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Reaching Natural Growth: Light Quality Effects on Plant Performance in Indoor Growth Facilities

Camilo chiang.

1 Department of Environmental Sciences—Botany, University of Basel, Schönbeinstrasse 6, 4056 Basel, Switzerland; [email protected]

2 Department of Research and Development, Heliospectra, Fiskhamnsgatan 2, 414 58 Gothenburg, Sweden; [email protected]

Daniel Bånkestad

Günter hoch, associated data.

To transfer experimental findings in plant research to natural ecosystems it is imperative to reach near to natural-like plant performance. Previous studies propose differences in temperature and light quantity as main sources of deviations between indoor and outdoor plant growth. With increasing implementation of light emitting diodes (LED) in plant growth facilities, light quality is yet another factor that can be optimised to prevent unnatural plant performance. We investigated the effects of different wavelength combinations in phytotrons (i.e., indoor growth chambers) on plant growth and physiology in seven different plant species from different plant functional types (herbs, grasses and trees). The results from these experiments were compared against a previous field trial with the same set of species. While different proportions of blue (B) and red (R) light were applied in the phytotrons, the mean environmental conditions (photoperiod, total radiation, red to far red ratio and day/night temperature and air humidity) from the field trial were used in the phytotrons in order to assess which wavelength combinations result in the most natural-like plant performance. Different plant traits and physiological parameters, including biomass productivity, specific leaf area (SLA), leaf pigmentation, photosynthesis under a standardised light, and the respective growing light and chlorophyll fluorescence, were measured at the end of each treatment. The exposure to different B percentages induced species-specific dose response reactions for most of the analysed parameters. Compared with intermediate B light treatments (25 and/or 35% B light), extreme R or B light enriched treatments (6% and 62% of B respectively) significantly affected the height, biomass, biomass allocation, chlorophyll content, and photosynthesis parameters, differently among species. Principal component analyses (PCA) confirmed that 6% and 62% B light quality combinations induce more extreme plant performance in most cases, indicating that light quality needs to be adjusted to mitigate unnatural plant responses under indoor conditions.

1. Introduction

Temperature and light are principal determinants of plant growth, as plants react to environmental conditions in their development. With improvements in controlled environment facilities, the use of indoor cultivation systems has increased worldwide, both for research and plant production. One of the problems, that especially plant researchers are confronted with, is a clear difference between plants grown under indoor versus outdoor conditions. These differences are limiting the transferability of results from indoor experiments to natural systems. Several experiments have tried to replicate outdoor growth in indoor facilities, but low correlations have been found [ 1 , 2 ]. Poorter et al., [ 3 ] suggested that this difference comes mainly from the different photothermal ratio (PTR), the ratio between the daily light integral and the daily mean temperature, which is generally much lower in growth chambers. The low PTR in indoor experiments mainly derives from the low and constant irradiances used, compared with the higher and variable sunlight conditions found in nature. In general, conditions in indoor facilities lead to higher specific leaf area (SLA), leaf nitrogen content, and relative growth rate. While maximum photosynthesis (A max ), plant height, and shoot dry weight (SDW), are lower compared with outdoor experiments [ 3 ].

Due to the high photosynthetic efficiency of blue (B) and red (R) light, high electrical efficiency of B and R LEDs, as well as the high technical requirements to create sun-like LED spectra [ 4 , 5 ], most existing indoor plant growth facilities with LED lighting systems use mixtures of mainly B and R light. However, different LED lamps use different proportions of B and R LEDs, or B and R in combination with other LED types, such as white and far-red. This results in very different lighting environments among different indoor growth facilities. In addition, the lack of a common protocol for reporting and measuring LED light irradiance further limits the comparability between experiments [ 6 ]. Many studies have investigated plant response to different B to R ratios. These studies revealed that independent of light intensity, a required minimum percentage of B light is necessary to maintain the activities of photosystem II and I [ 7 ]. Hogewoning et al., [ 8 ] suggested that at least 7% B light is necessary to reproduce near-natural plant growth. In addition, it has been observed that long exposures of monochromatic light can have drastic effects, including non-natural morphologies. With parameters such as shoot elongation, specific leaf area (SLA), chlorophyll concentration and photosynthetic performance being affected [ 9 , 10 , 11 , 12 ].

The vast majority of studies related to light quality effects on plants have been conducted under low light levels, varying between 20 to 330 µmol m −2 s −1 [ 13 , 14 , 15 , 16 , 17 , 18 ], with a few exceptions (for example 550 µmol m −2 s −1 [ 19 ]), even though interactions between light quantity and quality have been reported previously [ 9 ]. Finally, it is also important to consider other light quality related parameters, for example, the effect of red to far red ratio (R:FR). The applied light conditions in indoor cultivation typically has a much higher R:FR ratio (or a complete absence of FR) compared with sunlight conditions. This affects plant photosynthesis, morphology, and development (for example [ 8 , 10 , 14 , 15 , 18 , 19 , 20 ]). Once the R:FR ratio is corrected to more natural values, a more natural-like growth may be achieved, despite the large deviations from natural sunlight in other parts of plant biologically active radiation (280–800 nm; for example [ 21 ])

The aim of this study is to provide the first step in a series of experiments with the overall goal of reaching nature-like growth of plants under indoor conditions. Specifically, we investigate the effects of varying proportions of B and R light within walk-in growth chambers (phytotrons) on growth and physiological traits of plants from different functional groups. We also compared our findings to the same species grown in a natural-light field trial, where we expected more “natural-like” growth in our indoor treatments that applied a closer to natural light spectra. The inclusion of seven different species from different functional plant types further enabled us to identify if light quality affects plant performance differently among species and plant types. In contrast to many previous studies, we explicitly applied more natural-like R:FR ratios and light intensities [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ], and the plants were exposed to temperatures and air humidity based on the pre-measured field trial.

