If you’ve just landed here for the first time, this is a continuation of my October 17 grand opus Interrogating Plant Status in the Key of F where I pontificated, eruditely I hope, on the phenomenon of fluorescence emission induction in plants. If fluorescence induction sounds Greek to you, I strongly urge that you go back and read through that blog before proceeding. If you’re an expert at PAM fluorometry then everything that follows should be old hat, except for perhaps the fluorescence emission data which is the grist of this second go-round.
Commercial PAM fluorometers are nothing new. High-grade units like the Walz PAM-2500 and Hansatech FMS 2 have been mainstays in photosynthesis research since the 1980s. While of excellent quality, these instruments all have in common that they’re expensive, and rather bulky, even though advertised as field “portable”. Still, they’re not something I’d want to lug around for long periods, nor are they suited for high-throughput applications. The trend in instrumentation has been, irresistibly, the ever-shrinking form factor, while enhancing wireless broadband, user response, and cloud connectivity. The MultispeQ fits this mold: a pocket-size, fast (<15 seconds per measurement, see my comments further on), multipurpose PAM fluorometer designed for outdoor use anywhere you can imagine tracking under the sun.1 Not only can the MultispeQ be manipulated with one hand in any position, it’s Bluetooth 2 + EDR and micro-USB 2 enabled for pairing with your mobile phone or tablet for wireless transmission of in vivo fluorescence emission data to the PhotosynQ cloud server, nearly effortlessly.
Another compelling feature of the MultispeQ technology platform is its open architecture, which enables any researcher, citizen scientist, consultant, or intrepid farm operator, to build, program, and deploy their own units for whatever purpose they see fit. Of course, this takes a bit of training in electronics but the availability of off-the-shelf “plug and play” semiconductor components makes it potentially not any more challenging than, say, building your own PC from Newegg (Disclosure: I’m an avid builder and loyal Newegg customer). Saying that, MultispeQ is the brainchild of a group of photosynthesis experts and electrical engineering geeks at Michigan State University lead by Dr. David Kramer. The guiding philosophy of MultispeQ, and that of its developers, can be summed up as participatory research encompassing the broadest possible audience to collect, analyze, discuss, and share information about plant photosynthesis. This doesn’t preclude proprietary applications, but the accent is on sharing. There are several YouTube clips by David Kramer like this one where he articulates the goals and concepts driving the PhotosynQ vision. Unfortunately, the audio quality of this, and other, Kramer videos is sub-par so you’ll have to strain to get the full gist of the talk.
I stumbled on the MulitspeQ in 2017, a year after the beta release. To put it mildly, I was intrigued by the capabilities of the device and its networking concept. The fact that the MultispeQ’s sensor array output has been vetted against industry standards set it apart from the wave of environmental sensors that have recently flooded the market. It wasn’t long before I was scheming how to get my hands on one of these. The price was, and remains as of this writing, US $999, quite reasonable for a PAM fluorometer, but not in our budget. Fortunately, I was put in touch with a USDA plant physiologist at NC State who had recently purchased two MultispeQ devices for one of their projects. These were graciously loaned out to us over the 2018 growing season to deploy in our research. At the same time, we were conducting research on irrigation strategies for efficient corn production in the lower Coast Plain in Kinston, NC, with a programmable overhead linear-move system capable of precision water placement. Since we were also doing extensive plant phenotyping for this project, it made sense to piggyback the MultispeQ assay using the corn hybrids as test subjects. The field design for this project is available here.
Briefly, we conducted five MultispeQ assays: two at vegetative growth stages V9 and V12; at tasseling (VT), and at milk (R3), and dent (R5) stages. Two independent measures were taken on a fully expanded leaf blade on six pre-selected and tagged corn plants concurrently under observation for a suite of phenotypic traits, in each of 24 field plots. At VT and thereafter, measurements were taken on the dominant corn ear leaf, ±1. Measuring commenced mid-morning and lasted through mid- to late afternoon under existing atmospheric conditions: temperature, humidity, solar zenith angle, cloud cover. We sampled the plots incrementally by replication and plot number, beginning with plot 101, replication 1, thereon to plots 201, 301, 401. I don’t know if this was the best approach for measuring chlorophyll a fluorescence in the field, but I felt that it was the best strategy to complete the work in a timely manner while guarding against potential disruption from the weather or other unforeseen events. A full description of field operations management related to this project is available here.
