Green light is similarly effective in promoting plant biomass as red/blue light: a meta-analysis

Abstract

Whether green light promotes or represses plant growth is an unresolved but important question, warranting a global meta-analysis of published data. We collected 136 datasets from 48 publications on 17 crop species, and calculated the green light effect for a range of plant traits. For each trait the effect was calculated as the ratio between the trait value attained under a red/blue background light plus green, divided by the value attained under the background light only, both having the same light intensity. Generally, green light strongly increased intrinsic water use efficiency (15%), the shoot-to-root ratio (13%), and decreased stomatal conductance (−15%). Moreover, green light increased fresh weight to a small extent (4%), but not plant dry weight, resulting in a reduced dry matter content (−2%). Hence, green light is similarly effective at increasing biomass as red and blue light. Green light also showed to increase leaf area (7%) and specific leaf area (4%; i.e. thinner leaves). Furthermore, effects of green light were species-dependent, with positive effects on biomass for lettuce and microgreens, and negative effects in basil and tomato. Our data suggest that future research should focus on the role of green light in modulating water loss, its putative role as a shade signal, and the causes for its species-specific effects on crop biomass.

A meta-analysis of green light effects on plant growth shows minimal effect on biomass but a strong increase in water use efficiency and shoot:root ratio, along with decreased stomatal conductance.

Introduction

Light is central to plant growth, as it drives photosynthesis and is a signal for development (Wong et al., 2020). In the range of 350–750 nm, plants sense light through a number of photoreceptors, and respond correspondingly (photomorphogenesis; Shropshire and Mohr, 1983; Kendrick and Kronenberg, 1994). Photoreceptors that predominantly sense in the blue (400–500 nm; cryptochromes and phototropins), red, and far-red (600–700 nm and 700–750 nm; phytochromes) wavebands have been studied extensively (Lin, 2002; Bantis et al., 2018). While no unique photoreceptor in the green (500–600 nm) region has been identified, several photoreceptors and pigments have been proposed to absorb light in this waveband: cryptochromes, phytochrome, and zeaxanthin, as well as an unknown putative photoreceptor (Bouly et al., 2007; Sellaro et al., 2010; Wang and Folta, 2013; Smith et al., 2017; Battle et al., 2020). The ubiquitous green appearance of plants indicates that they reflect a larger percentage of green light (compared with other wavebands visible to the human eye) and this has sometimes led to the misconception that plants do not absorb or use green light. Besides, the predominant photosynthetic pigments of plants, chlorophyll a and b, each preferably absorbs in the range of 400–500 and 600–700 nm. The carotenoids (including carotenes and xanthophylls) absorb light in the range 500–600 nm, acting as accessory pigments to the chlorophylls (Hashimoto et al., 2016). Consequently, leaves usually absorb ~80% of green photons, compared with an absorptance >90% for red and blue light (Paradiso et al., 2011; Liu and Van Iersel, 2021). However, given that plants evolved under natural light, the photosynthetically active radiation (PAR) fraction of which contains ca. 30% green light, they have likely found ways to use it efficiently for both photomorphogenesis and photosynthesis (Smith et al., 2017).

Within the waveband range of PAR (400–700 nm), each wavelength powers photosynthesis with different efficiency (Paradiso et al., 2011). Under monochromatic and low light intensities, red photons have the highest quantum yield, followed by green and blue photons (McCree, 1971). However, the quantum yield of green light was higher than (Terashima et al., 2009) or comparable to red light (Liu and Van Iersel, 2021) at a high photosynthetic photon flux density (PPFD; e.g. 1000 µmol m⁻² s⁻¹), due to a more uniform distribution of green photons inside the leaves (Slattery et al., 2016).

Green light is often ignored in plant photobiology and horticulture (Smith et al., 2017); relatively low absorptance by chlorophylls, lack of ‘own’ photoreceptors, low photon conversion efficacy of green light-emitting diodes (LEDs; Kusuma et al., 2021), and (until recently) a lack of green LEDs (the ‘green gap’; Folta et al., 2005) have made it difficult to conduct research on the effects of green light. However, lately green light has attracted more attention, as a result of overcoming the ‘green gap’ (Smith et al., 2017; Battle et al., 2020; Liu and Van Iersel, 2021).