2.1. Light Treatments

Four different treatments were obtained through calibrating the phytotrons for the desired spectra as indicated in Table 1 and Figure 1 .

Applied spectra for the field trial and each of the different light treatment where 6%, 25%, 35% and 62% refers to the percentage of blue light as percentage of the photosynthetic photon flux density (PPFD) (In other words, excluding far-red). The integrated area between 400 and 700 nm corresponds to an approximate 575 μmol m −2 s −1 of photosynthetic photon flux density in each case.

Spectral characteristics of sunlight and of the indoor light treatments, based on the measured spectra shown in Figure 1 .

2.2. Plant Growth and Biomass Allocation

There was a significant interaction between the light treatments and the different species on the total plant height at the end of the experiments ( Table 2 ), where the relationship with the field trial was species dependent. Some species, for example, Alnus and Melissa , were significantly smaller independent of the light treatment, while others, for example, Ocimum , were taller than the same species in the field trial.

p -values derived from the full-factorial ANOVA analyses of the different measured plant traits, with light treatment and species as fixed factors, and the replicates of the individual light treatments as random factors. Non-significant p -values (≥0.05) are indicated as “-”.

* Lettuce was removed from these analyses. ** Interactions or factors were removed from the analysis due non-significance.

Comparing only among the phytotron treatments, all species had shorter individuals at higher percentages of blue (B) light (62%), which was most pronounced in Alnus and Melissa (58 and 52% lower height respectively, compared with the 6% B treatment; Figure 2 A). Other species like Ocimum and Triticum were less affected by changes in B light, but follow the same trend (20 and 15% lower height respectively, compared with the 6% B treatment; Figure 2 A). In several of the tested species, there was a significant difference in plant height between the two intermediate B treatments (25 and 35% B). Averaged across species, 6% B light produced 22% taller plants that were statistically significantly different from the two intermediate treatments. While in the other extreme, 62% B light yielded a statistically significant shortening of plants by approximately 20% compared with the average across treatments ( Figure 2 A). A dose response was obtained for specific leaf area in several species (SLA, Figure 2 B). Unlike the height results, and due to the species-specific reactions to the light treatments, the average response across species did not significantly differ, neither within the light treatments, nor between the light treatments and the outdoor control. However, Lactuca and Alnus, for example, had significant higher SLA at 6% B compared with other light treatments, while other species, for example, Raphanus and Triticum, had higher values at 25 or 35% B light compared with 6 or 62% B light.

Fold change on: plant height ( A ) and SLA ( B ), relative to the average field trial (dotted line). Coloured dots are the average of each species in both experiment runs ( n = 18), the black dots are the average values across all 7 species ( n = 126). Error bars indicate the standard errors. The grey area corresponds to the standard error of the field trial. Different letters indicate statistical difference between groups with experiment replicate and species as a random effect.

There were significant interactions between the light treatments and species for the dry biomass of leaves, shoots, roots and the total dry biomass ( Table 2 ). Similar to plant height and SLA, the relationship between plant biomass and light, under the different light treatments, with the field control was species dependent, yet averaged across all species. Leaf biomass did not significantly differ from the outdoor control in any of the light treatments.

If only the phytotron treatments are compared, there was a lower leaf biomass under 62% B light compared with 6% B light in all investigated species. This was especially the case for the two tree species tested, where Alnus and Ulmus were most sensitive to high percentages of B light ( Figure 3 A). On average, plants exposed to 6% B had 35% higher leaf biomass than plants exposed to 62% B ( Figure 3 A). Similar results were obtained for shoot biomass where, across all species, plants grown at 62% B had a significantly lower shoot biomass compared with all the other light treatments, and yet similar values as in the field trial (except for Ulmus and Ocimum , Figure 3 B). In contrast to the aboveground biomass, the effects of light quality on root biomass were different among all species ( Figure 3 C). In comparison to the field trial, four species ( Ulmus , Lactuca , Ocimum , Triticum ) had significantly higher root biomass in the phytotron treatments, while in three species ( Raphanus , Alnus , Melissa ) it was similar compared to the field trial ( Figure 3 C).

Fold change on: leaves ( A ), shoot ( B ), roots ( C ) and root to shoot ratio ( D ), as dry weight relative to the average value of the field trial (dotted line). Coloured dots are the average of each species in both experiments runs ( n = 18), the black dots are the average values across all 7 species ( n = 126). Error bars indicate the standard errors. The grey area corresponds to the standard error of the field trial. Different letters indicate statistical difference between groups with experiment replicate and species as a random effect.

Across all species, there was no strong effect of light quality on root biomass, but a trend to higher root biomass at 6% B ( Figure 3 C). Total biomass production followed the same trend as found for the individual plant organs, with a significant interaction between light treatment and species ( Table 2 ); higher values under indoor conditions independent of the light treatment, compared to the field trial and increasing biomass with increasing percentage of blue light (data not shown).

With respect to the effect of light quality on the allocation of biomass, there was a significant interaction between light treatment and species for the root to shoot (r:s) mass ratio ( Table 2 ). Almost all species had significantly higher r:s values in the phytotrons compared to the field trial independent of the light treatment, with Triticum showing a four to eight times higher investment in roots compared with the field control ( Figure 3 D). In some species (e.g., Alnus and Ocimum ), 6% and 62% B light induced higher r:s ratios than 25 and 35% B light, while other species (e.g., Melissa and Ulmus ) were almost indifferent with respect to light quality ( Figure 3 D).

