phenovation-redesign

Anthocyanins Uncovered: A Spectral Method for Stress and Senescence Detection in Plants

Thu, 30 Apr 2026 07:17:14 GMT

Anthocyanins (Anth) are water-soluble vacuolar pigments in higher plants, responsible for red coloration in leaves and often induced by environmental stresses such as UV radiation, drought, or nutrient deficiencies. Their accumulation serves as a marker for leaf stress and senescence, making their quantitative assessment valuable for understanding plant responses to environmental challenges. However, traditional methods for Anth estimation previously relied on destructive chemical extraction, which was time-consuming and impractical for large-scale or real-time studies. In their 2001 study, Gitelson et al. addressed this limitation by developing a nondestructive, reflectance-based method to estimate Anthocyanin content in intact leaves.

Gitelson et al. analyzed the absorption and reflectance spectra of leaves from four plant species; Acer platanoides (maple), Cotoneaster alaunica (cotoneaster), Cornus alba (dogwood), and Pelargonium zonale (pelargonium), across a wide range of pigment compositions. By comparing Anth-containing leaves to Anth-free leaves with matched chlorophyll (Chl) content, they identified a distinct absorption peak for Anth at 550 nm. This peak was linearly correlated with Anth content, establishing a direct relationship between spectral behavior and pigment concentration. However, the challenge lay in the spectral overlap between Anth, Chl, and carotenoids (Car) in the green region (500–600 nm), where Chl absorption is particularly strong. To estimate Anth accurately, it was necessary to account for Chl’s contribution to reflectance in this spectral range.

To isolate Anth’s spectral signal, Gitelson et al. developed the Anthocyanin Reflectance Index (ARI), defined as:

ARI = (R₅₅₀)⁻¹ – (R₇₀₀)⁻¹

where R₅₅₀ and R₇₀₀ are the inverse reflectances at 550 nm and 700 nm, respectively. The rationale behind this index is rooted in the optical properties of leaves:

At 700 nm, reflectance is minimally affected by Anth but strongly influenced by Chl. In Anth-free leaves, R₅₅₀ and R₇₀₀ are nearly equal, and their inverse values correlate closely with Chl content.
In Anth-containing leaves, Anth absorption reduces R₅₅₀ , causing the ARI to increase proportionally with Anth content. By subtracting the Chl-dominated signal at 700 nm, the ARI effectively isolates Anth’s contribution.

This method was validated across multiple species and pigment compositions, demonstrating a high linear correlation (R² = 0.94) between ARI and analytically measured Anth content. The standard error of estimation was ≤ 3 nmol/cm² for Anth levels between 0.3 and 25 nmol/cm². For higher Anth concentrations (>25 nmol/cm²), where the ARI’s sensitivity decreases, alternative indices such as (R₅₅₀)⁻¹ or R_NIR/R₅₅₀ (where R_NIR is reflectance in the near-infrared) were proposed, achieving a standard error of <3.9 nmol/cm².

Gitelson et al. compared the ARI to the red/green ratio (Gamon & Surfus, 1999), a previously proposed method for Anth estimation. The red/green ratio, however, is influenced by Chl, Car, and Anth simultaneously, leading to a weaker correlation (R² = 0.55) and a higher standard error (10.2 nmol/cm²). In contrast, the ARI demonstrated 2.5× greater accuracy, making it a superior method for Anth estimation in intact leaves.

For further details, we encourage readers to explore the original publication:

Gitelson, A.A., Merzlyak, M.N., Chivkunova, O.B., (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology,74(1), 38-45. doi: 10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2 .

The Power of the Red Edge: Using Gitelson’s Methods for Chlorophyll Measurement

Wed, 15 Apr 2026 08:41:45 GMT

According to Gitelson et al. (2003), the most accurate way to estimate chlorophyll content from reflectance measurements is to use the following algorithm:

Chlorophyll Index=(R _NIR /R _λ )−1

where:

R _NIR is the reflectance in the near-infrared (NIR) range (e.g., 750–800 nm).

R _λ is the reflectance in the green (520–585 nm) or red edge (695–740 nm) regions.

Gitelson et al. (2003) provide three key reasons for the superior accuracy of this approach:

1. Linear Relationship with Chlorophyll Content

The authors found that reciprocal reflectance (R _λ ) ⁻¹ in the green (520–585 nm) and red edge (695–740 nm) regions is linearly proportional to total chlorophyll content across a wide range of species and chlorophyll concentrations (1–830 µmol/m²).

In the blue (400–500 nm) and red (600–680 nm) regions, the relationship is non-linear and saturates at higher chlorophyll levels (>150 µmol/m²), making these regions less reliable for accurate estimation.

By subtracting the reciprocal reflectance in the NIR range (R _NIR ) ⁻¹ from (R _λ ) ⁻¹ , the authors created an index:

[(R _λ ) ⁻¹ −(R _NIR ) ⁻¹ ]

This subtraction eliminates the intercept (background noise) and makes the index linearly proportional to chlorophyll content across the entire range (1–830 µmol/m²).

2. Correction for Leaf Structure Variations

Impact of Leaf Structure: Leaf thickness, density, and internal scattering can vary significantly between species and even within the same plant. These structural differences affect reflectance, especially in the NIR range, where light scattering is dominant.

To account for structural variations, Gitelson et al. multiplied the index by R _NIR :

[(R _λ ) ⁻¹ −(R _NIR ) ⁻¹ ]⋅R _NIR =(R ^λ /R _NIR )−1

This adjustment reduces sensitivity to leaf thickness and density, making the algorithm more robust across different species and leaf structures.

3. Minimal Sensitivity to Pigment Composition

The slope of the relationship between (R _λ ) ⁻¹ and chlorophyll content is consistent in the green and red edge regions (520–585 nm and 695–740 nm) across different species. In contrast, the slopes in the blue and red regions vary widely, leading to less accurate estimates.

The coefficient of variation for the slope of the relationship (R _λ ) ⁻¹ vs. chlorophyll is minimal (<10%) in the green and red edge regions, ensuring high accuracy regardless of species or pigment composition.

The algorithm was validated using independent datasets (maple and beech leaves) and achieved an RMSE of less than 49 µmol/m² for chlorophyll estimation, which is significantly lower than other tested indices (e.g., R ₈₀₀ /R ₆₈₀ or (R ₈₀₀ −R ₆₈₀ )/(R ₈₀₀ +R ₆₈₀ ) ).

Gitelson et al. compared their algorithm with several previously developed indices (e.g., Blackburn 1998, Datt 1998) and found that their approach provided the lowest RMSE and the most linear relationship with chlorophyll content.

For further details, we encourage readers to explore the original publication:

Gitelson, A.A., Gritz †, Y., Merzlyak, M.N., (2003) . Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.

Journal of Plant Physiology 160, 271–282. https://doi.org/10.1078/0176-1617-00887

A Sustainable Solution for Rice Blast Control and Magnesium-Deficient Photosynthesis

Thu, 09 Apr 2026 12:47:50 GMT

Rice blast, caused by the fungal pathogen Magnaporthe oryzae, is a major threat to global rice production, capable of reducing yields by up to 50%. Conventional chemical fungicides, while effective, face increasing challenges due to pathogen resistance and environmental toxicity. Meanwhile, magnesium (Mg) deficiency—a widespread issue in intensive agriculture—compromises plant immunity and photosynthetic efficiency. Addressing both disease pressure and nutrient deficiency requires innovative, sustainable solutions. A recent study by Zhang et al. (2026), published in ACS Nano, introduces magnesium-doped zeolitic imidazolate framework-8 nanoparticles (Mg-ZIF-8 NPs)—a dual-functional nanomaterial designed to suppress rice blast and restore magnesium-deficient photosynthesis. This breakthrough offers a promising alternative to traditional agrochemicals, combining antifungal activity with nutrient supplementation.

The nanoparticles work by disrupting fungal cell membranes, inducing oxidative stress, and inhibiting the formation of infection structures in M. oryzae. At the same time, they replenish magnesium in rice leaves, restoring photosynthetic function and activating plant defense pathways. The study demonstrates that Mg-ZIF-8 NPs not only reduce disease severity but also improve rice growth under magnesium-deficient conditions, as evidenced by increased chlorophyll content, photochemical efficiency, and vegetative indices.

What makes this innovation particularly compelling is its systemic mobility within the plant. Mg-ZIF-8 NPs can be absorbed through both foliar and root application, ensuring targeted delivery to infection sites while minimizing environmental exposure. Advanced imaging tools, such as chlorophyll fluorescence imaging and multispectral analysis, were used to visualize the nanoparticles’ effects on rice physiology, confirming their ability to restore photosynthetic health and reduce stress responses. Biosafety assessments further support the potential of Mg-ZIF-8 NPs as a green nanofungicide. Tests on rice seeds, zebrafish, and earthworms revealed minimal toxicity, aligning with the principles of sustainable agriculture. The nanoparticles’ biodegradability and low ecological footprint make them an attractive option for modern farming systems seeking to reduce chemical inputs.

The study also highlights a paradigm shift in crop protection: the development of multifunctional nanomaterials that address both disease control and nutrient management. As climate change intensifies the pressure on global food systems, such innovations will be critical for optimizing resource use, reducing yield losses, and sustaining productivity under stress. The dual-functional nature of Mg-ZIF-8 NPs not only offers a powerful tool for rice blast management but also sets a precedent for the future of sustainable agriculture—where smart, eco-friendly solutions drive resilience in the face of adversity.

For further details, we encourage readers to explore the original publication:

Zhang, D., et al. (2026). Dual-Functional Magnesium-Doped Zeolitic Imidazolate Framework-8 Nanoparticles for Rice Blast Control and Restoration of Magnesium-Deficient Photosynthesis. ACS Nano 20, 2269–2286. https://doi.org/10.1021/acsnano.5c17850

Chlorophyll Fluorescence Measuring Methods OJIP - part 2

Mon, 30 Mar 2026 14:18:32 GMT

OJIP is a measurement method used to study chlorophyll fluorescence kinetics in detail, specifically the rapid initial rise in fluorescence during a saturating light pulse. This rise reveals information about energy fluxes and electron transport within the photosynthetic apparatus.

In Part 1 of our OJIP blog series, I explained which physiological processes cause this characteristic fluorescence increase. In Part 2, I will focus on the parameters that can be derived from the OJIP transient (JIP test). These parameters allow us to quantify the processes discussed earlier, turning the curve shape into measurable physiological indicators.

Schematic overview

JIP parameters focus on the pathway of energy through the photosynthetic system: they track the fate of light energy from the moment it is absorbed, showing whether it is used to drive photosynthesis or dissipated as heat or fluorescence. The parameters can be grouped into three categories: energy fluxes, quantum yields and efficiencies.

1.1. Energy fluxes

Flux parameters describe the amount of energy flow through the different steps of the electron transport chain:

(1) Absorption flux: Photons absorbed by the antenna pigments and creating excited chlorophyll.

(2) Trapping flux: Channeled energy from excited chlorophyll to the reaction center to be converted into the electron transport chain (QA reduction).