Whether green light is as effective as red or blue light at increasing biomass is unclear, and in turn, it is uncertain whether it should be included in (supplemental) lighting in greenhouses or vertical farms. Biomass was sometimes found to increase when partially substituting green for red and blue light at a fixed total light intensity (Kim et al., 2004a; Kaiser et al., 2019; Schenkels et al., 2020), but this effect was species and cultivar dependent (Dou et al., 2019, 2020; Kusuma et al., 2021): in one tomato cultivar (Solanum lycopersicum, cv. Komeett), basil (Ocimum basilicum) and one lettuce cultivar (Lactuca sativa, cv. Waldmann’s Green), biomass was increased by partially replacing red and blue with green light. However, biomass was unaffected in cucumber (Cucumis sativus), another cultivar of tomato (cv. Moneymaker), and other lettuce cultivars (cvs Green-skirt and Red Salad Bowl) (Kong et al., 2015; Hernández and Kubota, 2016; Kusuma et al., 2021; Zhang et al., 2021). Contrasting results of previous studies may also result from different experimental set-ups and growth conditions, such as the percentage of green in the light spectrum, peak wavelengths of green LEDs (Johkan et al., 2012; Battle et al., 2020), and the type of LEDs used (i.e. broad- versus narrowband LEDs). The range of narrowband green LEDs was previously categorized as ‘shortwaves’ (<530 nm), which were said to promote plant growth, while ‘longwaves’ (>530 nm) were hypothesized to inhibit cryptochrome signaling and were broadly found to suppress plant growth (Dougher and Bugbee, 2001; Battle et al., 2020). Until now, a quantitative analysis of these effects has been lacking.

Theoretically, green light might affect plant growth through the following effects: (i) once absorbed by chloroplasts, green light of low intensity (<200 µmol m⁻² s⁻¹) drives photosynthesis with a similar quantum efficiency as red, and with a higher quantum efficiency than blue light (Hogewoning et al., 2012). (ii) Absorbed green photons drive leaf photosynthesis more efficiently than red or blue photons at a high PPFD (e.g. 1000 µmol m⁻² s⁻¹) (Terashima et al., 2009; Liu and Van Iersel, 2021). (iii) Green light penetrates deeper into canopies compared with red or blue, and may increase whole-plant photosynthesis due to a more uniform light distribution within the canopy (Smith et al., 2017; Lanoue et al., 2022). (iv) Green light can induce responses akin to the shade avoidance syndrome (SAS), such as increases in petiole length, specific leaf area (SLA), and leaf area (Zhang et al., 2011; Snowden et al., 2016; Schenkel et al., 2020). These changes in plant morphology may increase whole-plant light interception, as well as improve light distribution in the canopy. Effects on whole-plant light interception will be strongest before full canopy closure is reached, i.e. at a leaf area index (LAI, unit leaf area per unit ground area) of less than 3 (Heuvelink et al., 2004). (v) In some species, green light reversed the ‘blue light response’ of stomatal opening (Frechilla et al., 2000; Talbott et al., 2002). Through a general reduction of stomatal conductance (g_s) without concomitant effects on net photosynthesis rate (P_n), green light was sometimes shown to increase intrinsic water use efficiency (iWUE; P_n/g_s; Bian et al., 2019; Ma et al., 2022).

To the best of our knowledge, no quantitative analysis of literature data on green light effects on biomass and its components has been conducted. This leaves a central question—namely whether or not green light promotes plant growth—unanswered. In this meta-analysis, we aimed to answer the following questions: (i) Does the partial replacement of red and blue with green light increase plant biomass? (ii) How strongly does green light enhance physiological and morphological traits underlying plant growth? (iii) Under what peak wavelength, fraction of green light, and on which species does green light have the largest effects? We hypothesized that (i) the partial replacement of red/blue with green light increases plant fresh weight (FW) and dry weight (DW); (ii) green light increases leaf area, SLA, height, P_n, and iWUE, and decreases g_s; and (iii) plant growth is expected to be stimulated most strongly under green LED with a short peak wavelength (<530 nm), and at a low fraction of green (<30%).

Materials and methods

Data collection

Peer-reviewed papers were collected from Google Scholar, using the keywords ‘green light’, ‘green photon’, ‘spectral effect’, ‘RB’, and ‘RGB’. This resulted in a total number of 5430 hits (in February 2023; Supplementary Fig. S1). Thereafter, by using titles and abstracts, we filtered out papers that did not contain experimental datasets and papers that did not focus on plant biomass and traits underlying it, after which a total of 109 papers remained. These papers were additionally checked for mentions of relevant experimental papers that we might have missed using the above Google Scholar search.