It may be noticed that our experimental layout is a “fractional” factorial design with two corn hybrids, NK78S and P1870, planted at two population densities: 30,000 and 40,000 plants per acre, with and without 2x side-dress nitrogen application. The fractional design was necessary to keep the experimental footprint within the span of the overhead irrigation system, while leaving a sufficient buffer zone between plots to accommodate bidirectional control of sprinkler nozzles “on and “off” during operation. Consequently, the data have some inherent limitations in terms of interpretation, particularly in regard to hybrid response. We’ll keep this in mind later on.
Before going further, I want to give a shout out to Muhammad Atif Shabir, Faisalabad University, Pakistan visiting scholar without whose assistance I could never have contemplated a “side” project of this scale. As it happened, Mr. Shabir was looking for a field activity to engage with, and he was sent to me. I quickly realized this was a gift from Allah, as the Muslims would proclaim. I am indebted to Mr. Shabir for persevering many long, oppressive, sweat-inducing hours in the field: Yes, this is still largely how ag research is done, even here in the putative technological utopia of the United States. Mr. Shabir was throughout, present, helpful, and inquisitive. Insh’Allah we’ll meet again under Punjabi skies in fair Faisalabad.
Back in the field, Mr. Shabir and I got hustling: he sampled one row of three individual plants, while I sampled the row adjacent. In this way, I could later test for systematic operator error (this turned out negative). Each measurement took about 50 seconds to finish. This was considerably longer than the advertised time of 15 seconds or less, but we were using the V1.0 release, not the current V2.0 which may have speedier electronics; I don’t know. We used the default Leaf Photosynthesis MultispeQ V1.0 protocol measuring a suite of fluorescence and absorbance parameters, in addition to several abiotic parameters.
Using the MultispeQ is easy. To begin, the operator opens the measuring head and inserts a patch of leaf into the head between the upper and lower arms. The light guide and surrounding seal should completely cover the leaf and be clamped securely to exclude outside light. After answering a series of user-defined questions about the project, the operator taps the measure button on the PhotosynQ mobile app. While the instrument is running, it relays graphical information back to the PhotosynQ app, which can be easily monitored via a smartphone carried in the operator’s other hand (there’s also a desktop and web app that I won’t discuss). At the end of each measurement, readings or “traces” are assigned three different QC color codes: “green” indicating the measured parameters were within normal range; “yellow” if there’s noise or some other problem such as movement detected; and “red” if the readings are too noisy or out of the acceptable range. Ultimately, it’s up to the operator to decide how to handle these different codes. This was a constant source of delay in our progress in the field. We adhered to a self-imposed protocol where, for a given plot, at least 50% of the traces must be code green while no code reds were acceptable. Thus, any red trace was deleted, and the measurement repeated at another location on the same leaf. The developers state that there’s no a priori reason to reject yellow or red traces. But we didn’t want to take any chances as there was no practical way to analyze traces on-the-go, nor repeat the measurements if we found out, later on, there was a problem. In the end, I think this was a good idea.
Before a trace is accepted, you can add notes, photos, review or delete measurements as needed. Once the traces are accepted, they’re cached on your mobile device until submitted to the PhotosynQ cloud server. Since we weren’t within range of WIFI in the field, traces were submitted as soon as we got back to the Cunningham Farm service center in Kinston. This was also a good time to check that all measurements were accounted for and in good standing before heading out.
So, what did we learn from this burst of activity?