(3) Electron flux: Electron transport further than QA

(4) Reduction flux: Reduction of end electron acceptors at the PSI side of the electron transport chain.

(5) Dissipation flux: Energy that is dissipated as heat or fluorescence.

1.2. Quantum yields + efficiencies

Next to the energy fluxes, we can calculate the fraction of absorbed energy by PSII that is used for a specific photochemical event. These fractions are also known as the quantum yield. Furthermore, the efficiency of the the event (with trapped energy) can also be calculated:

Quantum yield of primary photochemistry, reducing QA (φP₀, often expressed as Fv/Fm)
Quantum yield of electron transport from QA → QB → PQ pool (φE₀)
Quantum yield of electron transport to the final PSI acceptors (φR₀)
Efficiency with which a trapped excitation moves an electron further than QA into the electron transport chain (ΨE₀)
Efficiency with which electrons arriving at PSI reduce the end acceptors FA/FB, ferredoxin, or NADP⁺ (δR₀)

These parameters provide insight into the functioning of the photosynthetic apparatus and are valuable for studying, for example, the mode of action of various compounds or the effects of environmental stresses.

A parameter that combines multiple of these processes, and that is highly sensitive to stress is the Performance Index (PI) .

1.3. Performance index

The Performance Index (PI) was introduced because the commonly used parameter for photosynthetic efficiency, Fv/Fm, is not always sensitive enough to detect early or subtle stress responses. PI integrates multiple components of the photosynthetic process into a single value (energy absorption, trapping and electron transport). It is widely used by plant physiologists to evaluate the effects of biotic and abiotic stresses, and it is also a powerful parameter for breeding programs to rapidly screen genotypes under conditions such as drought or salinity stress. PI is considered one of the most sensitive OJIP-derived parameters.

There are two Performance Index variants: PI_ABS and PI_TOTAL .

PI_ABS combines three components into a single value:

The density of active reaction centres
The efficiency of trapping (the probability that absorbed light energy closes QA)
The efficiency of electron transport beyond QA

PI_TOTAL includes the three components of PI_ABS and adds a fourth:
4. The efficiency of electron transport to the final PSI acceptors

Because PI incorporates multiple steps in the photosynthetic electron transport chain, any disturbance affecting one of the components will be reflected in the PI value, making it more sensitive than individual parameters alone.

2. Literature review

To provide an overview of the JIP parameters and how they can be used, we performed a literature survey to assess how different types of stresses affect JIP parameters. The following stresses were included: salt stress, drought stress, heat stress, cold stress, and high-light stress.

In general, several JIP parameters consistently respond to stress across studies, but the direction of the response can vary: some parameters increase under stress in a certain species or conditions, while others decrease. The exact pattern depends on the plant species, or even cultivar, and the severity of the stress. A table overview of the results is summarized below (click the link).

3. How parameters from OJIP are often visualized and interpreted

The large number of available OJIP parameters can make interpretation challenging. However, it is valuable for researchers to view the full set of parameters, as each one reflects a different component of the photosynthetic apparatus. Effects in one part of the electron transport chain may not appear in another, so examining multiple parameters can reveal where specific changes or stress responses occur.

A useful way to visualize the complete dataset is through spider plots (Figure 1). These plots allow many parameters to be displayed simultaneously, making it easier to identify patterns, compare treatments, or detect stress-induced deviations.

In some studies, authors visualize the different energy fluxes (per cross‑section) using a leaf model, such as the one shown below (Figure 2). In this type of schematic, the width of the arrows represents the magnitude of each energy flux, with yellow indicating absorption flux, dark blue representing electron transport flux, light blue showing trapping flux, and red depicting dissipation flux. The black dots inside the central square illustrate the number of inactive or silent reaction centres.

This type of model is highly informative, as it summarizes the state of the photosynthetic apparatus in a single image. In the example shown above, two barley cultivars were compared under high‑light and low light conditions. From the flux pattern, it becomes clear that the cultivar ‘Arabi Aswad’ (left) is more capable of handling high light, having a larger electron transport flux, lower dissipation flux, and more active reaction centers. The cultivar ‘Arabi Abiad’ (right) is better adapted to low light.

The model can also be expressed as a membrane model, showing the fluxes per reaction center (RC). An example is given below (Figure 3).

Here, the effect of heating on the membrane model (per reaction center) is described by Strasser as:

• "Absorption per active reaction center increases due to the inactivation of some RCs"

• "The ratio of total dissipation to the amount of active RCs increases due to the high dissipation of the inactive RCs"

• "Electron transport per active reaction center increases due to a thermal activation of the dark reactions"

And on the leaf model (per cross section) as:

• "Decrease of electron transport per excited cross section due to the inactivation of reaction center complexes"
• "Decrease of the density of active reaction centers RC/CS (indicated as open circles)
• "Increase of the energy dissipation per excited cross section."
• "Decrease of the energy absorbed per excited cross section ABS/CS."

4. References

1. Strasser, R.J., Srivastava, A. And Govindjee (1995). Polyphasic chlorophyll a fluorescence transient in plants and cyanobacteria. Photochemistry and Photobiology 61: 32-42.

2. Strasser, R.J., Srivastava, A. And Tsimilli-Michael, M. (2000). The fluorescence transient as a tool to characterize and screen photosynthetic samples. In: Ynus, M. (Ed.) Probing Photosynthesis: Mechanisms, Regulation and Adaptation. Taylor and Francis, London: 445-483.

3. Strasser, R.J., Tsimilli-Micael, M. and Srivastava, A. (2004). Analysis of the Chlorophyll a fluorescence transient. In: Papageorgiou, G.C. and Govindjee (Eds.) Chlorophyll a fluorescence: a signature of photosynthesis. Springer, Dordrecht: 321-362.

4. Lotfi, R., H. M. Kalaji, G. R. Valizadeh, E. Khalilvand Behrozyar, A. Hemati, P. Gharavi‑Kochebagh, and A. Ghassemi. 2018. Effects of humic acid on photosynthetic efficiency of rapeseed plants growing under different watering conditions. Photosynthetica 56 (3): 962–970. https://doi.org/10.1007/s11099-017-0745-9.

5. Zushi, K., S. Kajiwara, and N. Matsuzoe. 2012. Chlorophyll a fluorescence OJIP transient as a tool to characterize and evaluate response to heat and chilling stress in tomato leaf and fruit. Scientia Horticulturae 148 : 39–46. https://doi.org/10.1016/j.scienta.2012.09.022.

6. Kalaji, H. M., A. Rastogi, M. Živčák, M. Brestič, A. Daszkowska‑Golec, K. Sitko, K. Y. Alsharafa, R. Lotfi, P. Stypiński, I. A. Samborska, and M. D. Cetner. 2018. Prompt chlorophyll fluorescence as a tool for crop phenotyping: an example of barley landraces exposed to various abiotic stress factors. Photosynthetica 56 : 953–961. https://doi.org/10.1007/s11099-018-0766-z.

7. Chen, X., Y. Zhou, Y. Cong, P. Zhu, J. Xing, J. Cui, W. Xu, Q. Shi, M. Diao, and H.‑Y. Liu. 2021. Ascorbic acid‑induced photosynthetic adaptability of processing tomatoes to salt stress probed by fast OJIP fluorescence rise. Frontiers in Plant Science 12 : 594400. https://doi.org/10.3389/fpls.2021.594400.

9. Stefanov, D., V. Petkova, and I. D. Denev. 2011. Screening for heat tolerance in common bean (Phaseolus vulgaris L.) lines and cultivars using JIP‑test. Scientia Horticulturae 128 (1): 1–6. https://doi.org/10.1016/j.scienta.2010.12.003.

11. Jedmowski, C., and W. Brüggemann. 2015. Imaging of fast chlorophyll fluorescence induction curve (OJIP) parameters, applied in a screening study with wild barley (Hordeum spontaneum) genotypes under heat stress. Journal of Photochemistry and Photobiology B: Biology 151 : 153–160. https://doi.org/10.1016/j.jphotobiol.2015.07.020.

13. Kalaji, H. M., A. Oukarroum, V. Alexandrov, M. Kouzmanova, M. Brestic, M. Zivcak, I. A. Samborska, M. D. Cetner, S. I. Allakhverdiev, and V. Goltsev. 2014. Identification of nutrient deficiency in maize and tomato plants by in vivo chlorophyll a fluorescence measurements. Plant Physiology and Biochemistry 81 : 16–25. https://doi.org/10.1016/j.plaphy.2014.03.029.

14. Mehta, P., S. I. Allakhverdiev, and A. Jajoo. 2010. Characterization of photosystem II heterogeneity in response to high salt stress in wheat leaves (Triticum aestivum). Photosynthesis Research 105 (3): 249–255. https://doi.org/10.1007/s11120-010-9588-y.

15. Jedmowski, C., A. Ashoub, and W. Brüggemann. 2013. Reactions of Egyptian landraces of Hordeum vulgare and Sorghum bicolor to drought stress, evaluated by the OJIP fluorescence transient analysis. Acta Physiologiae Plantarum 35 : 345–354. https://doi.org/10.1007/s11738-012-1077-9.

16. Ren, J., P. Guo, X. Zhao, X. Ma, X. Ai, J. Wang, H. Zou, and H. Yu. 2025. Differential photosynthetic responses to drought stress in peanut varieties: insights from transcriptome profiling and JIP‑Test analysis. BMC Plant Biology 25 : 957. https://doi.org/10.1186/s12870-025-06984-y.

17. Mihaljević, I., M. Viljevac Vuletić, V. Tomaš, Z. Zdunić, D. Vuković, and K. Dugalić. 2024. Assessment of photosynthetic capacity of two blackberry cultivars subjected to salt stress by the JIP fluorescence test. Journal of Berry Research 14 (1): 1–13. https://doi.org/10.3233/JBR-230026.

18. Rastogi, A., M. Kovář, X. He, M. Živčák, S. Kataria, H. M. Kalaji, M. Skalicky, U. F. Ibrahimova, S. Hussain, S. Mbarki, and M. Brestic. 2020. JIP‑test as a tool to identify salinity tolerance in sweet sorghum genotypes. Photosynthetica 58 (Special Issue): 518–528. https://doi.org/10.32615/ps.2019.169.

19. Ranjbarfordoei, A., R. Samson, and P. Van Damme. 2006. Chlorophyll fluorescence performance of sweet almond (Prunus dulcis (Miller) D. Webb) in response to salinity stress induced by NaCl. Photosynthetica 44( 4): 513–522. https://doi.org/10.1007/s11099-006-0064-z.

20. Jafarinia, M., and M. Shariati. 2012. Effects of salt stress on photosystem II of canola plant (Brassica napus L.) probed by chlorophyll a fluorescence measurements. Iranian Journal of Science and Technology (Sciences) 36 (1): 71–76. https://doi.org/10.22099/ijsts.2012.2058.

21. Zushi, K., and N. Matsuzoe. 2017. Using of chlorophyll a fluorescence OJIP transients for sensing salt stress in the leaves and fruits of tomato. Scientia Horticulturae 219 : 216–221. https://doi.org/10.1016/j.scienta.2017.03.016.