It should be noted that the effects of a given wavelength on plant growth, morphology, and development should not be investigated under monochromatic light treatments. This is because when plants grow under one narrowband light source only, the lack of all other wavebands may have larger effects on their phenotype than the presence of the monochromatic light source itself. For example in cucumber, monochromatic blue light promoted stem elongation relative to red and blue light, but when combining blue with red light, increasing the fraction of blue light in the spectrum decreased stem length (Hernández and Kubota, 2016; Liang et al., 2021). Hence, we ignored all studies in which only a monochromatic light source was used for a given treatment.

For data extraction, the following criteria were used: (i) monochromatic green light treatments were excluded; (ii) control and experimental groups were exposed to equal light intensity; (iii) the fraction of green light and absolute green light intensities were quantified; (iv) the light source of green light that partially substituted for light in the reference spectrum was in the region of 500–600 nm; (v) light treatments lasted for at least 7 d, for any effects of green light on plant growth to become manifest; and (vi) the only variable that was allowed to differ between treatments was light spectrum. As a result, 48 papers remained, containing 136 datasets, each of which was a direct comparison between RGB and RB (or between WG and W; this was the case for 3.4% of all data), in 17 species (Supplementary Fig. S1) (where W refers to a broad spectrum in the 400–700 nm waveband and WG refers to the part of the spectrum that is replaced by green light). In addition, four studies were conducted in greenhouses, of which two claimed that solar light was completely excluded and the other two indicated that solar light had a minimal effect, and hence these data were included in the analysis. For a closer analysis of the effects of green light on physiological and morphological traits in crops that are commonly grown in greenhouses and vertical farms, data from basil, lettuce, tomato, and microgreens were chosen; those species were highlighted and used for further quantitative analyses, as they had a sufficiently large number of datasets (n≥6, Table 1). Microgreens are defined as seedlings of edible plants that are harvested 1–2 weeks after the emergence of the first true leaves (Turner et al., 2020); these are not one species, but for the sake of simplicity we will discuss them as ‘species’ in the text below.

Table 1.

Number of datasets per species and trait used in the meta-analysis

Trait	Lettuce	Tomato	Basil	Microgreens	All species
DW	47	12	9	20	109a
FW	52	8	9	20	105a
Leaf area	26	11	9	2	56
S/R	34	2	2	0	31
DMC	37	3	9	18	76
Height	4	11	6	0	34
SLA	18	10	7	0	52
[Chl]	34	8	9	0	55
g s	6	4	2	0	17
P n	8	4	2	0	24
iWUE	6	7	7	0	26

^a In 90% of the data, weight refers to shoot; in all other cases weight refers to leaves, fruit, or the whole plant.

Abbreviations: [Chl], chlorophyll content; DMC, dry matter content; DW, dry weight; FW, fresh weight; g_s, stomatal conductance; iWUE, intrinsic water use efficiency; P_n, net photosynthesis rate; SLA, specific leaf area; S/R, shoot-to-root ratio based on DW.

Organization of datasets

For extracting experimental data from figures, WebPlotDigitizer (Version 4.6., Ankit Rohatgi, Pacifica, CA, USA) was used. In the extracted datasets, physiological and morphological traits and corresponding values were documented. Traits included chlorophyll content ([Chl]), dry matter content (DMC), DW, FW, g_s, iWUE, leaf area, P_n, plant height, shoot-to-root ratio of DW (S/R), and SLA. For P_n and g_s, only data that were obtained under the treatment spectrum and intensity (by using a transparent cuvette in leaf gas exchange measurements) were used. Recorded environmental factors during plant growth included CO₂ concentration (µmol mol⁻¹), air temperature (°C), relative humidity (%), background red-to-blue light ratio (R:B, calculated as the ratio between light intensity in the range of 600–700 nm and that of 400–500 nm), and treatment duration. In the case of different day and night temperatures, average temperature was calculated based on the relative lengths of photo- and scotoperiod.

Calculations

We expressed the effects of green light as the ratio between the value of a trait obtained under background light plus green light (Trait_{RGB or WG}), and the value of the same trait under the treatment with background light only (Trait_{RB or W}) at the same PPFD:

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}=\frac{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{i}{\mathrm{t}}_{\mathrm{R}\mathrm{G}\mathrm{B}\mathrm{o}\mathrm{r}\mathrm{W}\mathrm{G}}}{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{i}{\mathrm{t}}_{\mathrm{R}\mathrm{B}\mathrm{o}\mathrm{r}\mathrm{W}}}}\end{array}}$$ (1)

This procedure resulted in a number of green light effect datasets (Table 1) that were analysed statistically per trait and species.