2.3. Leaf Pigmentation

There were significant interactions between the different treatments and species in the pigment concentrations of the leaves ( Table 2 ). Furthermore, the difference between the field trial and the different light treatments was species dependent, but all investigated species exhibited higher Chl a concentration in leaves at 62% B light compared to the other light treatments (strongest effect in Lactuca ) and several species exhibited the lowest Chl a concentrations at 6% B light ( Figure 4 A).

Fold change on Chlorophyll a ( A ), Chlorophyll a:b ratio ( B ), carotenoids content ( C ) and Fv/Fm values ( D ) relative to the average value of the field trial (dotted line). Coloured dots are the average of each species in both experiments runs ( n = 18), the black dots are the average values across all 7 species ( n = 126). Error bars indicate the standard errors. The grey area corresponds to the standard error of the field trial. Different letters indicate statistically difference between groups with experiment replicate and species as a random effect.

On average across all species, 6% B was the only treatment significantly different from the field trial, with 24% lower concentration of Chl a. The effect on Chl b was similar to that of Chl a, with a smaller effect of the light quality on the total amount of Chl b (data not shown). As a result, the average a:b ratio across all species was not significantly different among the light treatments, but significantly higher than in the field trial ( Table 2 , Figure 4 B). The concentrations of carotenoids in leaves, showed overall very similar reactions to light quality as chlorophyll, with increasing concentrations at higher proportions of blue, and an interaction between the light treatment and species ( Figure 4 C, Table 2 ). Like chlorophyll and carotenoids, the Fv/Fm values, showed significant interaction between the species and the light treatments ( Table 2 ). Almost all species in the phytotron treatments with 25, 35 and 62% B had Fv/Fm values close to the field trial ( Figure 4 D), except Ocimum , which revealed higher Fv/Fm values indoors than in the field. Averaged across all species, Fv/Fm was significantly lower than in the field at 6% B ( Figure 4 D). Performance index (Pi) absolute values followed the same trend as Fv/Fm (data not shown, Supplementary Table S1 ).

2.4. Photosynthesis and Leaf Respiration

In contrast to the other plant traits tested, all species reacted uniformly to the light treatments in all measured photosynthesis and leaf gas exchange parameters, with no significant interaction between treatment and species effect found ( Table 2 ). When measured with the standardised light of the gas exchange chamber, the average maximum photosynthesis (A max ) across all species was significantly higher in plants raised at 62% B compared with the field trial ( Figure 5 A). Meanwhile, when the same parameter was measured under the in situ light, higher values were reached at either 25% or 35% B light compared with the field trial ( Figure 5 B). The quantum yield of the CO 2 fixation (α) had similar trends to A max , where on average no light treatment was significantly higher than the field trial when the standardised light was used. The 62% B light was the only treatment to induce higher α values than the other light treatments ( Figure 5 C). When α was measured using the in situ light, higher values were reached at either 6%, 25% or 35% B compared to the field trial ( Figure 5 D).

Fold change on maximum photosynthesis ( A max , A , B ), quantum yield of the CO 2 fixation curve (α, C , D ) and dark respiration (DR, E , F ) relative to the average value of the field trial (dotted line). Values were measured with either a standard light with 70% B light and 30% R light (‘standardised light’) or the actual ‘in situ’ light (see methods for details). Coloured dots are the average of each species in both experiments runs ( n = 18), the black dots are the average values across all 7 species ( n = 126). Error bars indicate the standard errors. The grey area corresponds to the standard error of the field trial. Different letters indicate statistical difference between groups with experiment replicate and species as a random effect.

The photosynthetic light compensation point (CP) and the dark respiration of leaves (DR) were significantly different among species ( Table 2 ). Averaged across all species, there were no significant effects of the treatments on CP when the standardised light was used. However, with in situ light significantly lower values were reached under 6 and 25% B conditions, compared with 35 and 62% B and the field trial (data not shown). DR was on average significantly lower in plants exposed to 62% B light compared with other light treatments and the field trial when the standardised light was used ( Figure 5 E). This was not the case for the in situ light, where although several species had higher DR values than the field trial, no significant difference was found between the treatments for the average across species ( Figure 5 F).

2.5. Principal Component Analysis (PCA)

Principal component analysis (PCA) for each species revealed a clustering of each treatment with varying degrees of overlap ( Figure 6 ); from easily differentiable groups between light treatments in some species, for example, Alnus , Lactuca and Triticum , to a more continuous gradient among treatments.

Principal component analysis (PCA) of the measured traits of each species: ( A ) Alnus , ( B ) Ulmus , ( C ) Ocimum , ( D ) Lactuca , ( E ) Melissa , ( F ) Raphanus and ( G ) Triticum , grown under 6% B, 25% B, 35% B and 62% B light. Each lighter point ( n = 18) corresponds to a plant and solid ones to the average weighted centroids of each light treatment, where the name of each species is mentioned in the respective upper right corner. Ellipses correspond to the standard error of the weighted centroids with a confidence interval of 95%.

Melissa , Raphanus , Alnus , Ocimum , Lactuca , and Triticum showed a large variability between treatments from outdoor (field trial) to indoor conditions, while the different light treatments tended to cluster. This was not the case for Melissa , Raphanus , and Ulmus , where the field trial was not clearly separated from the phytotron treatments ( Figure 6 ). The two intermediate treatments (25% and 35% B) yielded responses closer to the average (i.e., the centre of the figure) in most species. The loadings for score calculations were also plotted to determine the importance of each factor. No single parameter was specifically responsible for the variation across treatments and between species, except for CP in Ocimum growing in the field trial ( Figure S1 ). Independent of the species the first two components explained between 31% and 43% of the total variability.