22. Giorio, P., and M. H. Sellami. 2021. Polyphasic OKJIP chlorophyll a fluorescence transient in a landrace and a commercial cultivar of sweet pepper (Capsicum annuum L.) under long‑term salt stress. Plants 10 (5): 887. https://doi.org/10.3390/plants10050887.

23. Akhter, M. S., S. Noreen, S. Mahmood, H.‑u‑R. Athar, M. Ashraf, A. A. Alsahli, and P. Ahmad. 2021. Influence of salinity stress on PSII in barley (Hordeum vulgare L.) genotypes, probed by chlorophyll‑a fluorescence. Journal of King Saud University – Science 33 (1): 101239. https://doi.org/10.1016/j.jksus.2020.101239.

24. Zhang, T., H. Gong, X. Wen, and C. Lu. 2010. Salt stress induces a decrease in excitation energy transfer from phycobilisomes to photosystem II but an increase to photosystem I in the cyanobacterium Spirulina platensis. Journal of Plant Physiology 167 (12): 951–958. https://doi.org/10.1016/j.jplph.2009.12.020.

25. Weng, H., M. Wu, X. Li, L. Wu, J. Li, T. O. Atoba, J. Zhao, R. Y. Wu, and D. Ye. 2023. High‑throughput phenotyping salt tolerance in JUNCAOs by combining prompt chlorophyll a fluorescence with hyperspectral spectroscopy. Plant Science 330 : 111660. https://doi.org/10.1016/j.plantsci.2023.111660.

26. Chen, X., X. Liu, Y. Cong, Y. Jiang, J. Zhang, Q. Yang, and H. Liu. 2025. Melatonin alleviates photosynthetic injury in tomato seedlings subjected to salt stress via OJIP chlorophyll fluorescence kinetics. Plants 14 (5): 824. https://doi.org/10.3390/plants14050824.

27 . Antunović Dunić, J., Mlinarić, S., Pavlović, I., Lepeduš, H., and Salopek‑Sondi, B. 2023. Comparative analysis of primary photosynthetic reactions assessed by OJIP kinetics in three Brassica crops after drought and recovery. Applied Sciences 13(5): 3078. https://doi.org/10.3390/app13053078.

28 . Zhang, R. H., Zhang, X. H., Camberato, J. J., and Xue, J. Q. 2015. Photosynthetic performance of maize hybrids to drought stress. Russian Journal of Plant Physiology 62: 788–796. https://doi.org/10.1134/S1021443715060187.

29 . Meng, L.‑L., Song, J.‑F., Wen, J., Zhang, J., and Wei, J.‑H. 2016. Effects of drought stress on fluorescence characteristics of photosystem II in leaves of Plectranthus scutellarioides. Photosynthetica 54(3): 414–421. https://doi.org/10.1007/s11099‑016‑0191‑0.

30 . Zhou, R., Kan, X., Chen, J., Hua, H., Li, Y., Ren, J., Feng, K., et al. 2019. Drought‑induced changes in photosynthetic electron transport in maize probed by prompt fluorescence, delayed fluorescence, P700 and cyclic electron flow signals. Environmental and Experimental Botany 158: 51–62. https://doi.org/10.1016/j.envexpbot.2018.11.005.

31 . Liu, J., Li, H. J., Guo, Y. Y., Wang, G. X., Zhang, H. J., Zhang, R. H., and Xu, W. H. 2018. Effects of drought stress on the photosynthesis in maize. Russian Journal of Plant Physiology 65: 849.

32 . Zhang, K., Chen, B‑h., Hao, Y., Yang, R., and Wang, Y‑a. 2018. Effects of short‑term heat stress on PSII and subsequent recovery for senescent leaves of Vitis vinifera L. cv. Red Globe. Journal of Integrative Agriculture 17(12): 2683–2693. https://doi.org/10.1016/S2095‑3119(18)62143‑4.

33 . Zhang, L., Hu, T., Amombo, E., Wang, G., Xie, Y., and Fu, J. 2017. The alleviation of heat damage to photosystem II and enzymatic antioxidants by exogenous spermidine in tall fescue. Frontiers in Plant Science 8: 1747. https://doi.org/10.3389/fpls.2017.01747.

34 . Fan, Q., and Jespersen, D. 2023. Assessing heat tolerance in creeping bentgrass lines based on physiological responses. Plants 12(1): 41. https://doi.org/10.3390/plants12010041.

35 . Tan, W., Meng, Q‑W., Brestič, M., Olsovská, K., and Yang, X. 2011. Photosynthesis is improved by exogenous calcium in heat‑stressed tobacco plants. Journal of Plant Physiology 168: 2063–2071. https://doi.org/10.1016/j.jplph.2011.06.009.

36 . Arslan, Ö. 2023. The role of heat acclimation in thermotolerance of chickpea cultivars: Changes in photochemical and biochemical responses. Life 13(1): 233. https://doi.org/10.3390/life13010233.

37 . Mihaljević, I., Viljevac Vuletić, M., Tomaš, V., Vuković, D., and Zdunić, Z. 2025. Characterization of heat tolerance in two apple rootstocks using chlorophyll fluorescence as a screening method. Agronomy 15(6): 1442. https://doi.org/10.3390/agronomy15061442.

38 . Kalaji, H. M., Carpentier, R., Allakhverdiev, S. I., and Bosa, K. 2012. Fluorescence parameters as early indicators of light stress in barley. Journal of Photochemistry and Photobiology B: Biology 112: 1–6. https://doi.org/10.1016/j.jphotobiol.2012.03.009.

39 . Lee, J. H., Cabahug, R. A. M., You, N. H., and Nam, S. Y. 2021. Chlorophyll fluorescence and growth evaluation of ornamental foliage plants in response to light intensity levels under continuous lighting conditions. Flower Research Journal 29(4): 320–322. https://doi.org/10.11623/frj.2021.29.3.05.

40 . Bayat, L., Arab, M., Aliniaeifard, S., Seif, M., Lastochkina, O., and Li, T. 2018. Effects of growth under different light spectra on the subsequent high light tolerance in rose plants. AoB Plants 10(5): ply052. https://doi.org/10.1093/aobpla/ply052.

41 . Ceusters, N., Valcke, R., Frans, M., Claes, J. E., Van den Ende, W., and Ceusters, J. 2019. Performance Index and PSII connectivity under drought and contrasting light regimes in the CAM orchid Phalaenopsis. Frontiers in Plant Science 10: 1012. https://doi.org/10.3389/fpls.2019.01012.

42 . Faseela, P., and Puthur, J. T. 2016. Chlorophyll a fluorescence changes in response to short‑ and long‑term high‑light stress in rice seedlings. Photosynthetica 54(3): 549–558.

43 . Faseela, P., and Puthur, J. T. 2018. The imprints of the high light and UV‑B stresses in Oryza sativa L. ‘Kanchana’ seedlings are differentially modulated. Journal of Photochemistry and Photobiology B: Biology 178: 551–559. https://doi.org/10.1016/j.jphotobiol.2017.12.009.

44 . Mlinarić, S., Antunović Dunić, J., Skendrović Babojelić, M., Cesar, V., and Lepeduš, H. 2017. Differential accumulation of photosynthetic proteins regulates diurnal photochemical adjustments of PSII in common fig (Ficus carica L.) leaves. Journal of Plant Physiology 209: 1–10. https://doi.org/10.1016/j.jplph.2016.12.002.

45 . Lu, T., Yu, H., Li, Q., Chai, L., and Jiang, W. 2019. Improving plant growth and alleviating photosynthetic inhibition and oxidative stress from low‑light stress with exogenous GR24 in tomato (Solanum lycopersicum L.) seedlings. Frontiers in Plant Science 10: 490. https://doi.org/10.3389/fpls.2019.00490.

46 . Li, L., Li, X.‑Y., Xu, X.‑W., Lin, L.‑S., Zeng, F.‑J., and Chen, F.‑L. 2013.
Assimilative branches and leaves of the desert plant Alhagi sparsifolia Shap. possess a different adaptation mechanism to shade.
Plant Physiology and Biochemistry 74: 239–245. https://doi.org/10.1016/j.plaphy.2013.11.009.

47 . Liang, Y., Chen, H., Tang, M., Yang, P., and Shen, S. 2007. Responses of Jatropha curcas seedlings to cold stress: photosynthesis‑related proteins and chlorophyll fluorescence characteristics. Physiologia Plantarum 131(3): 508–517. https://doi.org/10.1111/j.1399‑3054.2007.00974.x.

48 . Huang, X., Shi, H., Hu, Z, Liu, A., Amombo, E., Chen, L., and Fu, J. 2017. ABA is involved in regulation of cold stress response in Bermudagrass. Frontiers in Plant Science 8: 1613. https://doi.org/10.3389/fpls.2017.01613.

49 . Mazur, M., Matoša Kočar, M., Jambrović, A., Sudarić, A., Volenik, M., Duvnjak, T., and Zdunić, Z. 2024. Crop‑specific responses to cold stress and priming: Insights from chlorophyll fluorescence and spectral reflectance analysis in maize and soybean. Plants 13(9): 1204. https://doi.org/10.3390/plants13091204.

50 . Fan, J., Hu, Z., Xie, Y., Chan, Z., Chen, K., Amombo, E., Chen, L., and Fu, J. 2015. Alleviation of cold damage to photosystem II and metabolisms by melatonin in Bermudagrass. Frontiers in Plant Science 6: 925. https://doi.org/10.3389/fpls.2015.00925.

51 . Li, J., Muneer, M. A., Sun, A., Guo, Q., Wang, Y., Huang, Z., Li, W., and Zheng, C. 2023. Magnesium application improves the morphology, nutrient uptake, photosynthetic traits, and quality of tobacco (Nicotiana tabacum L.) under cold stress. Frontiers in Plant Science 14: 1078128. https://doi.org/10.3389/fpls.2023.1078128.

52 . Sun, L., Li, X., Wang, Z., Sun, Z., Zhu, X., Liu, S., Song, F., Liu, F., and Wang, Y. 2018. Cold priming induced tolerance to subsequent low temperature stress is enhanced by melatonin application during recovery in wheat. Molecules 23(5): 1091. https://doi.org/10.3390/molecules23051091.

53 . Tsimilli-Michael, M. (2020). Revisiting JIP-test: An educative review on concepts, assumptions, approximations, definitions and terminology. PHotosynthetica 58: 275-292.

How Common Bean Leaves Cope with Moderate Drought

Mon, 30 Mar 2026 10:03:46 GMT

Drought stress is a major constraint on crop productivity, but how plants spatially regulate their physiological responses to water deficit remains poorly understood. While it’s often assumed that drought increases heterogeneity in leaf function, leading to “patchy” stomatal closure and photosynthetic activity—this study challenges that assumption. Using imaging-based phenotyping, the authors reveal that moderate drought in common bean (Phaseolus vulgaris) does not intensify within-leaf heterogeneity. Instead, it promotes a spatially coherent down-regulation of photosynthesis, photochemistry, and optical properties, maintaining functional integrity despite reduced assimilation.