Statistical analysis

Statistical analyses were performed using R (Version 4.1.2, R Core Team, Boston, MA, USA). R packages used in this study were ‘readxl’ (version 1.4.1) for data transformation and ‘ggplot2’ (version 3.4.0) for data visualization. In R, the normal distribution of data was tested with the Shapiro–Wilk test first; if data were not normally distributed, then data were visually inspected, and outliers were removed. Outliers were defined as observations that were more than 1.5× interquartile range (IQR) below the first quartile or more than 1.5×IQR above the third quartile; this was the case for 2.2% of all data (details in Supplementary Table S1). Then, a right-sided or left-sided Student’s t-test at P=0.05 was applied according to the hypotheses (see Introduction) for each trait, to verify whether the calculated ratio was significantly higher or lower than 1, indicating a positive or a negative green light effect on the studied trait, respectively.

Results

We assessed whether replacing part of the red and blue light fractions with green light (named ‘green light effect’ hereafter) affects plant biomass. Across all datasets, green light constituted 20.6% of the PAR spectrum, and ranged from 3% to 45% between datasets. Green light did not affect DW, but increased FW (+4%, P=0.02), resulting in a decrease in DMC (−2%, P=0.03; Fig. 1A). Moreover, green light increased leaf area (+7%, P=0.005) and SLA (+4%, P=0.007), but not plant height (Fig. 1A, B). Surprisingly, applying green light increased the S/R by 13% (P=0.008; Fig. 2A), a comparably strong effect. There was no statistical effect of green light on [Chl] and P_n (Fig. 1B, C, respectively). Green light strongly reduced g_s, by 15% (P=0.01; Fig. 1C). The strong reduction in g_s resulted in a substantial increase in iWUE (+15%, P<0.001). Our dataset was mainly based on experiments with RB background light, but included a few data (3.4%) with W background light. When excluding the data of WG and W from the dataset the overall results remained the same (details in Supplementary Table S2).

Fig. 1.

Green light effects on traits related to biomass (A), light interception (B), and photosynthetic gas exchange (C). The green light effect was calculated as the value of a trait in plants grown under RGB, divided by the value obtained under RB. Percentages represent green light effects, and ‘+’ and ‘−’ refer to increase and decrease of mean trait values, respectively. Bold italic values indicate significant effects: *, **, and *** represent significance at P=0.05, 0.01, and 0.001, respectively. n refers to the number of datasets per trait. For violin plots, shapes around each boxplot indicate the probability density of the data; components of each boxplot from top to bottom are the maximum value, the third quartile, the median, the second quartile, and the minimum values. The red horizontal line indicates a green light effect of 1.00, i.e. no effect. Abbreviations: B, blue light; [Chl], chlorophyll content; DMC, dry matter content; DW, dry weight; FW, fresh weight; G, green light; g_s, stomatal conductance; iWUE, intrinsic water use efficiency; P_n, net photosynthesis rate; R, red light; SLA, specific leaf area; S/R, shoot-to-root ratio based on DW.

Fig. 2.

Green light effects on fresh (A, C) and dry weight (B, D) comparing narrowband and broadband green LEDs (A, B) and green light effects on plant biomass in different species as a function of peak wavelength of narrowband green LEDs (C, D). The green light effect was calculated as the value of a trait in plants grown under RGB, divided by the value obtained under RB. Percentages represent the overall effects induced by green light, and ‘+’ and ‘−’ refer to increase and decrease of mean trait values, respectively. Bold italic values indicate significant values: * represents a significant effect at P=0.05. n refers to the number of datasets. For violin plots, shapes around each boxplot indicate the probability density of the data; components of each boxplot from top to bottom are the maximum value, the third quartile, the median, the second quartile, and the minimum values. The red horizontal line indicates a green light effect of 1.00, i.e. no effect. For scatter plots, R², P, and n represent the squared correlation coefficient of the regression, the P-value for the linear regression, and the number of datapoints. The black lines and grey shaded area are regression lines and 95% confidence intervals, respectively. Abbreviations: B, blue light; DW, dry weight; FW, fresh weight; G, green light; R, red light.