First, I downloaded the data to my desktop PC for review and post-processing. I confess that it took several months to compile all of it; the raw, unabridged files were quite large. Three fluorescence parameters were prioritized for analysis: effective quantum yield ΦII, or quantum efficiency of Photosystem II; non-photochemical quenching components ΦNPQ and ΦNO; and electron transport rate (ETR), a parameter derived as ETR = ΦII × PAR × 0.45 according to Kuhlgert et al 2016. The fluorescence parameters ΦII, ΦNPQ, and ΦNO represent the main pathways, or “yields”, for energy quenching in Photosystem II (PSII)2 and are related as pieces of the same pie: ΦII + ΦNPQ + ΦNO = 1. In this scheme, chlorophyll a fluorescence parameters are treated as mutually competing processes where an increase in efficiency in one fraction comes at the expense of another.
Quantum yield and non-photochemical quenching components were subjected to analysis of covariance (ANCOVA) as generalized linear-mixed models in SAS 9.4 with photosynthetic photon flux density (PPFD or “PAR”) as a continuous model covariate. In this way, photochemical response to irrigation and hybrid could be tested controlling for radiant light intensity throughout the sampling period3. The ETR data were treated differently, as explained further on. In deference to brevity (and space), only tasseling (VT) data are dissected following. Happily, these data were also the most revealing in terms of interpretive photochemistry.
The six panels in Figure 1 come from two sources: Panels A-C are from a 2017 paper in the journal Remote Sensing showing the relationship between PPFD, that is, radiant energy in the 400 to 700 nm bandwidth, and chlorophyll a fluorescence parameters ΦII, NPQ, and related ETR in maize leaves estimated by the joint Fraunhofer Line Depth and Laser-Induced Saturation Pulse (FLD-LISP) method. This work is unique because it’s the only published source of contactless chlorophyll a fluorescence that I’ve come across.
According to the authors, the FLD-LISP method is a combined passive and active remote sensing tool with the potential for eliciting long-distance, canopy-scale measurement of photochemistry and plant health in the field. The hitch is, as so often, the results from this study were obtained under controlled conditions. In this case maize plants, with two other non-agronomic species, were raised in an environmental growth chamber with controlled temperature, humidity, lighting, in an artificial soil medium supplied with precisely calibrated water and nutrients. This is how most plant research begins, for good reason. It is much easier to detect plant signals under controlled conditions than in the open where there’s so much other “noise” in the environment to contend with. Ultimately, though, a phenotypic response detected under controlled conditions must be validated by measuring the same thing in the field, in a process called “ground-truthing” or “benchmarking”. This is central to the emerging field of plant phenomics which is critical for rapid assessment of genomic traits in plant science research as well as engineering long-distance interrogation via remote sensing.
Placed beneath the FLD-LISP panels in Figure 1, for comparison, are the panels D, E, and F. These are replicate FLD-LISP parameters measured by the MultispeQ at Kinston in 2018. The Kinston data are aggregated over five growth stages from V9 to R5. Nevertheless, the impressions look similar to their FLD-LISP counterparts. Note that each panel in Figure 1 has a line slicing through the data points. These are regression lines relating fluorescence parameters to PPFD. Each regression line has an associated equation and r-square (R2) value or the coefficient of determination. In a classical linear function f(x), the r-square measures the degree of correspondence between an independent variable x, and dependent variable y. The R2 may take values from 0 to 1; generally, R2 values greater than 0.80 indicate a strong relationship between x and y, something we can depend on for predictive purposes.
It can be observed from Figure 1 that the nature of the relationship between ΦII and PPFD varied by source: the FLD-LISP method assigned a linear, or straight-line, relationship whereas, the line slicing through our Kinston ΦII data shows a degree of curvature. We found that a 2nd-order polynomial estimated apparent ΦII more precisely than a simple linear function. Such curvature was detected in other parameters as we drilled down in the data. Also, note the two red arrows in Figure 1F. The upper arrow points to the clear outer envelope of the ETR “plume” while the lower arrow points to a diffuse inner envelope. It’s my hypothesis that clear ETR envelopes signify a balanced train of electron transport in PSII; that is, plants with this type of ETR signature are able to self-regulate photochemistry, keeping the photosynthetic machinery humming even if under less than optimal conditions. On the other hand, diffuse irregular ETR envelopes implicate a perturbation, or uncoupling, of electron transport, and hence, photochemistry in PSII. In other words, something is out of whack. We’ll return to this idea later on in our Kinston ETR data.