This study combined gas exchange measurements , chlorophyll fluorescence imaging , and multispectral imaging to analyze the spatial variability of physiological and optical traits in common bean leaves under well-watered (control) and moderate drought conditions . It focused on three leaf positions— basal, middle, and apical —to assess how drought influences spatial patterns.

Under control conditions, common bean leaves exhibited subtle spatial gradients: lower photosynthetic activity, photochemical efficiency, and pigment-related indices at the apical region compared to basal and middle segments. However, drought reduced the magnitude of these traits—net photosynthesis, stomatal conductance, and electron transport rate all declined—but did not amplify positional differences. Instead, drought diminished spatial variability, leading to a more uniform down-regulation of leaf function.

Drought reduced stomatal size and increased stomatal density, but these changes were uniform across the leaf. Leaf relative water content (RWC) declined under drought but remained spatially consistent, indicating coordinated hydraulic responses. This suggests that common bean maintains synchronized stomatal regulation even under stress, preventing pa tchy closure.

Under well-watered conditions, PLSR models strongly predicted stomatal conductance using imaging-derived traits (e.g., Fq'/Fm', rETR, NDVI). However, under drought, predictive power collapsed, likely because stomatal conductance was uniformly low, and imaging traits primarily reflected photoprotective adjustments rather than dynamic stomatal behavior.

Drought increased non-photochemical quenching (NPQ) and altered reflectance in blue, red, and near-infrared wavelengths, indicating pigment and structural adjustments. Yet, these changes were spatially uniform, further supporting the idea of coordinated acclimation rather than patchy stress responses.

The study suggests that spatial coherence in leaf responses to drought may be an adaptive mechanism, preserving hydraulic and photosynthetic integrity. This challenges the traditional view of drought-induced patchiness and highlights the need to account for spatial stability in phenotyping studies.

As climate change intensifies, understanding the hidden rules of spatial stability will be key to building resilience. The future of stress physiology lies not just in measuring what plants lose under adversity, but in uncovering how they reorganize to endure it. This work is a call to look closer, measure smarter, and rethink how we define, and harness, plant adaptability in the face of environmental challenge.

For further details, we encourage readers to explore the original publication:

Lazarević, B., et al. (2026). Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging. BMC Plant Biology. DOI doi.org/10.1186/s12870-026-08233-2

Chlorophyll Fluorescence Imaging Quantifies Stress in Weeds Under Mechanical and Chemical Control

Mon, 26 Jan 2026 09:15:33 GMT

Weeds remain one of agriculture’s most persistent challenges, competing with crops for water, nutrients, and sunlight, often leading to significant yield losses. While chemical herbicides and mechanical weeding are the most common control methods, their precise effects on weed physiology, and how weeds respond to these stresses, have not been fully explored. A recent study by Quan et al. (2023) in Frontiers in Plant Science sheds light on this issue by using chlorophyll fluorescence imaging to monitor how weeds react to mechanical and chemical damage.

The researchers focused on two common weed species: Digitaria sanguinalis (large crabgrass) and Erigeron canadensis (Canadian fleabane). Using PhenoVation’s PlantExplorer KS, a mobile chlorophyll fluorescence imaging system, they tracked changes in key photosynthetic parameters, such as Fv/Fm (maximum quantum yield of PSII) and ETR (electron transport rate), to assess how different stress treatments affected weed health.

One of the most striking findings was the site-specific impact of chemical stress. When the herbicide glufosinate was applied to different parts of the weeds, the leaf underside (abaxial surface) showed the most severe damage, with a photosynthetic inhibition rate (R) of 75% after seven days. This suggests that herbicides penetrate more effectively through the leaf underside, possibly due to differences in cuticle structure or stomatal distribution. The study also noted that Erigeron canadensis, with its dense leaf trichomes, exhibited a slower response to chemical stress compared to Digitaria sanguinalis, highlighting the importance of species-specific responses in weed management.

Mechanical stress, simulated by scratching the leaves to mimic weeding machinery, resulted in a short-term decline in photosynthetic efficiency, followed by partial recovery within one to two days. The photosynthetic inhibition rate (R) for severe mechanical stress was 11%, significantly lower than that of chemical stress. Unlike chemical damage, which is often irreversible, mechanical stress triggered a transient physiological response, with parameters like Fv/Fm and ETR initially dropping but beginning to recover after 48 hours. However, severe mechanical damage—affecting more than 50% of the leaf area—led to more pronounced and lasting effects, indicating that the degree of injury plays a critical role in weed survival.

Fluorescence images of Fv/Fm parameters were captured daily during the experimental period, and the changes of Fv/Fm were monitored continuously. (A, B) shows the image changes of Fv/Fm in Erigeron canadensis with different levels of mechanical stress, (C, D) shows changes of Fv/Fm in Digitaria sanguinalis with different levels of mechanical stress.

The study’s most intriguing finding was the effect of combined mechanical and chemical stress. When weeds were subjected to both types of damage simultaneously, the photosynthetic inhibition rate (R) reached 71–73% after just three to four days—a level of damage comparable to seven days of chemical stress alone. This suggests that mechanical damage enhances herbicide efficacy by breaking the leaf cuticle, allowing chemicals to penetrate more deeply and act more rapidly. This synergistic effect underscores the potential of integrated weed management strategies that combine mechanical and chemical methods for more efficient control.

The implications of this research are significant for agricultural practices. By understanding how weeds respond to different types of stress, farmers can optimize their control strategies—whether through targeted herbicide application, mechanical weeding as a complementary tool, or species-specific approaches that account for variations in leaf morphology. Chlorophyll fluorescence imaging provides a real-time, non-destructive method for assessing weed stress, allowing for dynamic adjustments in weed management.

In conclusion, the study by Quan et al. (2023) highlights the value of chlorophyll fluorescence imaging in understanding and optimizing weed control. By revealing the hidden physiological responses of weeds to mechanical and chemical stresses, this research offers a scientific foundation for developing more effective, sustainable, and precise weed management practices.

For further details, we encourage readers to explore the original publication:

Quan, L., et al. (2023). Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging. Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1188981

Chlorophyll Fluorescence measuring methods: OJIP

Wed, 17 Dec 2025 13:57:38 GMT

Chlorophyll fluorescence is one of the most popular technologies for fast non-invasive measurements of photosynthetic efficiency, which is used to get a better understanding of plants and how they react to their environment. It is very useful for quickly screening plants, like when breeding for a new cultivar, or testing the effect of a new product. Also in high-tech greenhouses, direct feedback from plants proves to be crucial for steering the climate and lighting. Other fields are also increasingly implementing chlorophyll fluorescence technologies, such as ecology, forestry and arable farming, using drones and satellites (making use of solar-induced fluorescence: SIF).

The CF2GO and PlantExplorer systems from PhenoVation measure chlorophyll fluorescence via the PAM or OJIP protocol. With PAM, we measure two distinct states of chlorophyll fluorescence during the protocol: minimum and maximum fluorescence. To do this, modulated measuring light pulses are given to the plants to obtain fluorescence signal (see the in-depth blog on the PAM protocol for more information). The difference between minimum and maximum fluorescence gives a measure of how efficiently the plant transforms light energy into chemical energy.

The OJIP protocol also measures minimum and maximum fluorescence, but zooms in specifically on this rise to maximum. In literature, this is called ‘Kautsky Chlorophyll Fluorescence Induction Kinetics’. The rise might seem simple, but it carries a surprising amount of information about how the plant is functioning and processing energy, which has been studied thoroughly over the past century by fundamental scientists.

One particularly influential model explaining the Kautsky effect was developed by Strasser and his colleagues (Strasser et al., 1995), forming the foundation of the OJIP protocol. The OJIP protocol is also deployed in the CF2GO systems, and I have briefly touched upon it in a previous blog. However, since many important and sensitive parameters are derived from OJIP, and given its complexity, I’ve decided to dedicate a full post to it. Here, I will dive into this theoretical model from Strasser. In a second part, I will go into the measured parameters.

The OJIP rise according to Strasser 

During the OJIP measurement, an extremely high-light flash is given to the plants for 1 second. A camera with a specialized sensor (highly sensitive since fluorescence is a weak signal) and filter, captures the fluorescence rise coming from the plant.

This rise does not go up in a linear way. When plotted on a logarithmic time scale, the following pattern is visible:

As seen, the fluorescence level rises in three steps (Strasser called it ‘triphasic kinetics’). These steps are called the J, I and P plateau. The first plateau, “J”, appears within 3 milliseconds, followed by the ‘I’ plateau at approximately 33 milliseconds. Finally, the “P” plateau marks the ‘peak’ or maximum fluorescence, usually after around 300 to 800 milliseconds.

These steps do not appear without reason; they appear because we are following the first initial charge separations (transfer of electrons from a donor to an acceptor) and reactions in the photosynthetic apparatus, which I will explain, according to Strasser’s model, in the next part.

O-J step

The moment light energy is absorbed by the antenna pigments of a photosystem, almost instantly (within femtoseconds) the chlorophyll around the reaction center becomes ‘excited’ with this charge. An electron, introduced into the system by the splitting of water, can carry this energy.
For the charged electron to move into the chain, the first electron acceptor in Photosystem II, called QA, must be in an ‘open’ state (‘free of electrons’). The moment QA accepts a charged electron, a small fraction of the excitation energy is released as fluorescence, which we detect as the minimal fluorescence, or origin fluorescence (Fo) . This process happens very quickly; Fo is measured after just 0.05 milliseconds.

When QA is ‘occupied’ by an electron, it cannot accept another one until the first electron passes to the next electron carrier, called QB. However, excitation of chlorophyll is a faster process than the passing of electrons, which means the absorbed light energy cannot enter the electron transport chain due to QA being still occupied. Because of this, the excited chlorophyll returns to its ground (uncharged) state by releasing the excess energy as fluorescence (one of the three possible fates of energy). This results in an increase in fluorescence, which is observed as the rise from O to J in the fluorescence induction curve.

J-I step

At the time the electron is transferred from QA to QB (the second electron carrier in PSII), another electron can be accepted by QA. This means light energy can be used for photochemistry again, and as a result, less energy is released as fluorescence (remember the three possible fates of absorbed light). The movement of electrons from QA to QB therefore leads to a temporary stabilization of the fluorescence level, as the rate of QA closing equals the rate of QA reopening. This stabilization becomes visible after roughly 3 milliseconds, where fluorescence reaches a steady level, known as the J plateau.

The second electron carrier, QB, requires two electrons before it passes them on to the next electron acceptor. Once QB receives a second electron from QA, it takes up two protons (H ⁺ ) from the stroma and becomes plastoquinol (PQH ₂ ). PQH ₂ then leaves photosystem II and diffuses into the plastoquinone (PQ) pool within the thylakoid membrane.

Eventually, the cytochrome b6f complex accepts these electrons. However, cytochrome b6f can only accept and process electrons from PQH ₂ at a finite rate, creating a bottleneck.