Next, we examined the effects of environmental factors and experimental practices on the green light effect on plant biomass (Supplementary Fig. S2). Overall, these affected the green light effects only slightly: red-to-blue light ratios in the background light (Supplementary Fig. S2G, H) and treatment duration (Supplementary Fig. S2I, J) had no statistically significant impact on the green light effect on plant biomass. We did find small, statistically significant positive correlations between CO₂ concentration as well as relative humidity and green light effect on FW (Supplementary Fig. S2A) and biomass (Supplementary Fig. S2C, D), respectively, and negative correlations between air temperature and green light effect on plant biomass (Supplementary Fig. S2E, F). Although the effect sizes for the correlations between air temperature and green light effect on biomass were noticeable (−4.5% °C⁻¹ on FW and −3.2% °C⁻¹ on DW), considering that five data points in the 25–27 °C temperature range strongly affected the slope of these correlations, as well as considering the small effect sizes for the other two environmental parameters (+0.2% per µmol mol⁻¹ on FW, +0.58% %⁻¹ on FW, and +0.37% %⁻¹ on DW, respectively), we assume that these effects can be ignored in the other analyses presented here.

We then investigated whether different types of green LEDs (narrow- or broadband) affect the green light effect on plant FW and DW (Fig. 2A, B). We define narrowband as a waveband with a single peak that covers only part of the 500–600 nm range. Broadband, on the other hand, is defined as a waveband that spans the entire 500–600 nm range and has one or several peaks. The type of green LEDs significantly influenced the green light effect: when using narrowband green LEDs, FW was increased by 5% (P=0.04), and DW was decreased by 3%, which was a statistically non-significant effect (Fig. 2A, B). When using broadband green LEDs, on the other hand, no statistical effects were found (Fig. 2A, B). We further investigated whether there was a relationship between the peak wavelengths of narrowband green LEDs and plant biomass: significant, positive linear relationships were found for FW and DW (Fig. 2C, D), suggesting that the closer the peak wavelength was to the upper bound (600 nm), the stronger the effect of green light was on plant biomass. Though the correlations were highly significant, the values for the squared correlation coefficient of the regression were low (R²=0.05–0.08), indicating that only a small percentage of the green light effect could be explained by peak wavelength.

Subsequently, we investigated whether the green light effect depends on the fraction of green light, as well as total PPFD (Fig. 3). In basil, increasing the green fraction in the spectrum by up to 40% significantly and strongly reduced FW, and there was no statistical effect on DW (Fig. 3A, B). In tomato, on the other hand, no statistical correlation between the green fraction in the spectrum and the effects of green light on plant biomass was found (Fig. 3C, D). Finally, in lettuce, there was no statistical effect of the fraction of green light (Fig. 3E, F). Also, in lettuce the green light effect did not show obvious correlations with total PPFD, while in basil and tomato, the number of datapoints and different PPFD treatments were too low to conclude whether PPFD had an influence.

Fig. 3.

Green light effects on fresh weight (A, C, E) and dry weight (B, D, F) in different species as a function of green light fraction in the spectrum in basil (A, B), tomato (C, D), and lettuce (E, F). The green light effect was calculated as the value of a trait in plants grown under RGB, divided by the value obtained under RB. Different colors of symbols indicate total PPFD. R², P, and n represent the squared correlation coefficient of the regression, the P-value for the linear regression, and the number of datapoints. The black line, black dashed lines, and grey shaded area are regression lines with and without significant P-values and 95% confidential intervals, respectively. Abbreviations: B, blue light; DW, dry weight; FW, fresh weight; G, green light; PPFD: photosynthetic photon flux density; R, red light.

Then, we investigated whether the growth of different species responded differently to green light (Fig. 4), and if so, which factors underlying growth drove these differences (Fig. 5). Green light effects on biomass were species-dependent: a stronger effect for lettuce than for tomato and basil was observed and there was no statistical effect on microgreens (Fig. 4). Green light increased FW of lettuce by 8% (P=0.008) but did not affect DW (Fig. 4). In tomato, green light reduced biomass by 6–8%, but this was a statistically non-significant effect (Fig. 4). In basil, green light significantly reduced DW by 8% (P=0.04), while FW was decreased by 6%, which was a statistically non-significant effect (Fig. 4). It is also noteworthy that many more datasets were available for lettuce (n=47–52) than for the other three species (n=8–20).

Fig. 4.