3. Discussion

Previous studies investigating the effect of the spectral light quality on plant performance were mainly focused on single species, and they generally did not directly compare findings with natural conditions. In the present study, we deliberately investigated a suite of species from different functional plant types to determine if, and how, they react to the different treatments. Through application of the same mean climatic conditions indoors, as in the initial field trial, we could better assess which LED light conditions are generating the most natural-like plant performance. Our results showed clear differences within and between the light treatments when compared to the field trial on most measured plant traits. The effect sizes were highly species-specific, while effect directions were similar among species, with the clear exception of SLA and root biomass production. As expected, light treatments with very extreme blue: red (B:R) ratios (6 and 62% B) induced more extreme (‘unnatural’) values in most plant traits than treatments with a more balanced B:R ratio (25 and 35% B).

3.1. Light Quality Effects on Morphology

Studies that compared indoor with outdoor plant growth were previously often biased by a higher plant density in the indoor condition [ 3 ]. In our study, we deliberately kept the exact same plant densities between the field and the phytotron trials to avoid any stand density bias on plant morphology. The effects of B light percentages on plant morphology have been previously reported in several studies [ 8 , 11 , 12 , 21 , 22 , 23 , 24 , 25 ]. In general, B light is sensed by the cryptochrome system, where under high irradiances or high levels of B light, plants exhibit shorter and stunted growth (For example [ 8 , 14 , 26 ]). It is also known that a total lack of B or R light negatively affects plant performance, including growth rate, height, photosynthesis and several other parameters. For example, Hernandez et al. [ 10 ] found that tomato plants grew shorter under either B or R light mixtures compared with only B or R light.

Previous studies have shown that under high levels of B light, there is an increase in the palisade cell area, which can lead to an increase in leaf thickness (For example [ 8 , 10 , 12 ]). However, this B light-induced increase in leaf thickness does not necessarily have to translate into a lower SLA [ 27 ]. Dougher and Budgee [ 22 ] identified that the direction of the effect of B light on SLA is very species dependent. Independent of the applied light quality, Poorter et al. [ 3 ] found that on average, indoor experiments tend to produce plants with higher SLA compared to field grown plants, mainly due to higher temperatures and lower light quantity in indoor facilities. In our study, which applied the average temperature and light quantity as in the field trial, the SLA of most species was similar between plants growing in the phytotrons and in the field.

Under the different treatments stem, leaf, root, and total dry biomass largely followed the trend in plant height. The lower biomass at high B% can thus be explained by a stronger inhibition of stem elongation by B light due to an increased cryptochrome activity [ 14 ], exposing the plants to lower irradiance due to larger distances to the light source compared with plants treated under a lower percentage of B light. In addition, the stunted growth of plants at high B% leads to an increased self-shading of leaves and decrease in light interception, which has been proposed to result in negative consequences for the whole plant productivity [ 21 ]. Although the individual species reacted differently between phytotrons and the field trial, on average, a significantly higher plant biomass within our phytotron treatments compared with the field was found (except for the 62% B treatment). In contrast, Poorter et al. [ 3 ] reported lower biomass under indoor conditions compared with field grown plants depending on species and functional group. Again, this apparent contradiction could be explained by the fact that in contrast to other indoor experiments, we deliberately applied the same average temperatures and light strength in the phytotrons as were measured in the field trial. Poorter et al. [ 3 ] demonstrated that indoor experiments often use low levels of light, which might reduce plant biomass in comparison with outdoor-grown plants.

While the effect of light quality on the aboveground organs was quite similar among species in the current study, the direction of the effect on roots was clearly species dependent. With species such as Alnus and Ocimum exhibiting higher root growth at very low and high B%, and species such as Raphanus and Ulmus showing increased root production at intermediate B percentages (25 and 35% B). To date, scarce information is available on the effects of light quality on belowground plant productivity. A previous study by Yorio et al. [ 28 ] reported that under 10% B mixed with 90% R light there was a higher root production in Lactuca, Raphanus, and Spinacia, compared with plants grown under pure R light. Nhut et al. [ 29 ] found that mixtures of B and R light stimulate the production of roots compared with pure R light in strawberry plantlets. Independent of light quality, we found a significantly enhanced root production in the phytotron treatments compared to the field grown plants, except for the 62% B treatment. As indicated by Poorter et al. [ 3 ], indoor climatization might induce root zone conditions that differ markedly from field conditions, leading to altered root production and consequently profoundly changed plant growth. As all plants in our experiment were regularly watered in both field and phytotron treatments, we can exclude that the observed higher root productivity in the phytotrons results from different water availability between indoor and field trials. However, pot soil temperature was not monitored, and it is possible that it differed significantly between indoor and field conditions, partly due to the lack of infrared radiation from the LED lamps.