Because of the limitations of cytochrome b6f, PQH ₂ accumulates in the pool, which slows down the flow of electrons. QA cannot pass on new electrons because QB is still occupied. As a result, light energy cannot enter the electron transport chain, causing the excited chlorophyll to fall back to the ground state, releasing energy as fluorescence again. This causes the fluorescence level to rise, which is observed as the transition from J to I.

I-P step

Further downstream in the electron transport chain, electrons start to be accepted by Plastocyanin (PC) and Photosystem I (PSI). PSI uses these electrons, along with newly absorbed light energy to drive the formation of NADPH, a key product of the light reaction of photosynthesis.

As the electron flow continues, new electrons can enter the electron transport chain from Photosystem II. This means that light energy is again used for photochemistry, rather than being released as fluorescence (energy fates), resulting in a stabilization of the fluorescence level, the I plateau.

However, like earlier stages, PC and PSI require some time to reach full efficiency. This delay creates again a bottleneck further downstream in the chain. Under the conditions of the extreme high light intensity (during the 1 second pulse), the whole system is now saturated, meaning all electron carriers are ‘closed’ and unable to accept more electrons.

When this happens, light energy cannot be transferred into the electron transport chain and the excited chlorophyll again falls back to the ground state, releasing energy as fluorescence. This leads to the final rise in fluorescence, reaching the maximum level (Fm) , observed as the P peak in the OJIP curve.

What can we do with this?

As you can see, during the 1-second light pulse, many processes can be followed. From these plateaus and the shape of the curve, the functioning or efficiency of the system can be quantified. Strasser and his colleagues, and other researchers, created more than 25 formulas that allow us to calculate how the plant’s photosynthetic apparatus is functioning. These derived parameters range from an overall efficiency of the system to zoomed into individual processes , for instance:

• Light absorption- and trapping parameters

(antenna size, trapping flux per center, first electron acceptor efficiency)

• Reaction center parameters

(# of reaction centers per chlorophyll, # of open reaction centers, efficiency per center)

• Electron transport parameters

(Electron transport flux per center, efficiency of transport to different electron acceptors)

• Energy conversion parameters

(Dissipation flux, photochemical flux)

Stress (such as drought stress, heat stress, salt stress, or pests and diseases) have a huge effect on the functioning of the system, and therefore can be picked from these measurements. Also modes of actions of products can be quantified.

In part 2 of the OJIP blog, I will focus on the parameters that are calculated from the OJIP rise, what they mean, and summarize the effects of stress on them.

References

• Strasser, R.J., Srivastava, A. And Govindjee (1995). Polyphasic chlorophyll a fluorescence transient in plants and cyanobacteria. Photochemistry and Photobiology 61: 32-42.

• Strasser, R.J., Srivastava, A. And Tsimilli-Michael, M. (2000). The fluorescence transient as a tool to characterize and screen photosynthetic samples. In: Ynus, M. (Ed.) Probing Photosynthesis: Mechanisms, Regulation and Adaptation. Taylor and Francis, London: 445-483.

• Strasser, R.J., Tsimilli-Micael, M. and Srivastava, A. (2004). Analysis of the Chlorophyll a fluorescence transient. In: Papageorgiou, G.C. and Govindjee (Eds.) Chlorophyll a fluorescence: a signature of photosynthesis. Springer, Dordrecht: 321-362.

• Tsimilli-Michael, M. (2020). Revisiting JIP-test: An educative review on concepts, assumptions, approximations, definitions and terminology. PHotosynthetica 58: 275-292.

Unlocking the Secrets of Quantitative Disease Resistance

Thu, 04 Dec 2025 18:22:05 GMT

Temporal Regulation of WRKY6 in Solanum pennellii

Quantitative Disease Resistance (QDR) provides durable, broad-spectrum protection against pathogens like Sclerotinia sclerotiorum , a necrotrophic fungus infecting over 400 plant species. Unlike qualitative resistance, QDR is polygenic, offering partial but sustainable resistance under field conditions. Wild tomato relatives, such as Solanum pennellii , exhibit natural variation in QDR, making them ideal models for studying the regulatory timing of defense responses. While genetic loci associated with QDR have been identified, the temporal dynamics of gene expression during early infection remain poorly understood. Einspanier et al. (2025) investigated how early activation of defense genes, particularly the transcription factor WRKY6, influences QDR in S. pennellii challenged by S. sclerotiorum.

Three genetically distinct accessions of the wild tomato relative Solanum pennellii were selected based on their contrasting lag phase durations, making them ideal models for dissecting the temporal dynamics of defense responses:

LA1282: Exhibits a prolonged lag phase (56.88 hours), representing a high-QDR genotype.
LA1941: Displays an intermediate lag phase (49.92 hours), serving as a moderate-QDR genotype.
LA1809: Characterized by a short lag phase (38.16 hours), representing a low-QDR genotype.

Test inoculum was prepared as a standardized fungal suspension of Sclerotinia sclerotiorum isolate 1980, cultivated from sclerotia on Potato Dextrose Agar (PDA) and incubated in Potato Dextrose Broth (PDB). The suspension was adjusted to an optical density (OD₆₀₀) of 1.0 and supplemented with Tween-80 to ensure uniform dispersion. Small droplets (10 μL) of the fungal suspension were applied to the center of detached Solanum pennellii leaves. Mock treatments consisted of PDB without fungal material, serving as negative controls.

Plants were cultivated under controlled environmental conditions (21°C, 65% relative humidity, 16-hour photoperiod) to ensure reproducibility and minimize environmental variability. This setup allowed for precise monitoring of disease progression and gene expression dynamics in response to S. sclerotiorum infection. The screening system consisted of a PlantExplorer Pro+ , providing high-resolution Fv/Fm (chlorophyll fluorescence) imaging to monitor photosynthetic stress every 12 hours post-inoculation (hpi), and loss of photosynthetic efficiency as an indicator of pathogen impact. A Navautron Automated Lesion Tracker was used to record lesion expansion and to track disease progression.

Resistant genotypes (e.g., LA1282) exhibited a prolonged lag phase (56.88 hours), delayed photosynthetic stress, and sustained Fv/Fm values, while susceptible genotypes (e.g., LA1809) showed rapid lesion expansion and early decline in photosynthetic efficiency. Transcriptomic analysis identified 71 differentially expressed genes (DEGs) in the resistant genotype at 48 hours post-inoculation (hpi),

including pathogenesis-related (PR) genes, oxidative stress response genes (e.g., DOX1, LOX1), and transcription factors (e.g., WRKY6). WRKY6 emerged as a central regulator, with elevated basal expression in resistant genotypes, suggesting a primed defense state. Gene Set Enrichment Analysis (GSEA) further confirmed the activation of defense-related processes.

Conclusion

The study demonstrates that quantitative disease resistance (QDR) in Solanum pennellii against Sclerotinia sclerotiorum is driven by temporal shifts in gene expression, particularly during the lag phase of infection. The researchers identified WRKY6 as a central regulator of defense-related genes, with elevated basal expression in resistant genotypes contributing to delayed lesion onset. These findings underscore the importance of early defense activation and genotype-specific regulatory dynamics in shaping QDR, ultimately enhancing the plant’s ability to delay pathogen invasion.

For further details, please read the original publication:

Einspanier, S., et al. (2025). Temporal Shifts in Gene Expression Drive Quantitative Resistance to a Necrotrophic Fungus in a Tomato Crop Wild Relative. BioRxiv. DOI: 10.1101/2025.01.01.522345

Exploring the Impact of UV-A Light on Basil (Ocimum basilicum L.)

Thu, 27 Nov 2025 10:02:36 GMT

The Role of UV-A Light in Plant Physiology

Light is a fundamental environmental factor that influences plant growth, development, and stress responses. While the effects of ultraviolet (UV) radiation on plants have been extensively studied, the specific impacts of UV-A light (315–400 nm), particularly its wavelength and intensity, remain less understood. UV-A radiation is known to influence various plant processes, including photosynthesis, photomorphogenesis, and secondary metabolite production. Unlike UV-B, which primarily induces stress responses, UV-A can act as a photoregulatory signal, modulating plant growth and development. Recent advancements in LED technology and high-throughput phenotyping have opened new avenues for investigating how UV-A radiation affects plant physiology, morphology, and biochemical composition. These effects of UV-A are highly species-specific, dose-dependent, and influenced by environmental conditions.

A study conducted by Vodnik et al. (2023), examines the effects of supplemental UV-A light of different wavelengths (365 nm and 385 nm) and intensities on basil ( Ocimum basilicum L. ). It combines conventional physiological measurements, biochemical analyses, and high-throughput phenotyping to provide a comprehensive understanding of basil’s response to UV-A radiation. Four treatments combine baseline red–blue LEDs with UV-A at 365 nm, 385 nm, or both, at total intensities ranging from 3.5 to 16 W m⁻² (E1–E4). Plant traits are assessed using 3D multispectral scanning, chlorophyll fluorescence imaging using the CropReporter , and biochemical analyses of pigments and phenolic compounds.

Basil showed high tolerance to UV-A. across all treatments, including the highest intensity. No negative effects were detected in biomass, plant height, leaf area, or digital volume. Morphological development remained consistent with the control lighting conditions. UV-A did not reduce photosynthetic efficiency. Higher UV-A doses yielded increased effective quantum yield (Fq'/Fm') and electron transport rate (ETR), indicating improved photochemical performance. The rise in xanthophyll cycle pigments (VAZ) and VAZ-to-chlorophyll ratios suggested activation of photoprotective mechanisms that help prevent photoinhibition.

Although anthocyanin accumulation was not observed, the Anthocyanin Index increased under UV-A, likely reflecting structural or spectral changes. UV-A exposure induced a dose-dependent rise in hydroxycinnamic acid derivatives, such as rosmarinic acid-glucoside and caffeic acid-glucoside, which act as UV screens and antioxidants. Glycosylation enhances their stability and strengthens basil’s oxidative stress defense. Basil’s UV-A tolerance appears to rely on enhanced thylakoid electron transport, increased photoprotective pigments, and the upregulation of antioxidant phenolics. Together, these mechanisms maintain photosynthetic efficiency and mitigate UV-induced stress.

The findings indicate that UV-A can be safely integrated into LED lighting strategies to improve plant resilience and increase valuable secondary metabolites. High-throughput phenotyping tools such as multispectral 3D scanning and chlorophyll fluorescence imaging support precise monitoring and optimization of light regimes in indoor farming systems.

Conclusion

This study demonstrates that basil exhibits remarkable tolerance to UV-A radiation, thanks to a combination of enhanced photosynthetic efficiency, protective pigment accumulation, and metabolic adaptation. The integration of high-throughput phenotyping with conventional physiological and biochemical analyses provides a holistic understanding of UV-A responses, paving the way for innovative lighting strategies in horticulture.