To summarize: The FLD-LISP method and our MulitspeQ elicited similar response from the activity of photochemical and non-photochemical quenching in PSII. This bodes well for benchmarking long-distance solar-induced fluorescence in the open. Now, let’s drill deeper down.
In Figure 2, quantum yield response to PPFD has been partitioned into separate irrigation components, “full season”, “critical stage” and “no” supplemental irrigation. There are several interesting observations that can be made from this. First, the dashed trend lines are estimates of ΦII interpolated from model-derived regression equations. In so doing, the scatter of measured data points has been squelched to avoid chart clutter. The solid circles perched on the lines are the mean quantum efficiency at each of seven PPFDs calculated by the method of least squares in the Mixed Procedure in SAS 9.4. The open circles are the centered means, i.e. the overall mean quantum yield adjusted for the covariate PAR in the model. Second, note that quantum efficiency decreases with rising PPFD. At PPFD >1,500 µmol/m2/s, apparent quantum efficiency did not exceed 22%, which is a fraction of the theoretical maximum quantum efficiency of 85.4% for maize according to Bjorkman and Demmig 1987. This may appear counter-intuitive as maize has evolved mechanisms like the carbon-4 (C-4) metabolism to thrive under elevated temperature and incident light, compared to carbon-3 wheat and soybean. Nonetheless, sun-adapted plants may activate photoprotective mechanisms even in diffuse light. Third, the nature of the quantum yield response can be observed in the broad swath of PPFDs where maize quantum efficiency was downrated up to 27% under no irrigation compared with full season and critical stage irrigation. This implicates a direct link between the operating efficiency of PSII activity in maize leaves under mild drought stress and grain yield. Although the period around tasseling was relatively dry, with four rain-free days before tasseling on July 7, and ten days after with < 2.5 mm (0.10”) precipitation, this “mini drought” reduced corn grain yield by 23.1%, on average, under no irrigation. Typical symptoms of moisture stress in maize include diurnal leaf rolling, seen in Figure 3. Normally the plant recovers after dark, but the damage done is irreversible.
Paradoxically, 2018 was near record-breaking for precipitation at Kinston and across North Carolina: 162.46 cm (63.96”) was officially recorded at the Cunningham station. In contrast, the thirty-year average stands at 126.82 cm (49.93”). But this is something we in the Southeast have had to contend with forever: too much rain when you don’t need it and too little when you do. It’s the reason why, contrary to the display of lush countryside everywhere in eastern North Carolina, research has shown irrigation is profitable in most years and for most crops. Even relatively brief periods of high water consumption (transpiration) when coupled with inadequate supply (xylem transmission) can wreak havoc on canopy-scale photochemistry, and in turn, reduce crop yield in ways that are quantifiable but imperceptible to the human senses. Saying that, PAM fluorometers can be misleading because the light guide samples only a small fraction of the leaf area, 64 mm2 exactly for the MultispeQ. Leaves may be exposed to direct or diffuse light at different times of the day. As such, measures of plant productivity like yield and biomass depend on the integration of canopy-scale photosynthesis as well as other factors like leaf area index, leaf angle, and shading. This is well beyond the ability of PAM fluorometers to assess.
Moving further on, Figures 3 and 4 portray ΦNPQ and ΦNO response to PPFD at tasseling.
Remembering that ΦNPQ and ΦNO are related to quantum yield, ΦII as ΦII + ΦNPQ + ΦNO = 1, the ΦNPQ response in Figure 4 looks roughly like an inversion of ΦII in Figure 2. But what about ΦNO? In Figure 5, ΦNO can be observed rising slightly with PPFD but overall the ΦNO response to irrigation is lacking. To interpret this, we reach back to the definition of these two parameters: ΦNPQ is an energy partitioning parameter that indicates how much energy is been dissipated by regulated non-photochemical quenching. Whereas ΦNO indicates how much energy is being dissipated by unregulated non-photochemical processes.