3.2. Light Quality Effect on Leaf Pigmentation

The concentration of chlorophyll and carotenoids changed strongly with light quality in our study. Under natural sunlight, cryptochrome activity is reduced at high radiation, thereby signalling strong light conditions in the plant. The same effect can be achieved under experimental conditions by exposing plants to high percentages of B light [ 30 ]. The high proportion of B light in our 62% B treatment thus triggered the enhanced production of photosynthetic pigments despite the fact that the other treatments with lower B% had the same PPFD. In fact, the low concentrations of Chl a and b in plants that have been treated with low levels of B light or monochromatic R light in previous studies, have even led to photo-oxidative stress in plants due to an increase of O 2 - and H 2 O 2 radicals that induce cellular damage [ 8 , 19 ]. Barnes and Bugbee [ 30 ] proposed that a minimum of 20−30 μmol m −2 s −1 of B light is necessary to reach natural-like growth and morphologies, even if such a minimum requirement for B light appears to be highly species-specific [ 31 ]. It is likely that due to all of our light treatments including at least 6% of B light, we did not observe light quality related stress effects in our experiment. However, we identify that even with over 30 μmol m −2 s −1 of B light (at 6% B), higher percentages of B can increase the photosynthetic maximum capacity in several species, indicating that it is not just the quantity of B light, but also its relationship with other wavebands in the spectrum. Interestingly, most species showed higher Chl a:b ratios in the phytotrons compared to the field trial. This effect has been observed previously in indoor-grown plants [ 32 ], where it is attributed to the lack of fluctuating light conditions in indoor facilities.

Like chlorophyll, the production of carotenoids was also significantly increased with 62% of B light compared to 6% B (and 35% B), yet only the 25% B and the 62% B treatments induced higher carotenoid concentrations than in the field trial. Hogewoning et al. [ 8 ] reported an increase of carotenoids in cucumber plants when B was increased to 50% in the light spectra. An increase of carotenoids has been shown to work as an accumulative protection mechanism correlating with high light intensities or high B ratios. For example, the authors of [ 12 ] found that Fv/Fm of rapeseed leaves was reduced under monochromatic B or R light treatments, compared with mixtures of B and R. They attributed this to a higher PS II damage and linked the higher concentrations of carotenoids to a protection mechanism against oxygen radical formation. This is in line with our Fv/Fm results, where lower percentages of B in the applied spectra induce small but significant differences of the Fv/Fm values in almost all investigated species.

3.3. Light Quality Effects on Photosynthesis

When A max was measured under the same standardised light conditions (30% B and 70% R) in the current study, plants under 63% B showed, on average, significantly higher A max compared to plants under 25% B and the field trial. This could be partially explained by the increased chlorophyll concentrations in 63% B treated plants (see above). Previously, higher A max have been linked to higher levels of stomatal conductance and nitrogen concentration, where the latter is correlated to Rubisco, cytochrome, proteins and chlorophyll content [ 33 ]. A higher A max has also been suggested to partially derive from an instantaneous stimulation of photosynthesis (i.e., during the exposure to the light within the gas-exchange chamber) due to the lack of adaptation to the standardised light condition [ 8 ]. In our case, using 70% R in plants adapted to 62% B may promote a higher A max , meanwhile this may not be the case in plants adapted to lower percentages of B light, and therefore higher percentages of R light. Kim et al. [ 15 ] have shown that in Pisum sativum about four days were necessary to reach full photosynthetic acclimation after a transition from a PSI to a PSII stimulating light environment and vice versa. Similarly, Hogewoning et al. [ 34 ] showed in duckweed, that six days were needed to fully acclimate to different light conditions, using the Chl a:b ratio as the control parameter.

In contrast to the measurements of standardised light, when measured under the respective in situ light conditions, A max was significantly lower at very low (6%) or very high (62%) B light conditions, despite the higher concentration of chlorophyll at 62% B or small differences in SLA ( Figure 2 B). In a similar but more extreme experiment, several long-term studies reported lower net photosynthesis or A max in plants raised under monochromatic B or R light [ 8 , 11 , 12 ]. Hogewoning et al. [ 8 ], also reported dysfunctional photosynthesis in cucumber plants, grown under pure R light and a dose response curve in A max when the B% was increased up to 50% B, with no further increase of A max beyond 50% B. The increase of A max with B percentages was associated with a reduction of the SLA, an increase of N and chlorophyll per leaf area, and higher stomatal conductance under mixtures of B and R light compared with only B or R [ 8 ]. Matsuda et al. [ 35 ] reported an increase of A max in spinach plants exposed to a 1:1 B: R radiation compared with just B light, associated with increased leaf N concentration. Shengxin et al. [ 12 ] showed that dark adapted Fv/Fm values were higher (as an indicator for less photo-stress) under mixtures of B and R light compared with monochromatic B or R light.

The effects of treatments on photosynthesis were also visible in the quantum yield of the CO 2 fixation curve (α) of the investigated species. Similar to A max , a more natural level of B light may explain a higher efficiency when an ‘in situ’ light was used for our gas-exchange measurements, with significantly higher values indoor than in the field trial. Similar results have been reported at 15–30% B compared with 50% B [ 8 ]. This effect may indicate the evolutionary adaption of species to the natural sunlight spectrum, with higher quantum yield under a more natural B:R ratio (circa 33% of B in the sunlight spectrum [ 36 ]). Other conditions with extreme levels of B or R light may require the adaptation to each light condition, where CO 2 fixation may have a wavelength dependence related to absorption properties of the different pigments involved. Terashima et al. [ 37 ], described three major causes for the wavelength dependency of the quantum yield: absorption by photosynthetic carotenoids, absorption by non-photosynthetic pigments and an imbalanced excitation of the two photosystems, where an imbalance in excitation will result in quantum yield losses [ 27 , 38 ]. It has been shown that a correct light stimulus, with light qualities matching the species-specific ratio of PSII and PSI, is key to high quantum efficiency of photosynthesis [ 39 ]. The light compensation point of photosynthesis (CP) was generally not affected by light quality. Similar results have been observed in previous cases [ 9 , 12 ].