For further details, please explore the original publication:

Vodnik, D., et al. (2023). Phenotyping of basil (Ocimum basilicum L.) illuminated with UV-A light of different wavelengths and intensities. Scientia Horticulturae. DOI: 10.1016/j.scienta.2022.111638

High-throughput Phenotyping towards Herbicide discovery

Thu, 20 Nov 2025 12:44:17 GMT

The Role of Chlorophyll Fluorescence in Herbicide Screening

The global challenge of herbicide resistance, coupled with environmental concerns, has intensified the demand for innovative, sustainable, and effective herbicides. Traditional herbicide discovery methods are often slow, resource-intensive, and environmentally taxing. Multichannel plant imaging, for example chlorophyll fluorescence imaging, can offer a robust indicator of plant health and stress responses. Plant imaging offers rapid, quantitative, and high-throughput screening of novel herbicidal compounds. This approach not only accelerates the identification of promising candidates but also minimizes environmental impact by reducing the need for extensive field trials.

Chlorophyll fluorescence , particularly the Fv/Fm parameter, quantifies the maximum quantum efficiency of photosystem II (PSII). Herbicides that disrupt photosynthesis induce a measurable decline in Fv/Fm, making it a powerful early marker of herbicidal activity. This method is highly sensitive, non-destructive, and capable of detecting stress responses before visible symptoms appear.

A Case Study

A recent study conducted at Ghent University (Backx et al., 2024; Backx et al., 2025), using the CropReporter system, demonstrated the efficacy of plant imaging in screening novel herbicidal compounds. The researchers focused on 3-acyltetramic acids and their prodrugs, which are biologically derived and show promise as sustainable herbicides. Leaf disks were obtained from fully developed leaves of tomato plants ( Solanum lycopersicum ‘Moneymaker’ L. ), cultivated under controlled greenhouse conditions (21 °C, LED lighting, and regular fertilization). Standardized disks (0.6 cm or 1.1 cm diameter) were punched from leaves and placed in 24- or 96-well plates containing sterile distilled water for a destressing period prior to treatment. Fv/Fm measurements were taken at 24, 48, 72, 96, and 144 hours post-treatment. IC₅₀ values (the concentration required to reduce Fv/Fm by 50%) were calculated using four-parameter log-logistic models.

Key Findings

The study demonstrated that Fv/Fm imaging provides a fast, reliable, and quantitative method for herbicide screening . Compounds inducing strong reductions in Fv/Fm corresponded with high herbicidal activity, validating the use of this parameter as a proxy for phytotoxicity. The approach also enabled early detection of herbicidal effects with minimal compound use, quantitative potency ranking, and structure-activity analysis.

While in vitro assays provide valuable insights, translating these results to field conditions requires careful consideration. Some prodrugs, which showed promising activity in leaf disk assays, did not outperform the original compound in spray tests on Amaranthus retroflexus seedlings. Targeted field validations are still needed to ensure real-world efficacy. However, high-throughput phenotyping does reduce the time and cost associated with herbicide development, enabling researchers to focus on the most promising candidates. And, by minimizing the use of compounds and reducing reliance on field trials, the approach aligns with the principles of sustainable agriculture. The integration of phenotyping data with other omics technologies (e.g., genomics, metabolomics) can further refine the understanding of herbicide modes of action and resistance mechanisms.

Conclusion

B y using multispectral plant imaging parameters in herbicide d iscovery as a quantitative proxy for plant health, researchers can rapidly screen novel compounds, optimize their efficacy, and reduce environmental impact. As demonstrated by the work at Ghent University, this approach not only benefits early-phase herbicide discovery. It may also pave the way for more sustainable and effective weed management strategies.

For further details, please explore the original research publications:

Backx, S., et al. (2024). Synthesis of Mixed Phosphonate Esters and Amino Acid-Based Phosphonamidates, and Their Screening as Herbicides. International Journal of Molecular Sciences. DOI: 10.3390/ijms25094739
Backx, S., et al. (2025). Synthesis and Herbicidal Assessment of 3-Acyltetramic Acid Prodrugs. ACS Omega. DOI: 10.1021/acsomega.5c02851

The New CF2GO

Thu, 10 Jul 2025 12:39:51 GMT

In June, the CF2GO (Chlorophyll Fluorescence 2 Go) was officially launched at the Greentech conference in Amsterdam in the Netherlands, and was nominated out of 47 products for the innovation award by the jury!

The CF2GO is our newest camera system capable of measuring real-time efficiency of photosynthesis and plant stress from a distance by capturing chlorophyll fluorescence signals. Its ability to operate 1 meter away from the plant with high accuracy sets it apart from traditional sensors, that often work with leaf clippers. With the CF2GO, we bring the same scientific quality and accuracy as our other systems to the greenhouse.

The information that lies in chlorophyll fluorescence has been studied over the last 80 years at universities and research groups. The past ~20 years, this fundamental research has been implemented in practical applications that are used in agriculture (mainly by large breeding companies, multinationals and universities). The integration of this technique into a camera system with a small body that can measure from afar is novel, and marks a new step in the usage of photosynthesis sensors as it makes its way into greenhouses.

With real-time data on efficiency of photosynthesis and stress indicators, deeper insights into crop performance can be obtained, enabling more informed decisions in cultivation practices. The camera can be mounted either stationary, or on for instance a robotic arm that moves throughout the greenhouse.

The CF2GO is already operational in four commercial greenhouses (two for validation and fine-tuning of the product). In the magazine 'In Greenhouses', or 'Onder Glas' in Dutch, cultivation manager Johan Langelaan from Maarel Orchids explains why they started integrating the CF2GO in their cultivation. He explains: "We can now see in real-time when the plants require more or less light for photosynthesis, allowing us to adjust screening and lighting immediately. We can do this without fear of potential damage from too much or too little light. This way, we get more out of the crop and can grow with greater confidence". Another important advantage they mention is that no interpretation is required: “because you get hard facts and measured values from the system, and you can immediately see what they mean”.
Maarel Orchids is now actively working on integrating the measurements with the climate computer to automate lighting and shading controls, which is something that is mostly done manually still.
The article can be found at: https://www.onderglas.nl/gewas-krijgt-exact-de-hoeveelheid-licht-die-het-nodig-heeft/

In this blog, I will explore the technical specifications of the CF2GO and how it captures its measurements. I will also showcase data from experiments and real-world examples from greenhouses, demonstrating what photosynthesis and stress data look like and how this information can be used to optimize cultivation practices.

Figure 1: Two of the CF2GO's mounted in two different commercial greenhouses.

Technical specifications CF2GO

Hardware - build quality

The CF2GO is constructed from a solid aluminium frame, in which all the components (light sources, camera, filter, sensor, computer) are integrated in an air- and dust free environment. The system is cooled by thermal bridges that protect the critical components from dust and moist. The housing frame is made from powder coated aluminium, which can withstand the fluctuating environmental conditions in the greenhouse, making it very durable.

Software

The control software runs on the internal computer to capture different images using pre-installed protocols. The gathered data is transmitted to MyLegnd for interpretation (see next section). The software also has a remote-control feature, enabling integration with other systems, such as phenotyping platforms. Additionally, data can be accessed via an Application Programming Interface (API), allowing for the development of customizable automated programs (for example for direct communication with a climate computer).

Data interpretation

The data collected by CF2GO is integrated into the MyLegnd user interface (Figure 2), where the values can be observed simultaneously with other vital parameters from other sensors to get a comprehensive overview of important greenhouse data. These other parameters include PAR light intensity, temperature, humidity, CO₂ concentration, transpiration, light response curves, and more.

Later in this blog, I will highlight some of the key parameters provided by the CF2GO.
If needed, data and graphs can be downloaded for further personal use. Additionally, datasets can be stored locally on the CF2GO's internal computer for up to 5 years .

Calibration

To ensure reliable and accurate data, the CF2GO systems are carefully calibrated before shipping. Calibration is essential for all electronic measuring devices to minimize electronic noise and reduce variability between units, regardless of the type of measurement.

In experiments testing the CF2GO systems (Figure 3), we observed that the variation between the devices after calibration was extremely minimal: 0.22% difference, with a correlation coefficient (R²) of 0.9989 (Figure 4).

Theory

Figure 5 shows the pathway of sunlight at the leaf. After absorption of light in the leaf, much of the energy is directed toward photochemistry (1), but not all. A portion of the energy is dissipated as heat (2) or emitted as fluorescence (3). These three possible pathways always happen simultaneously, and are in direct competition with each other, meaning an increase in one results in a decrease in the other. This phenomenon allows us to calculate photosynthetic efficiency, and the other energy pathways.

The CF2GO measures photosynthesis and stress using a protocol known as the fast chlorophyll a fluorescence rise (also known as the OJIP method). This method records chlorophyll fluorescence over the course of a single 1-second light flash.

Fast chlorophyll fluorescence rise (OJIP measurement)

The light flash during the protocol has a very high blue light intensity of at least 6000 μmol m ^-2 s ^-1 at 100 cm distance. This intense light (3 times the intensity of the sun) is required to fully saturate the plant’s photosystems (see introduction blog), a critical step for capturing accurate and reproducible fluorescence signals and calculating key photosynthetic parameters.

During the 1-second light pulse, fluorescence signals from the plant rise, and the camera tracks this increase with the 1000 frames it takes within this 1 second. The resulting graph produces a characteristic double-S-shaped curve with two distinct plateaus during the rise, termed the J plateau and I plateau—hence the name OJIP (Figure 6).

The “O” step represents the “origin”. The “J” step appears within 2 milliseconds, followed by the “I” step at approximately 30 milliseconds, and finally, the “P” step marks the “peak” or maximum fluorescence (when all photosystems are closed due to high light energy, see figure 7).

The difference between minimal and maximal fluorescence provides information about the functioning of the photosystems of the plant (photosynthesis), and the plateaus in the curve reflect specific electron flow dynamics of in the photosynthetic process.

Tracking these processes provides insights into the photochemistry and energy pathways occurring within the plant. These processes are highly sensitive to environmental and biological stresses, such as water stress, heat stress, nutrient deficiencies, pests, or diseases. This sensitivity makes the OJIP protocol an effective tool not only for understanding photosynthesis but also for detecting plant stress early (before anything can be seen on the plant by eye).

For more in depth information about the OJIP method, or the specific proteins within the thylakoid membrane where these processes happen, the following papers can give nice insights:

Key parameters measured by the CF2GO, and the effect of stress

Efficiency of Photosynthesis

This parameter measures the efficiency of the PSII-photosystems, which are driving photosynthesis (see introduction blog). It represents the fraction of the light energy absorbed that is used to drive photosynthesis. (Sudden) reductions in the efficiency of photosynthesis can be seen as a sign of stress.

A healthy plant has a stable potential photosynthesis efficiency during the night, typically around 0.75-0.84. These values in the night represent the plant’s potential maximal capacity to use light for photosynthesis when all processes are fully relaxed, and all photosystems are open (dark adapted).

During the day, efficiency of photosynthesis values tend to drop. This is because the amount of light energy captured by the plant is more than what it can use for photosynthesis. To prevent light damage, some of that energy is actively released by the plant as heat (called non-photochemical quenching) which, as an effect, lowers the overall efficiency of photosynthesis during the daytime (remember the three possible pathways of energy).