My interpretation of ΦNO is that it’s evidence of self-regulating energy dissipation processes taking command during periods of stress, as nature intended. If, however, we observed a spike in ΦNO in any of the irrigation treatments, this would signal that unregulated quenching processes were taking over. This concept is perhaps better visualized in Figure 6 where mean quantum yield and non-photosynthetic quenching are plotted side-by-side for comparison.
A key observation from Figure 6 is that the ΦII and ΦNPQ response appears roughly opposite, or inversely proportional with respect to irrigation, whereas ΦNO is relatively indifferent.
This suggests that our maize hybrids are endowed with a degree of photochemical resilience even when facing environmental stress. This is great news for farmers who depend on improved genetics to smooth out the inevitable bumps in the road from seed emergence to maturity. It’s a nasty world out there, so the plant must be equipped with mechanisms to self-regulate and/or avoid stress to reach the point of physiological maturity. Fortunately, evolution has equipped terrestrial plants with various mechanisms for survival; fluorescence emission and NPQ are just two examples. For the farmer, however, plant survival is no comfort. Any stress, no matter how slight, can impact plant metabolism, photochemistry, and ultimately, net carbon assimilation. It’s left to the farmer’s knowledge, operations savvy, and keen judgment at every point to optimize productivity. Those who’ve experienced the agony of defeat in crop failure understand it’s a supremely hard act to follow.
Lastly, we consider ETR. Earlier I mentioned that our fractional experimental design put limits on the inferences possible with regard to maize hybrid and/or population density. To partly mitigate this constraint, I’ve broken down the ETR response according to hybrid population density and irrigation, shown in Figure 6.
It can be observed in both hybrids that trend lines for no irrigation trailed well beneath full season and critical stage irrigation, particularly at higher PPFDs. But hybrid P1870 trails considerably lower compared to NK78S. It can also be noticed that the diffuse lower ETR boundary consists entirely of points estimated from plots where no supplemental irrigation was applied. Three such plots: 204, 305, and 403 are annotated in Figure 6. Not coincidentally, grain yield from these same plots placed in the lower 25th percentile. Recall above where I posited that a clear ETR envelope indicated normal self-regulation of the electron transport chain whereas a diffuse envelope implicates a perturbation, or uncoupling, of electron transport, a kink if you will, in the photochemical chain in PSII. It can be deduced from the ETR envelopes in Figure 6 that hybrid P1870 exhibited greater diffusion compared to NK78S. Also recall that hybrid P1870 was planted at a higher population density: 40,000 plants per acre vs. 30,000 plants per acre for NK78S. The evidence from ETR analysis suggests that P1870 overall was less resilient in terms of sustaining normal electron transport at tasseling; in particular, some plants in plot 403 were apparently stressed to the point of uncoupling. We observed similar ETR signatures in the linear growth phase V12, when plant-to-plant competition for resources was maximal (not shown). In short, the ETR analysis suggests that hybrid P1870’s planting density was too high relative to the available resources or, that particular hybrid was not well adapted to higher populations, or both4. In any case, ETR appears to be a highly sensitive and visually distinct marker of phenotypic adaptive capability in the field.
Finishing, let’s wrap things up point by point:
- Near-distance FLD-LISP fluorescence (Rahinzadeh-Bajgiran et al. 2017) and the MulitspeQ PAM fluorometer elicited similar responses from the activity of photochemical and non-photochemical quenching in PSII, and electron transport rate (ETR) in maize under varying photosynthetic photon flux density (PPFD).
- The apparent quantum efficiency of PSII decreased with rising PPFD, which did not exceed 22% of the maximum theoretical rate at PPFD >1,500 µmol/m2/s considered the average radiant intensity under full sunlight at mid-latitude.
- With no supplemental irrigation, quantum efficiency at tasseling was reduced up to 27% compared with critical stage and full-season irrigation. Grain yield was also reduced by 23% under no irrigation.
- Non-photochemical quenching components ΦNPQ and ΦNO responded as expected for well-regulated genotypes.
- ETR analysis suggests that it’s a highly sensitive marker for phenotypic adaptive response in maize.