In the current study, the average dark respiration (DR) using the standardised light, independent of the species, was relatively lower at 62% B compared with the other light treatments or the field trial. Atkin et al. [ 40 ] described in tobacco that observed changes in DR were dependent on the previously applied irradiance (tested between 0 to 300 μmol photons m −2 s −1 ). An instantaneous stimulation of the photosystems in low light adapted plants due the stimulus of an intensity radiation burst was hypothesised. Although the total photon flux was the same between treatments in our study, similar short time effects on DR might have occurred when plants were exposed to a high intensities and light spectrum that they were not adapted to.

3.4. Principal Component Analysis

The PCA analyses performed in this study confirmed that the effects of light quality on plant performance are highly species dependent, and adjustments of the light spectra may help to promote more natural like growth, where more natural growth like plants tend to group closer to the field trial in the PCA. Applying a light spectrum with similar B and R light proportions to sunlight is proposed to avoid physiological plant responses to a lack or excess of B light (which might also differ among species). Although 7% B has been recommended to avoid dysfunctional photosynthesis [ 8 ], this study indicates that levels of 25 to 35% B light in the spectrum are needed in indoor conditions to avoid undesired (i.e., unnatural) effects of the light spectrum on plant growth. This was demonstrated with higher distances of the 6%B light treated plants from the field trial plants in the PCA. No specific trait was identified across the different species to have a higher importance than others ( Figure S1 ), where the ranking of importance of each measured parameter was species dependent. Independent of this, the PCA clearly indicated that other environmental variables should be controlled (e.g., air flux, soil temperature) or more precisely mimicked in indoor growth facilities if natural-like growth is required. A similar approach has been previously used [ 41 ] to understand the difference between indoor and outdoor experiments, with a focus on Arabidopsis ’s metabolism where a clearer clustering of the indoor and outdoor conditions was obtained. Similar values of the first and second component to the ones presented here (first and second component explaining 28 and 15% of the variance, respectively compared with 24 and 15% average across species in our study).

4. Materials and Methods

4.1. plant material and pre-growing conditions.

In this study, we investigated young plants of 7 species from different functional plant types to include the species as the source of variation: trees represented by black alder ( Alnus glutinosa (L.) Gearth, provenance HG4, Zurich, Switzerland), Scotch elm ( Ulmus glabra Huds., provenance Merenschwand, Aargau, Switzerland), herbs represented by basil ( Ocimum basilicum ‘Adriana’), lettuce ( Lactuca sativa ), melissa ( Melissa officinalis ), radish ( Raphanus raphanistrum subsp. sativus (L.) Domin), and grasses represented by winter wheat ( Triticum aestivum ). For the experiments, all plants were raised from seeds. The seeds of both tree species were purchased from the Swiss federal institute for forest, snow and landscape research, WSL, Birmensdorf, Switzerland. All herb seeds were provided from Wyss Samen und Pflanzen AG, Zuchwil, Switzerland, and Triticum seeds were supplied form Sativa AG, Rheinau, Switzerland. Hereinafter, the species will be referred to by their scientific genus name for clearness. Due to the different germination speeds the timing of sowing was different for the species as follows: seeds of Alnus and Ulmus were sown in 20 × 40 × 2 cm trays with commercial substrate (pH 5.8, 250 mg L −1 N, 180 P 2 O 5 mg L −1 , K 2 O 480 mg L −1 , Ökohum, Herrenhof, Switzerland) 43 days before the start of the experiments and were left to germinate under 190 μmols m −2 s −1 of photosynthetic photon flux density (PPFD: 400–700 nm) with 25% Blue (B: 400–500 nm), 32% Green (G: 500–600 nm) and 41% Red (R: 600–700 nm) light and an R to far red (FR: 700–800 nm) ratio (R:FR. 655–665 nm and 725–735 nm; according to [ 42 ]) of 5.1 for 23 days, using LED lighting with a day length of 16 h. Twenty days before the start of the experiment, the light was increased to 240 μmols m −2 s −1 PPFD, with a R: FR of 5.1, to acclimate the plants to higher intensity levels. Thirteen days before the start of the experiment Melissa seeds were sown in the same type of trays and keeping the last-mentioned environmental conditions. Six days before the start of the experiments the remaining species were sown in the same type of trays and under the same environmental conditions, with the exception of Triticum, which was sown immediately in round 2 L pots with a density of 15 seeds per pot (13.5 cm diameter, Poppelmann, Lohne, Germany). All light measurements were done using a using a spectrometer (STS, OceanOptics, Florida, United States). During the germination and the pre-treatment period, the different seedlings were raised at 25 °C/50% relative humidity (RH) during daytime and 15 °C/83% RH during night, with 10 h per day and one-hour light/temperature/humidity ramping pre and post day.

At the start of the experiment, all species, excluding Triticum , were transplanted to the same type of 2 L pot previously used for Triticum, with a single individual in each pot. Moreover, Triticum was thinned to 10 plants per pot. The pots were filled with the same substrate as used in the germination trays, and 4 g of Osmocote slow release fertiliser (Osmocote exact standard 3–4, Scotts, Marysville, OH, USA), containing 16% total N, 9% P2O5, 12% K2O and 2.5% MgO, was added to each plot. All plants were watered daily in the morning throughout the experiment.

The pre-growing procedure was repeated 3 times for this study: First, for the field-trial that was used as reference for the phytotron experiments, and then twice for the different light treatments of the phytotron experiment. (See control and light quality treatments below). No significant difference in initial height or biomass was found at the start of the experiments within species for the different replications (data not shown).