Green light effects on fresh weight (A) and dry weight (B) across several horticultural crops. The green light effect was calculated as the value of a trait in plants grown under RGB, divided by the value obtained under RB. Percentages represent the overall effects induced by green light, and ‘+’ and ‘−’ refer to increase and decrease of mean trait values, respectively. Bold italic values indicate significant values: * and ** represent significant effects at P=0.05 and 0.01, respectively. n refers to the number of datasets. For violin plots, shapes around each boxplot indicate the probability density of the data; components of each boxplot from top to bottom are the maximum value, the third quartile, the median, the second quartile, and the minimum values. The red horizontal line indicates a green light effect of 1.00, i.e. no effect. Abbreviations: B, blue light; DW, dry weight; FW, fresh weight; G, green light; R, red light.

Fig. 5.

Green light effects on traits underlying growth in lettuce (A), tomato (B), and basil (C). The green light effect was calculated as the value of a trait in plants grown under RGB, divided by the value obtained under RB. Percentages represent the overall effects induced by green light, and ‘+’ and ‘−’ refer to increase and decrease of mean trait values, respectively. Bold italic values indicate significant values: * and ** represent significant effects at P=0.05 and 0.01, respectively. n refers to the number of datasets. For violin plots, shapes around each boxplot indicate the probability density of the data; components of each boxplot from top to bottom are the maximum value, the third quartile, median, the second quartile, and the minimum values. The red horizontal line indicates a green light effect of 1.00, i.e. no negative effect. Abbreviations: B, blue light; [Chl], chlorophyll content; DMC, dry matter content; DW, dry weight; FW, fresh weight; G, green light; g_s, stomatal conductance; iWUE, intrinsic water use efficiency; P_n, net photosynthesis rate; R, red light; SLA, specific leaf area; S/R, shoot-to-root ratio based on DW.

In lettuce, green light exposure significantly decreased g_s (−21%, P=0.012) and did not affect P_n, which together resulted in a strong increase in iWUE (+31%, P=0.005; Fig. 5A). Moreover, green light increased leaf area (+11%, P=0.02) and SLA (+6%, P=0.04; Fig. 5A). S/R in lettuce increased by 18% (P=0.005), while DMC was unaffected (Fig. 5A). In tomato, green light exposure led to an increase of 5% in SLA (P=0.04) and a decrease of 7% (P=0.02) in plant height, whereas leaf area was unaffected (Fig. 5B); iWUE was increased by 29% (P=0.007; Fig. 5B). In basil, the only significant effect found was an increase of 11% in SLA (P=0.008; Fig. 5C).

Discussion

There is an ongoing debate on whether or not green light is as effective as blue or red light at increasing plant growth (Folta and Maruhnich, 2007; Smith et al., 2017; Battle et al., 2020), and if so which traits underlie growth as well as which features of green light (e.g. peak wavelengths and fractions) affect this response. This meta-analysis aimed to quantitatively answer these questions. The analysis reveals that green light does not affect DW but increases FW, and significantly increases leaf area and SLA. Also, our results suggest that green light optimizes water use efficiency, by modulating stomatal conductance.

Green light has a minor effect on plant biomass and increases intrinsic water use efficiency

Generally, green light exposure had no effects on DW, indicating that on average, whole-plant carbon balance (photosynthetic CO₂ uptake minus mitochondrial respiration) was unaffected (Fig. 1A, C). This may put into question the assumption that green light can increase whole-plant photosynthesis through better whole-plant light distribution in a dense canopy (Dou et al., 2019; Lanoue et al., 2022). However, it should be acknowledged that plant morphology resulting from indoor cultivation is often very different from that of field-grown crops (Poorter et al., 2016), and that green light effects on whole-plant photosynthesis in the field are much less explored than those in protected cultivation. We suggest that more studies on the effects of green light should be conducted in the field, such as by utilizing wavelength-selective filters that block part of the spectrum of incoming sunlight (Hanelt et al., 1997; Bischof et al., 2002). Furthermore, green light resulted in an increase of 4% in FW (Fig. 1A), which in our opinion is a relatively minor increase. This may partly explain the reason for conflicting effects of green light on biomass in previous studies. The discrepancy between a positive FW and a non-significant DW effect suggests that green light reduced DMC (by, on average, 2%), an effect which at this point we cannot explain (Fig. 1A).

