gene expressions. It was very encouraging for us to see more people started thinking that the PhotosynQ platform and hand-held devices are useful and practical to the broad applications.

We are very excited about the new challenges, collaborations and long-lasting friendships. And we all hope to see you again!

Hello Fargo, I’ve come for your beans!

Project: Bean Variety Trials at North Dakota State University
Project Leads: Juan Osorno and Ali Soltani, North Dakota State University
Goal: Collect photosynthesis and plant health data on 150 varieties of common bean for eventual QTL (genetic) mapping.

Project Page
View and analyze the data (create a login if necessary)
Juan Osorno’s NDSU page

 

Hello Fargo!
Hello Fargo!

This week I went to Fargo, North Dakota to meet with Professor Juan Osorno and post-doc Ali Soltani, bean breeders at North Dakota State University. I bet you didn’t know that NDSU has one of the premier bean breeding programs in the US – well they do!

On my flight in, I told the guy next to me I’d never been to North Dakota before, and his response was “You’re going to love it”… Love it? North Dakota? Well, yes, I did love it. People were nice, and it appeared that everyone was there because they wanted to be, which makes sense, you don’t end up in North Dakota for no reason. Agriculture is booming, and the the fields are gigantic (at least in comparison to the ones I was used to growing up in central New York). So, what were we doing there? I’ll let Ali give a recap:

So our goal is to show that you can correlate photosynthetic outcomes to actual genes or groups of genes.   This has so far proven difficult and slow to achieve for breeders especially in comparison with the dizzying pace of mapping the genome, which has been automated and has come down in cost many orders of magnitude over the last 15 years. We took measurements of 150 different varieties with 6 replicates each (900 measurements total).  Each measurement included two protocols: SPAD (a measure of leaf greenness which correlates to Nitrogen content) and Phi2 (a measure of photosynthetic efficiency).

Stephan collecting data using the Android app
Stephan collecting data using the Android app

It took us some time to get ready to collect data.  We had to go to a coffee shop to get internet to make sure everyone had an account at PhotosynQ.org and their cell phones had the PhotosynQ android app installed correctly.  But once we got to the field (a full 1.5 hours away!), taking measurements was a snap.  The only technical problems we had were swapping batteries as they needed to be recharged – that was a big success for us, and shows we’re ready to do real work with this thing!

MultispeQs charging their batteries after a hard days work.
MultispeQs charging their batteries after a hard days work.

So let’s look at some preliminary results using the online analysis tool (so you can view and play with the data too!  Note that you may have to create a login first). This tool is intended to be a Swiss Army knife of sorts – it can do lots of quick analysis, but none of them too deeply.  If you need to do multiple regression analysis… you’ll probably have to just download the data 🙂  We might to see more data in this project this week, as Ali and Stephan go back to a second field, we’ll see.  Also, Ali is working on more in depth device comparisons, to try to use statistics to parse out the variation coming from the device versus that coming from the varieties themselves.

 

We can also compare two variables on the X and Y axis. Here we have LEF (linear electron flow) a measure of energy from photosynthesis compared to light intensity. Each device has a separate series. These differences may be due to calibration, or differences in plants, hard to know yet.
We can also compare two variables on the X and Y axis. Here we have fluorescence in the steady state (normal light) versus that from a saturated state (very high light).  These differences may be due to calibration, or differences in plants, hard to know yet.
SPAD (a measure of greenness) was fairly consistent across devices as you can see. Some variation is due to the fact that each device only measured 60 of the 150 varieties, so there's not perfect overlap there.
SPAD (a measure of greenness) was fairly consistent across devices as you can see. Some variation is due to the fact that each device only measured 60 of the 150 varieties, so there’s not perfect overlap there.
The most important outcome from this trial was to determine if 6 devices could produce consistent results. As you can see here, device 43 was reading too high on light intensity PAR - we'll have to investigate that!
The most important outcome from this trial was to determine if 6 devices could produce consistent results. As you can see here, device 43 was reading too high on light intensity PAR – we’ll have to investigate that!
This is a simple average of Phi2 for 15 varieties. The black bars are 1 standard deviation.
This is a simple average of Phi2 for 15 varieties. The black bars are 1 standard deviation.  Anything statistically significant here?… mmm… not quite yet.
Histogram showing Phi2 (photosynthetic efficiency) for the entire sample - distribution isn't too bad!
Histogram showing Phi2 (photosynthetic efficiency) for the entire sample – distribution isn’t too bad!  Not a lot of outliers which means the MultispeQs worked ok.
temperature by time
This graph doesn’t show much from a plant health perspective, but it does show how temperature in the device varied over time. In general we’ve found that people’s hands heat up the device the longer they hold it. You can see that effect here for each device (each series snakes upwards), and you can see how long it took us to take all our measurements. This is something else we need to address in the next version.
Here's a map of the field colored by device ID. The entire field is offset to the left by about 10 meters. However, you can see that each user measured from left to right over only a few rows, which was correct - cool!
Here’s a map of the field colored by device ID. The entire field is offset to the left by about 10 meters. However, you can see that each user measured from left to right over only a few rows, which was correct – cool!