So, what’s the final verdict on MultispeQ?
While the MultispeQ was able to predict yield loss at tasseling, it’s not known how fluorescence emission signals are propagated temporally and what is their cumulative effect on plant performance. More work on this is needed. Relating leaf-scale fluorescence to canopy-scale is trickier, something we could not attempt without access to solar-induced fluorescence imaging.
While we rate this pilot study as successful, it’s clear that MultispeQ is not the ideal solution for high throughput phenotyping applications. Even though relatively speedy (we clocked about 50 seconds per measurement vs. 15 seconds claimed by developers) the time and labor involved in retrieving a statistically reliable sample from hundreds of field plots via MultispeQ would be prohibitive. In this regard, the MultispeQ is better suited for benchmarking long-distance fluorescence, rather than large-scale field phenotyping. Still, it’s a great educational tool for quickly assessing fluorescence parameters like quantum efficiency and, indirectly, plant health in the field. Greenhouse applications would also appear promising. In particular, we commend the global reach and collaborative spirit of the PhotosynQ project.
Future maize assays should focus on taking multiple leaf measurements in a few selected genotypes from mid-morning to mid-afternoon, repeated weekly beginning V3 through R3 (milk stage). In this way, overlays of fluorescence traces may better assess diurnal and seasonal variation which would be more informative than our discreet, one-shot growth stage approach at Kinston. In maize, the time leading up to V6 is critical for determining several yield components: plant population; ears per plant, and kernel rows per ear. Therefore, it’s critical to capture this frame in any temporal analysis. Unfortunately, we were delayed making the first, pre-sidedress leaf assay until V9. Further, wet ground conditions early on delayed side-dressing until V9, long past the optimal time for this critical operation. So, we don’t know how late side-dressing may have impacted maize development and final grain yield, irrespective of other factors.
But that’s how it goes. Every year brings fresh challenges. In the real world of agricultural prognostication, time is like digital pixel resolution: three years of data are better than one; five years even better; and ten years plus make for precision near- and long-term forecasts if you have the $$ to stretch it out that far. Most don’t.
It’s all in the tolerance for error, which is inescapable. Just keep a keen eye on emergence. You can’t win without that.
1Each MultispeQ trace is tagged with a latitude and longitude. However, it’s not clear whether it’s grabbing this information from your mobile device or via an internal GPS receiver. Either way, the positional accuracy is probably no better than about 5 m (~16 ft) under ideal conditions. This should be kept in mind if you’re doing spatial analysis of experimental data. For plot- or plant-scale geotagging, you would need to carry a secondary device to incorporate higher-precision location information.
2Fluorescence emission yield is not reported by the MultispeQ. Recall that fluorescence represents the fraction of radiant energy that does not enter the reaction centers of Photosystem II, so it’s not an indicator per se of PSII activity.
3The MulitspeQ also measures leaf surface temperature using a contactless thermal IR sensor in the instrument head. In our VT sampling leaf surface measurements ranged from 28° C to 39° C. The ambient temperature point typically associated with a decrease in photosynthesis activity in maize is around 35° C, and goes to zero around 43° C. Ambient temperature and leaf temperature vary according to factors like stomatal density and conductance, and leaf thickness, among others. Leaf temperature information was not included in our predictive models. However, it’s something to watch out for when evaluating PSII activity.
4 No inference is possible about hybrid NK78S at 40,000 plants per acre due to the fractional factorial design.
Björkman, O., and B. Demmig. 1987. Photon yield of O2 evolution and chlorophyll fluorescence characteristics at 77 K among vascular plants of diverse origins. Planta 170, 489–504. https://doi.org/10.1007/BF00402983
Kuhlgert, S., Austic, G., Zegarac, R., Osei-Bonsu, I., Hoh, D., Chilvers, M.I., Roth, M.G., Bi, K., TerAvest, D., Weebadde, P., and D.M. Kramer. 2016. MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. Royal Society Open Science 3, 160592. https://doi.org/10.1098/rsos.160592
The author thanks the Corn Growers Association of North Carolina and Syngenta Biotechnology for their generous support of this work.
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