Green light significantly decreased g_s, by 15% (Fig. 1C), in line with previous studies (Frechilla et al., 2000; Talbott et al., 2002; Kim et al., 2004b; Bian et al., 2019). Reduction in g_s under green light is considered a cryptochrome-dependent response, which is mediated by the interconvertible flavin adenine dinucleotide, the chromophore of the photoreceptor cryptochrome (Folta and Maruhnich, 2007; Smith et al., 2017; Battle et al., 2020). As gatekeepers of leaf carbon and water fluxes, stomata play a vital role in balancing growth against survival. With a non-significant tendency of enhanced P_n and significantly decreased g_s (Fig. 1C), green light caused a significant increase in iWUE (15%), consistent with previous findings (Bian et al., 2019; Trojak et al., 2021; Ma et al., 2022). Thus, green light may be used to help plants cope better with drought-stress, as suggested by Bian et al. (2021). Recently, an innovative way of utilizing green light to stimulate stomatal closure was proposed: by activating optogenetic tools such as the anion channelrhodopsin 1 in Guillardia theta (GtACR1), green light may be used to prevent disorders caused by Ca²⁺ deficiency by transiently reducing transpiration and therefore growth rates of phloem-fed leaves, which may prevent Ca²⁺ levels from dropping below a critical threshold (Jones et al., 2022). Given the positive effect on mitigating drought stress, green light might be applied to crops pre-drought, by using a green-colored film to filter out parts of the spectrum other than green light to decrease water loss and maintain plant productivity.

Green light may be a signal for low light

From the top to the bottom of a fully grown canopy, incident light usually decreases exponentially. Throughout the canopy, red and blue light decrease with a steep gradient, whereas FR and green light decrease relatively less, and therefore the R:FR and B:G ratios change throughout the canopy, and both are considered to be shade signals (Fraser et al., 2016; Smith et al., 2017). R:FR is associated with the SAS, which includes rapid elongation of stems and leaves, upward orientation of leaves, increased apical dominance, and early flowering (Franklin and Whitelam, 2007; Franklin, 2008). These responses were also reported in Arabidopsis, basil, and tomato when exposed to green light (Zhang et al., 2011, 2021; Wang and Folta, 2013; Meng et al., 2019; Schenkels et al., 2020). However, in our meta-analysis the most pronounced SAS response, stem elongation (Fernández-Milmanda and Ballaré, 2021), was absent (Fig. 1B), suggesting that unlike FR, green light does not generally trigger the SAS. This was in agreement with the findings of Zhang et al. (2022), where an effect on stem elongation was absent when partially replacing red and blue light with green light at a fixed PPFD, but stem elongation was found when FR was included in the spectrum. Thus, the SAS seen under green light treatments may actually be a result of the change of R:FR induced by the partial substitution of green for red light, rather than an effect of green light itself. It has been shown that both green light and FR could induce shade responses, but they may differ in their respective sensing systems, as green light is perceived through cryptochromes and/or another putative photoreceptor (Wang and Folta, 2013; Smith et al., 2017), whereas phytochromes mediate shade responses induced by FR (Franklin and Whitelam, 2007). Furthermore, the shade responses induced by FR can be augmented by the addition of green light, suggesting a complementary role of green light with FR in triggering shade responses (Wang et al., 2015). We did find that green light caused other responses related to low light and shade, including increased SLA and S/R (Poorter et al., 2006; Fernández-Milmanda and Ballaré, 2021) (Figs 1A, B, 5), and the effect on S/R was particularly strong (13%; Fig. 1A). We, therefore, speculate that plants can sense signals related to low light not only from R:FR but also green light, which may help the plant acclimate to low light environments (Wang and Folta, 2013; Smith et al., 2017). In addition to the increased S/R, our data confirmed that green light increased leaf area (Fig. 1A). However, a larger leaf area was only observed in lettuce, not in tomato and basil (Fig. 5). The increase in leaf area might thus help to explain the positive green light effect on biomass in lettuce, but not in the other two species (Fig. 4). Within a certain range of LAI (e.g. LAI <3), an enlarged leaf area intercepts a greater amount of irradiance utilized for photosynthesis, consequently leading to an increase in biomass (Heuvelink et al., 2004; Williams et al., 2021). A similar phenomenon was also observed in lettuce under FR, which triggers SAS and increases biomass through an increased light interception (from an expanded leaf area; Jin et al., 2021). Meng et al. (2019) also found that replacing blue with green or FR increased leaf expansion and shoot mass of leafy greens. These results suggested similar roles for green light and FR in triggering shade avoidance related responses, which in this case caused an increase in plant biomass from an expanded leaf area at an early growth stage.