4.2. Control and Light Quality Treatments

To establish a control treatment as a reference point for natural growth, all seven target species were grown in a field trial for 35 days (4 August 2017–7 September 2017) at the botanical garden of the University of Basel, Switzerland. Throughout the field trial, the in situ climate and the natural sunlight spectrum was recorded ( Figure S2 and below). Following the field trial, we exposed plants from the seven different species to four mixtures of B and R light, which can be expressed as a B/R ratio, or as percentage of B light in four walk-in Phytotrons (1.5 m × 2.5 m) with full control of temperature, air humidity and light quality and quantity (prototypes, Enersign GmbH, Basel, Switzerland). To unify nomenclature with previous studies, the four different light treatments will be referred to by their respective B light proportion ( Table 1 ). The light treatments were chosen based on previous literature (e.g., Hogewoning et al. [ 8 ]), measurements of natural light completed in situ [ 36 ], and technical capacities of the phytotrons at the average light intensity of the outdoor treatment. For each treatment, the replication per species was 9 pots (with either one or more individuals per pot depending on species; see above). In all light treatments, the average PPFD from the field trial (575 μmol m −2 s −1 ) was provided at the average height of the different species using 18 LED panels for each chamber consisting of a mixture of B (400–500 nm) , White (2500 K), R (600–700 nm) and FR (700–800 nm) LEDs per panel (prototypes, DHL-Licht, Hanover, Germany). The LED lighting system of each chamber was mounted on movable ceilings, the height of which can be adjusted through the environmental control software of the chambers. To preserve similar light levels at average plant height, the height of the lamps was adjusted twice during the experiment. Based on the field trial conditions, the day length was set to 13 h and 5 min, giving a constant daily light integral (DLI) of 27.1 mol m −2 day −1 in all light treatments. Similar to the light conditions, temperature and humidity during day and night were set to average field trial conditions: 22 °C/66% RH and 18 °C/79% RH, for day and night, respectively, with a period of one-hour ramping before and after daytime. A uniform temperature and humidity distribution within each chamber was ensured by a constant vertical air stream from below. To avoid border and space effects, all plants were randomly distributed within each phytotron on two tables. The tables were rotated by 90° every day. Each light treatment was replicated twice (two separate runs of all four light combinations), where the distribution of the chambers was random between the two runs.

At the end of the 35-day experimental period, a suite of measurements was conducted in the field trial and the phytotron experiments. A description of the measured parameters is given in the following paragraphs. Due to limitations imposed by the lamp characteristics at high intensities, a higher R:FR ratio compared with outdoor (1.8 vs. 1.1) was applied in order to reach the targeted light intensities. No UV light was applied in the phytotrons.

4.3. Climatic Growth Conditions

In order to apply the most natural conditions within the phytotrons, the climate from the field trial at the botanical garden of the University of Basel, Switzerland, was recorded throughout the 35-day growth period ( Figure S2 ). Relative humidity, temperature, and PPFD were measured every 5 min with a weather station (Vantage pro2, Davis, Haywards, CA, USA). In addition, sunlight spectra in the waveband 350–800 nm were recorded every minute using a spectrometer (STS) that was equipped with an optical fiber and a cosine corrector (180º field-of-view; CC-3-UV-S, OceanOptics) placed by the weather station’s PAR sensor facing upwards. The spectrometer was connected to a Raspberry Pi 2 computer for automatic sampling, integration time adjustments and data storage. A posteriori, the spectra were used to calculate photon flux densities within specific wavebands: PAR, B, G, R and FR. The PAR light measurements were verified by comparing the data from the weather station with the data from the spectrometer readings. The data from the field trial were used to calculate average diurnal and nocturnal temperature, air humidity and PAR conditions for the phytotron treatments.

4.4. Morphological Parameters

By the end of the 35-day growth period, plant height was measured as total height from the substrate to the apical tip. In the case of long inflorescences ( Raphanus ) or plants without a clear stem ( Triticum ), extended leaf length was recorded as height, and in the case of Lactuca , no height was recorded. Two full-grown leaves from the top three mature leaves were collected from each plant to measure leaf area (LI-3100, Licor, Lincoln, NE, USA) and calculate the specific leaf area (SLA) in cm 2 g −1 on a dry leaf weight basis. Dry weight (DW) was measured separately for leaves, stems and roots after 10 days drying at 80ºC in a drying oven (UF 260, Memmert, Schwabach, Germany). Due to the lack of a clear stem, only total aboveground and root biomass were measured for Lactuca , Melissa and Triticum . All reported organ weights and the below to above ground biomass ratio (root:shoot-ratio) refer to plant dry mass.

4.5. Chlorophyll Fluorescence and Chlorophyll Content

One night before the end of the experiment, fast chlorophyll fluorescence induction was measured on one of the top three leaves in four randomly chosen plants of each species and treatment by using a continuous excitation fluorometer with an intensity of 3500 μmol m −2 s −1 centred at 627 nm (Pocket PEA, Hansatech instruments Ltd., Norfolk, UK). The plants were dark adapted for at least 20 min before recording photosynthetic maximum quantum yield (Fv/Fm) and the absolute performance index (PI) of the leaves, which has been correlated previously to stress (for calculations and details, see [ 43 ]).

During harvest, two discs of 1.13 cm 2 area from the top four leaves were punched and stored in a 1.5 mL Eppendorf tube together with four to six glass beads of 0.1 mm diameter for later chlorophyll analysis. The tubes were quickly frozen in liquid nitrogen and then kept at −80 °C until analysis. During the day of chlorophyll measurement, the tubes were agitated two times for 10 s to triturate the tissue using a mixing device (Silamat S6, Ivoclar Vivadent, Schaan, Liechtenstein). After adding 0.7 mL of acetone to each tube, they were agitated again for 10 s and then centrifuged at 13,000 rpm at 4ºC for 2 min. A total of 0.25 mL of the supernatant was dissolved in 0.75 mL of acetone, and the sample absorption spectra were measured using a spectrometer (Ultrospec 2100 pro, Biochrom, Holliston, MA, USA). Chlorophyll a and b concentrations, chlorophyll a to b ratio (Chl a, Chl b and a:b ratio, respectively) and total carotenoid concentrations as mg g −1 , were calculated from the spectra using the values at 470, 646 and 663 nm as described in [ 44 ].