In figure 8, the efficiency of photosynthesis is tracked on a soybean plant that had been sprayed with glyphosate (herbicide). The second day after spraying, a drop in the values can be seen (from 0.6 to 0.4 during the day, and from 0.75 to 0.65 during the night). While this happened, visual symptoms could not be seen on the plant. Visual signs of stress, such as changes in color, appeared only after 4 days (in this case shown as decrease in HUE, indicating yellowing of the leaves; figure 9). Of course, glyphosate is specifically designed to kill plants quickly, making death inevitable. However, in the case of other stressors, such early detection could provide a critical window for intervention, preventing stress or damage and increasing yields.

NPQ(t)

Not all light energy can be used by the plant for photosynthesis, which forms a problem for plants because the energy has to go somewhere. Uncontrollable energy can react with molecular oxygen, generating an excited state of oxygen known as 'reactive oxygen species (ROS), which is very harmful for plants and causes cellular damage. Hence, plants have developed protection mechanisms to actively manage the energy dissipation, called Non-Photochemical Quenching (NPQ). NPQ is the process of active controlled discarding of the excess energy as heat to avoid damage in the photosynthetic apparatus.

NPQ is a dynamic process and fluctuates based on the light intensity and the health of the plant. NPQ is always present under light conditions. However, (sudden) strong increases in NPQ values in combination with dropping photosynthetic efficiency values points to stress. This response suggests that the plant is absorbing more light energy than it can use for photosynthesis, and is activating its protective mechanisms to prevent damage.

In figure 10, the plant that had been sprayed with glyphosate showed increasing NPQ(t) values.

Electron Transport Rate (ETR)

provides an estimate of the speed at which electrons are transferred in the electron transport chain of the photosynthetic apparatus. This value is positively correlated with the photosynthetic activity of a plant and CO2 assimilation (when the stomata are open) and is a widely used parameter for gaining insight into the plant’s processes and stress responses.

An important advantage of ETR compared to the photosynthesis efficiency value is that it also takes light level (PAR) and light absorption into account in the calculation. This provides a better understanding of the actual photochemical activity in relation to (fluctuating) environmental factors.

The higher the ETR value, the faster the electrons are transferred in the electron transport chain. Also here, sudden drops in ETR, especially under constant light conditions, can point to stress.

On the second day after spraying glyphosate on the soybeans, a significant drop in ETR was observed (Figure 11).

Deviation ETR

is an indicator that shows how far the measured ETR deviates from the theoretical maximum that could be achieved under ideal, non-limiting conditions. The theoretical maximum is a value that increases linearly (Figure 12). However, in practice, the ETR can never increase linearly indefinitely because factors like CO2 uptake become limiting, and the plant becomes saturated with light. The greater the deviation from the theoretical maximum, the less efficiently the plant uses the light at that specific intensity.

Typically, a threshold value is determined, which is crop-specific, within which the values should fall.

A sudden rise in ETR deviation indicates that something is wrong. In stressed plants, the ETR deviation becomes significantly larger because stress negatively affects the speed of electron transport, causing the measured ETR to remain lower than theoretically possible at that light level (and thus increasing the deviation).

Experiment with Phalaenopsis plants (CAM) in a controlled environment

To understand how the CF2GO measurements can be used, we conducted experiments on different plants. One of these experiments was with phalaenopsis plants.

Phalaenopsis is a CAM plant, which absorbs CO₂ at night and stores it as malate. During the day, the plant closes its stomata and the stored malate is used to release the trapped CO₂, which is then utilized to sustain photosynthesis. A key challenge for growers is to apply optimal lighting according to the malate release. As phalaenopsis is a shadow plant, too much light can induce damage (like leaf yellowing), and too little influences yield.

In an experiment where we kept the light levels constant, we observed a decline in photosynthetic efficiency at the end of the light period (Figure 13). We believe that this reduction was due to the depletion of malate, leading to insufficient CO ₂ availability within the plant to run photosynthesis.
Since photosynthesis was less efficient in the afternoon, providing full light was wasteful, and potentially harmful for the plants. Therefore, we reduced the LED light intensity in steps during this time and, as hypothesized, observed an increase in photosynthetic efficiency.

Examples from the greenhouse

In a greenhouse with phalaenopsis, we saw the same effect as the controlled experiment: a slight drop in efficiency of photosynthesis in the afternoon (Figure 14). Consistent with the drop in efficiency of photosynthesis, the deviation ETR increased. One hour before the drop in efficiency, the time it took to reach maximum fluorescence was already decreasing. Based on these values, the grower takes action by screening more or scaling down the supplemental lighting.

In this greenhouse, they also work with the parameter deviation ETR (Figure 15). They set a threshold of 30%. On several occasions, the deviation ETR peaked above the threshold (see red arrows). This was likely caused by sudden full sunlight reaching the plants on a partly cloudy day, where the shading screens were not closed. This is critical for phalaenopsis plants, as the high-light intensity can damage the plants. The changing conditions during a partly cloudy day are challenging to manage, because they occur quickly and last for a short duration. Sometimes, the stress that this gives can persist for several hours or even days, because of the photodamage to the PSII systems that the plant needs to repair.

In another greenhouse, this time with potted plants, the efficiency of photosynthesis was plotted alongside DLI and ETR in the MyLegnd interface (Figure 16). From this data, we saw that the cultivation was going well, according to the stable efficiency of photosynthesis levels.
On May 31, the ETR was at its highest point. On this day, a significant amount of light was available (high DLI), and photosynthesis was also efficient. Furthermore, this was the hottest day, but the conditions were not stressful for the plants. The increase in temperature likely allowed the plant processes to occur more quickly and efficiently.

This information gives the grower a better understanding of what his plants can handle and when conditions become stressful. The following day, on June 1st, the DLI was lower, but the ETR remained high. The day after, the DLI was much lower, which also resulted in a low ETR.

Introduction to Chlorophyll Fluorescence

Wed, 14 May 2025 10:22:30 GMT

What is chlorophyll fluorescence?

In agriculture and horticulture, optimizing photosynthesis and preventing stress (e.g. ensuring adequate water supply, necessary nutrients, and favorable conditions) are essential for maximizing crop yield. Moreover, efficient light capture and energy conversion are key to healthy plant growth and high productivity. Technological advancements now enable growers and researchers to quantify and monitor plant processes, for instance photosynthetic activity, stomatal conductance, and transpiration, with greater precision, providing insights into plant functioning that were previously difficult to achieve. One particularly influential technology in plant sciences is the analysis of chlorophyll fluorescence.

But what exactly is this fluorescence signal, and why is it used so often in plant science?

When a plant is illuminated, chlorophyll molecules—particularly the photosystems within—absorb light. While most of this energy is used for photosynthesis, some is dissipated as heat, or re-emitted as fluorescence (Figure 1). Unlike reflected light, fluorescence is emitted by the plant itself, effectively making the plant 'giving off' light (Figure 2).

Chlorophyll fluorescence is emitted at relatively long wavelengths at the edge of the visible light spectrum, with peaks of emission at 680 and 730 nm (Figure 3). It originates from the plant's photosystems, which are located in the thylakoid membrane of the chlorophyll molecules. The photosystems are involved in capturing light, and driving photosynthesis.

Basis of Chlorophyll Fluorescence Measurements

To understand how chlorophyll fluorescence analysis works, we need to explore the basis of photosynthesis.

As seen, fluorescence is emitted from the chlorophyll molecules, specifically the protein complexes known as photosystems I and II, which are embedded in the thylakoid membranes (Figure 4).

Photosynthesis processes are split in two phases:
• The light phase
• The dark phase (also called Calvin cycle)

When we are looking at chlorophyll fluorescence, we are looking at the light dependent phase of photosynthesis.

During the light-dependent phase, energy from light is converted into chemical energy, as a way to store it. When the photosystems capture light through their antennae, their reaction centers become excited. This energy must be effectively managed by the plant, otherwise it can react with molecular oxygen, generating an excited state of oxygen known as 'reactive oxygen species' or ROS, which are harmful and can cause cellular damage. Hence, plant processes to safely direct the energy operate at high speeds. The transfer of energy from excited-state chlorophyll to photochemistry or heat dissipation is referred to as quenching .

The light energy captured by the photosystems is used to carry electrons through the electron transport chain across the thylakoid membrane (Figure 5). The process establishes a hydrogen proton gradient, which powers the enzyme ATP synthase to produce ATP. Simultaneously, light energy enables the formation of NADPH. The energy stored in ATP and NADPH is subsequently utilized in the Calvin cycle (dark phase) to fix CO₂ and synthesize glucose.

This entire process occurs at a specific rate (though a fast one), which limits the proportion of absorbed light energy that can be allocated exclusively to photosynthesis. As previously mentioned, some of this energy is dissipated through other pathways: as heat or fluorescence.

Energy dissipation through heat and fluorescence happens continuously (Figure 6). To prevent photo-damage, the plant tries to actively regulate this energy release via a process known as non-photochemical quenching (NPQ). NPQ represents the dissipation as heat, in a controlled way. Additionally, the excited chlorophyll molecules release some of the absorbed energy as photons, which is the fluorescence. Fluorescence is primarily emitted by photosystem II and accounts for only a small fraction of the absorbed energy (approximately 1-2%).

The different energy pathways are in direct competition with each other, meaning that an increase in one pathway leads to a decrease in the others. This competition enables us to calculate and quantify how a plant balances energy absorption, conversion, and dissipation, providing valuable insights into the plant's physiological functioning.

Why does fluorescence yield change?

When a plant is transferred from dark to light, fluorescence emission increases sharply within the first few seconds (Figure 7). Understanding what happens inside the chlorophyll molecules during this transition is crucial.

The initial rise in fluorescence yield during the first few seconds after light exposure has been explained by a reduction in electron acceptors within the photosynthetic pathway (Kautsky et al., 1960). When photosystem II (PSII) absorbs light and electrons (originated from the splitting of water, H2O) are accepted into the electron transport chain, it cannot accept other electrons until the first ones are passed further on to other carriers in the chain. During this period, the reaction center of PSII is considered 'closed.'

A fraction of closed reaction centers results in a temporary reduction in photochemical efficiency, which is followed by a corresponding increase in fluorescence yield (remember the three fates). In dark adapted plants, non-photochemical quenching (NPQ) is initially absent, as these processes take time to activate. Hence, fluorescence yield reaches a maximum. Once NPQ mechanisms start to engage, fluorescence yield decreases and eventually stabilizes at a steady state, the timing of which varies among plant species.

This reaction makes it possible to quantify the maximum potential photosynthetic efficiency of plants, as well as the actual efficiency of photosynthesis under light, and heat dissipation. The interesting thing is that fluorescence yield also changes when plants experience stress. Some PSII centers may become inactive or impaired, altering the fluorescence dynamics.

In future blogs, we will delve into how fluorescence is measured using various methods and explore the key parameters that can be derived from these signals.

Chlorophyll Fluorescence measuring methods: PAM fluorometry

Fri, 16 Aug 2024 08:07:41 GMT

Chlorophyll Fluorescence Measuring Methods

Measuring protocols for chlorophyll fluorescence have been developed over the last century by biophysicists in scientific studies. In the past 20 years, this fundamental research has been implemented into field applications. Various approaches have been developed to analyze different aspects of the photosynthetic process, and can be broadly categorized into three types: single turnover flashes, fast induction of chlorophyll fluorescence, and photosynthetic steady-state quenching analysis.

