I'm now at the point where I want to download some of the data I've collected and I can find definitions of some of the parameters that I've measured (phi2, LEF, SPAD, PAR), but I'm wondering what these others are? Thanks
First, ignore Phi2 and all the other stuff there. Those things are either not ready for prime time, or they are values which don't matter (calibration values and stuff). Otherwise...
Phi2 Expected value range: 0 - .84. Phi2 is the efficiency of Photosystem 2 . Read it roughly as "X% of the incoming light energy is going to do photochemistry (ie do useful work for the plant". So .8 would 80% of the energy is going to do useful work.
NPQ Expected value range: 0 - 5 (though it can go as high as 40). NPQ is Non-Photochemical Quenching. This is actually a cutting edge estimate of NPQ which normally takes an hour to measure, we call it NPQt. But anyway, it says "how much of the incoming light is being dissipated as heat". It is a unitless value.
LEF Expected value range: 0 - 500ish. LEF is Linear Electron Flow. Read as "X number of micro moles of electrons per second are flowing out of Photosystem 2." LEF is calculated as *LEF = Phi2 * light intensity PAR x .42 *. The .42 is the amount of light which is hitting Photosystem 2, as compared to that which is reflected, diffused, or hits Photosystem 1. So if your LEF value looks funny - investigate Phi2 and PAR first!
PAR Expected value range: 0 - 2000ish (can be higher in certain types of sun/cloud conditions). PAR is Photosynthetically Active Radiation, the portion of light which is usable by plants to do photosynthesis.
Chlorophyll Content Expected value range: 0 - 75ish. 75 means the leaf is very green (contains lots of chlorophyll and nitrogen), 0 is not green (very little chlorophyll / nitrogen).
What's normal / what to expect
Hope this helps clarify things!
sample 940, cal 940, sample 650, cal 650 what these ??? It's sample and calibration?
The sample and cal values are blank raw absorbance values. When you calculate SPAD you need to have a blank as a reference (just like when you measure absorbance). The calculation for SPAD is:
SPAD ~ log((sample 940 - blank 940) / (sample 650 - blank 650))*100
I put a ~ instead of a = because this produces a number which correlates very well to Minolta's SPAD, but is offset. So we then calibrate the devices to an actual Minolta to remove the offset
There are also a bunch of 'userdef' values, which are the calibration to the Minolta values (see attached). We've already calibrated this stuff, so I wouldn't worry about doing it your end.
Thanks, that's helpful. When I see a negative value for any of these parameters then it basically means that the datapoint needs to be discarded? Also, the number of datapoints is significantly less than I thought I was collecting. One of the days (June 16) I sampled nearly 100 leaves, yet I've only got 49 datapoints for that day. It was one of the days where I had to get your help downloading data. Is it possible the other datapoints were lost in the great beyond?
Just a few more questions-are the time stamp data accurate?
I've got one sampling date where a couple of columns are headed by "get_userdef1, 2 and 5". Can these be ignored too?
Timestamps are in GMT which is why they look off.
Hmmm I hope they weren't lost... They should be on the device or on the web... there are no more cached data points on that phone? I'm not sure, we'd have to troubleshoot that.
Yes you can ignore userdef values, see post above for description of those.
In terms of negative values, the only way to really tell what's going on is to look at the raw trace which you can do by clicking on the data point in the spreadsheet view or on a graph. Some data points are errors, and some are simply dead (ie no photosynthesis). I would argue you shouldn't exclude dead leaves (flat line) whereas you should exclude clear user error (moved the leaf or device).
Here are some good examples for The One measurement:
a good measurement:
user error (moved the device or something):
more user error:
a dead leaf (flat line):
We're working on some ways to flag this data to make it easier to identify and to help you all develop a quicker intuition on what is good and what is bad.