The type of green light matters, but in a species-specific manner

With the rapid development of LED technologies, options for the use of green LEDs in indoor cultivation are growing (Wu et al., 2020). We found that different characteristics of the spectrum (i.e. peak wavelengths of green LEDs and fractions of green light) influenced the green light effects on biomass in various species (Figs 2, 3). Previous studies qualitatively concluded that shorter wavelengths (e.g. <530 nm) complement blue light-induced responses (Battle et al., 2020), whereas longer wavelengths (580–600 nm) restricted plant growth due to a reduction in [Chl] (Dougher and Bugbee, 2001; Fan et al., 2013). Our analysis points to the opposite (Fig. 2C, D), i.e. the green light effect on biomass for all tested species tended to get stronger the higher the peak wavelength, albeit with weak squared correlation coefficients of the regression (R²=0.05–0.08). When partially replacing red and blue light with green light at longer peak wavelengths, part of the spectrum of green light may easily extend into the red light range (600–700 nm), which has the highest photosynthetic efficiency in the PAR range (McCree, 1971). Therefore, a higher biomass may be expected relative to that of green light with shorter peak wavelengths. Moreover, since this effect was more obvious in lettuce, further research should be conducted in different species to confirm the effects from longer-wavelength green light. Also, future research should aim to determine the causes underlying the strong green light effects in lettuce.

Though green light generally improved FW (Fig. 1A), crop species showed distinct differences (Fig. 4). Green light effects on biomass were more positive in lettuce and microgreens, while they were negative in basil and tomato (Fig. 4). Positive effects of green light on lettuce biomass have been documented before (Kim et al., 2004a, 2006; Bian et al., 2016; Chen et al., 2016; Li et al., 2021). Green light increased leaf area, S/R, and reduced g_s in lettuce (Fig. 5A). Thus, we speculate that the increased leaf area could intercept more light for photosynthesis and an increased S/R meaning that more assimilates are allocated to the shoot, and therefore an increase in shoot DW is expected. However, the shoot DW of lettuce was not significantly increased (Fig. 4B), which might be due to the lower g_s and P_n at leaf level (Fig. 5A). Therefore, green light seems to have a counterbalancing effect on plant growth: a higher leaf area that intercepts more light for crop photosynthesis and (at least in some cases) lower leaf-level photosynthesis constrained by the lower g_s. Furthermore, these two opposing effects might also explain why green light effects were found to be inconsistent in previous studies. However, the limited amount of data make it difficult to interpret green light effects on growth traits when comparing species (Fig. 4, 5). Therefore, we recommend future research should test the effects of green light on plant growth in more species. In addition, the green light effects at different stages of plant growth should be taken into consideration.

Conclusions

This meta-analysis indicates that green light is roughly equally effective in promoting plant growth as red and blue light. Surprisingly, despite the absence of an effect on DW, there was a small positive (4%) effect on FW. The effects of green light on plant biomass, as well as on physiological and morphological traits, were species-dependent. Green light seemed to trigger responses related to shading and/or acclimation to low-light environments, such as increased S/R (13%), leaf area (7%), and SLA (4%), but, unlike FR, it did not increase stem length. Adding green light increased the biomass of lettuce and microgreens, whilst it showed negative effects in basil and tomato. Furthermore, the increased FW of lettuce might be (partly) caused by larger leaf area and S/R. Green light also increased iWUE (15%) through decreased g_s (−15%) and a non-significant increase in P_n, suggesting that green light may improve the tradeoff between carbon gain and water loss.

Supplementary data

The following supplementary data are available at JXB online.

Fig. S1. Flow chart of the methodology.

Fig. S2. Effects of environmental parameters on green light effects on plant biomass.

Table S1. The removed outliers from the normality test.

Table S2. Green light effects on traits of plant growth at different background spectra.

Author contributions

EK and EH designed the research; YC collected and analysed the data; YC wrote the manuscript; EK, EH, LFMM, and ZB reviewed and edited the manuscript; EK, EH, and ZB supervised the research; EK, EH, ZB, QY, and LFMM obtained the funding.

Conflict of interest

The authors declare no conflicts of interest.

Funding

The authors gratefully acknowledge financial support from the Joint PhD Program between Wageningen University & Research (WUR) and Chinese Academy of Agricultural Sciences (CAAS) (no. MOE11NL1A20151701N).

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.