While these types can be combined, they are most commonly implemented through two popular technical platforms: Pulse-Amplitude Modulation (PAM), and the fast polyphasic rise of fluorescence by continuous excitation (called OJIP).

From 2009 to 2019, a total of 4490 scientific papers were published using chlorophyll fluorescence techniques (Zavafer et al. 2020). From those papers, PAM was used in 2459 papers (55% of all publications), and OJIP accounted for 1073 papers (24%).

In this blog, we will delve into the most widely adopted method: PAM fluorometry.

PAM Fluorometry

The PAM fluorometry protocol uses modulated light to measure fluorescence. The measuring light consists of very short pulses (in the microsecond range) that can be applied at different frequencies. These measuring pulses itself are so short that they have no impact on photosynthesis, ensuring that the fluorescence yield reliably reflects the plant’s photosynthetic activity under various photochemical scenarios.

Figure 1 gives an overview of the PAM fluorometry protocol, divided into sections covering dark adapted measurements, light adapted measurements, and dark recovery measurements. We will highlight each section below.

PAM - Dark Adapted Measurement

A parameter from chlorophyll fluorescence analysis that is used a lot in science is the potential maximum efficiency of photosystem II (Fv/Fm). Photosystem II (PSII) is a key component of photosynthesis, and responsible for splitting water and transferring excited electrons (see introduction blog). Fv/Fm therefore serves as a measure of plant photosynthetic potential, and reflects the plant’s ability to fully utilize light when all photosystems are open. To ensure all photosystems are open, the plant must be kept in complete darkness for 15-30 minutes or longer. This period allows for the release of any previous quenching, making the measurement possible.

After dark adaptation, the base fluorescence level (Fo) is measured using the very short measuring-light pulses (Figure 2). The Fo reflects the minimal fluorescence level emitted by PSII when all reaction centers are 'open'. The light pulses are so short that no energy transfer occurs in the reaction center beyond fluorescence emission and minimal photosynthesis.

Next, a saturation light pulse (>6000 μmol m2 s1; which is 3 times the intensity of the sun) is applied to fully saturate the photosystems. The photosynthethic processes have to adapt to this extreme light, but this takes some time. The system is flushed with electrons, effectively clogging the chain, shortly blocking photosynthesis. As energy can not flow towards photosynthesis anymore during this time, fluorescence yield increases to a maximum; Fm (remember the three fates of energy) (Figure 3). This measurement is done in dark adapted plants, because other energy transfer processes (heat dissipation: NPQ) must be zero.

The difference between Fo and Fm, known as Fv, is expressed relative to Fm (Equation 1), which calculates the potential maximum efficiency of PSII. This value represents the potential maximum efficiency because no other competing processes are active during the measurement.

PAM - Light Adaptation Measurement

To measure a plant's adaptation to light, actinic light is turned on at a specified intensity. The actinic light induces photosynthesis. During the actinic period, multiple measurements (with measuring- and saturating pulses) are taken to assess the quenching processes happening in the plant.

The values of minimal and maximal fluorescence measured in the light are named differently than in the dark, namely: Fs' and Fm' (Figure 2). The difference between Fs' and Fm' is called Fq'.

The behavior of the line is reflecting the plant's adaptation process. Under light conditions, the plant cannot use all light energy for photosynthesis (see introduction blog), and therefore also initiates processes to safely dissipate excess energy, called non-photochemical quenching (NPQ). NPQ increases when a plant is adapting to light, which is why the line is sloped. Eventually, fluorescence yield reaches a steady state.

From the values of Fq' and Fm', the actual photosynthetic efficiency (called ΦPSII) can be calculated (Equation 2).

Using ΦPSII, the electron transport rate (ETR) can be calculated (Equation 3). ETR is a widely used parameter in plant science, with its main advantage over ΦPSII being that it incorporates light intensity and light absorption rate, making it more representative of actual photosynthetic activity in a given environment. It is highly correlated with actual carbon fixation.

ΦPSII alone can be beneficial in controlled conditions (where light intensity is fixed), or when comparing relative efficiencies across treatments, as it remains a simpler assessment that does not require light intensity and light absorption measurements.

where alpha (α) is the absorbance of the leaf, PSII ratio (β) is the proportion of light absorbed by PSII, and actinic light intensity ( I) is the photosynthetic photon flux density (PPFD) of the actinic light in μmol m-2s-1.

NPQ is calculated by comparing the maximal fluorescence in the light (Fm') with the maximal fluorescence in the dark (Fm):

Dark Recovery

In the dark, after a period of illumination, the plant gradually recovers (or ‘un-quenches’) from the absorbed light energy. This recovery can be monitored to provide insights into different quenching mechanisms, which are characterized by fast- and slow-relaxing components.

Dark recovery can be measured using a quenching protocol, where the dark-adapted and light-adapted stages are performed consecutively. Immediately after the actinic light is turned off, a low-intensity far-red light pulse (approximately 5 seconds) is applied. This pulse ‘flushes’ electrons from the electron transport chain. Far-red light is used, because only Photosystem I (PSI) absorbs it, effectively absorbing all electrons from electron carriers in the chain.

Minimum fluorescence is measured after this treatment, and is called Fo’.

The difference between Fm’ and Fo’ is referred to as Fv’. The measurement of maximum fluorescence after the far red pulse is called Fm'' (two primes instead of one).

After collecting these values, further photochemical and non-photochemical parameters can be calculated.

Further Photochemical Quenching Parameters

Other often used photochemical parameters that are calculated are qP and qL.

qP represents an estimate of the fraction of PSII reaction centers that are open and ready for photochemistry, derived using the following equation (Equation 5):

However, studies found that the correlation between qP and the fraction of open PSII centers was not always linear. This non-linearity is due to qP being based on the “puddle model”, where each PSII reaction center acts independently with its own antenna system and there is no energy transfer among different complexes (Kramer et al., 2004; Baker, 2008). Evidence suggests this model does not always reflect physiological reality (Butler, 1978; Lavergne & Trissl, 1995; Lazer, 1999; Barber, 2003; Kramer et al., 2004).

Barber (2003) showed that PSII centers form dimers within the thylakoid membrane, and subsequent work found that they are embedded in a shared antenna matrix capable of excitation-energy transfer between units (Baker, 2008). This configuration is known as the “lake model” (or “connected units model”) and is considered more representative for most oxygen photosynthetic organisms, particularly higher plants.

Based on the lake model, Kramer et al. (2004) derived a new parameter, qL (Equation 6), which more accurately estimates the fraction of open PSII centers:

Although qL is widely acknowledged, and offers a more theoretically refined measure, its practical application and interpretation also come with complexities and some limitations (see paper). Despite the theoretical advantage of qL, qP is still often used in studies. Researchers look for relative changes or trends compared to a control and with consistent measurements, qP can still be a good indicator for stress among treatments.

Further Non-Photochemical Quenching Paramters

During dark‐recovery (un‐quenching), different non‐photochemical quenching mechanisms recover at different rates. These variations allow us to distinguish between fast‐relaxing and slow‐relaxing components. The NPQ value we have seen before (Equation 4) is a fast quenching mechanism, and also rapidly relaxes upon darkening.

However, if a parameter is desired that includes both fast and slow quenching processes (such as state‐transitions and photoinhibition), the coefficient qN (Equation 7) can be used. This parameter effectively differentiates between these processes, as qN = qE + qT + qI.

qE (energy quenching) (Equation 8) is a fast-relaxing process that occurs within 2-5 minutes. In contrast, qI (photo-inhibitory quenching)(Equation 9) is a slow-relaxing process that can extend from several hours to days, depending on the rate of photoinhibition. This extended recovery time is dependent on the amount of damage to the PSII units that the plant must repair.

A limitation of qN is its reduced sensitivity to changes when the values are elevated, making it less responsive.

Total energy partitioning

In addition to controlled energy release, some energy is emitted in a non-regulated manner. We can derive this form of energy release relative to the other forms of energy release (PQ, NPQ), to get the quantum yield. The fraction of open PSII centers (qL) is also used. The quantum yield of this non-regulated energy release is expressed as ΦNO and can be calculated using the following formula (Equation 10):

With the quantum yield of non-regulated energy release established, the quantum yield of NPQ (ΦNPQ) can be calculated (Equation 11):

These calculations let us partition the absorbed energy into photochemistry, regulated heat dissipation, and non-regulated losses, providing valuable insight into the plant's energy distribution, since: ΦPSII + ΦNPQ + ΦNO = 1

Deriving NPQ without dark adaptation

NPQ is an important parameter when studying photosynthesis and optimizing plant efficiency. However, measuring NPQ requires determining the maximal fluorescence yield (Fm) in the dark. Dark adaptation of plants is not always practically feasible and, if done too briefly, can introduce errors. To address this, Tietz et al. (2017) proposed a method for estimating NPQ using the parameter Fo' as a reference instead of Fm. This approach leads to a new equation for NPQ:

The values for NPQ and NPQ(t) are highly linear and accurate in most cases. However, the formula is based on several assumptions, which can be a limitation. These assumptions are discussed in their paper, where an overview of the potential drawbacks is provided in their discussion section.

References

Baker, N.R. (2008). Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual Review of Plant Biology 59: 89-113.
Barber, J. (2003). Photosystem II: the engine of life. Quarterly Reviews of Biophysics 36: 71-89.
Barber, J. (2016). Photosystem II: the water splitting enzyme of photosynthesis and the origin of oxygen in our atmosphere. Quarterly Reviews of Biophysics 49: e16
Butler, W.L. (1978). Energy Distribution in the Photochemical Apparatus of Photosynthesis. Annual review of plant biology 29: 345-378.
Kramer, D.M., Johnson, G., Kiirats, O. and Edwards, G.E. (2004). New fluorescence parametesr for the determination of QA redox state and excitation energy fluxes. Photosynthesis Research 79: 209-218.
Lavergne J. and Trissl, H-W. (1995). Theory of Fluorescence Induction in Photosystem II: Derivation of Analytical Expressions in a Model Including Exciton-Radical-Pair Equilibrium and Restricted Energy Transfer Between Photosynthetic Units. Biophysics Journal 68: 2474-2492.
Lazár, D. (1999). Chlorophyll a Fluorescence Induction. Biochimica et Biophysica Acta (BBA) - Bioenergetics 1412: 1-28.
Tietz, S., Hall, C.C., Cruz, J.A. and Kramer, D.M. (2017). NPQ(T): a chlorophyll fluorescence parameter for rapid estimation and imaging of non-photochemical quenching of excitons in photosystem-II-associated antenna complexes. Plant, Cell & Environment 40: 1243-1255.
Zavafer, A., Labeeuw, L. and Mancilla, C. (2020). Global Trends of Usage of Chlorophyll Fluorescence and Projections for the Next Decade. Plant Phenomics 2020: 1-10.