4.6. Leaf Gas Exchange

Six days before the end of the experiment, a light response curve of net CO 2 leaf-exchange was measured in one of the top three leaves in three randomly chosen plants per species and treatment using a LI-6800 photosynthesis system (LI-COR, Lincoln, NE, USA). The light response curves were measured under two different light spectra: (i) a standardised artificial light spectrum, composed of 70% R and 30% B (in the following referred to as ‘standardised light’) provided by the chamber head light source to study photosynthesis of the different species under a uniform light spectrum, and, (ii) the respective growing light spectrum (in the following referred to as ‘in situ spectrum’) provided by using a transparent, clear-top chamber head (Clear-top leaf chamber 6800-12A, LI-COR) to study photosynthesis of the different species under their respective growing spectra and avoid any bias on photosynthesis from a non-adapted spectrum. Twelve different light intensities: 2000, 1500, 1000, 800, 600, 400, 200, 100, 50, 25, 10 and 0 μmol m −2 s −1 of PPFD were used for light response curves with the ‘standardised light’ spectrum. Due to lower maximum irradiance in the phytotrons limited by the light quality being applied (see above), the light response curves for the ‘in situ’ growing light were measured only up to a maximum radiation of 700 μmol m −2 s −1 of PPFD (700, 480, 380, 200, 100, 60, 30, 20, 17, 15 and 0 μmol m −2 s −1 of PPFD). All leaf CO 2 -exchange measurements were conducted at 400 ppm CO 2 , 60% relative air humidity and 20 °C leaf temperature, with 60 to 120 s as the threshold for stability after each light change intensity. Stability of readings was assumed when the difference of the slopes between IRGA’s were smaller than 0.5 μmol mol −1 sec −1 and 1 for CO 2 and H 2 O, respectively.

For each light curve, 12 different light models were fitted accordingly [ 45 ], including a model for photo-inhibition [ 46 ]. For each species and treatment, the model with the best fit (lowest sum of squares) was selected (details in [ 45 ]). The selected model was then used to calculate the following four values from the light response curve: maximum photosynthesis within the range of measured light (A max ), quantum yield of the CO 2 fixation (α) as the slope of the linear curve between 0 and 100 μmol m −2 s −1 of PPFD, dark respiration (DR) and the light compensation point (CP) of photosynthesis.

4.7. Statistical Analysis

To evaluate the effect of the light treatments, a two-way analysis of variance (ANOVA) was performed for all measured parameters, considering the species and different treatments as fixed factors and the two replicates of each treatment as a random factor. The significance of the random factor was evaluated using a restricted likelihood ratio test. The data were checked for normal distribution, independence and homogeneity of the variance.

To enable the direct visible and statistical comparison of the treatment effects across species, each measured trait was normalised relative to its mean value on the field trial for each species (the original trait average values per species and treatments are available in Table S1 ). The normalised values were used to perform a one-way ANOVA, considering the treatments as fix factor and species as random factors ( Table S2 ). A Tukey pairwise multiple comparison test was used as post hoc analysis to identify significant differences ( p < 0.05) among treatments. In several cases when all indoor light treatments differed from the field trial, an additional one-way ANOVA was performed without the field trial to highlight the individual response differences to the different light treatments (Data non shown).

Finally, to identify the specific traits that have the maximum variation between treatments and to quantify which treatment gave the overall most similar response compared to the outdoor trial, a principal component analysis (PCA) was performed separately for each species, using the different measured traits as input values. To perform a PCA analysis, the same number of observations is required for each variable but due to fewer photosynthesis measurements, chlorophyll measurements and fluorescence measurements than the number of plants used for biomass measurements, in each species and treatment, the missing values of chlorophyll content and light parameters were imputed using normal distribution with the same average and standard deviation of the available data. All analyses were performed using R [ 47 ] and the package plyr for data processing and lm4, car, RLRsim, emmeans for data analysis and multicomp and vegan for statistically significant representations.

5. Conclusions

The applied light spectra in this study significantly influenced plant morphology, pigment concentration and photosynthesis. Less deviating responses compared to the field trial were reached with either 25% or 35% of B light in almost all species. Hence, if natural like plant growth is desired in indoor plant cultivation, the application of a balanced light spectrum is generally recommended. Despite this, spectral quality of the light source is only one of many factors that can potentially bias plant performance. In this study, we thus aimed to apply similar climatic conditions within the growth chambers as were measured in the field trial to compare outdoor with indoor growth. Nevertheless, we still found significant differences between phytotron and field grown plants in most of the investigated plant traits. This highlights the difficulties to exactly reproduce natural plant performance in indoor growth facilities, as well as the necessity to include the simulation of additional environmental factors (e.g., replication of natural minimum and maximum temperature, humidity and irradiance changes, wind speed and direction) in indoor experiments with plants.

Acknowledgments

We thank Georges Grun and the gardeners at the botanical garden of the University of Basel for their technical support for the phytotron experiments and climate measurements. We also thank Sarah Newberry for proofreading the manuscript.

Supplementary Materials

The following are available online at https://www.mdpi.com/2223-7747/9/10/1273/s1 , Figure S1: Principal component analysis (PCA) of the measured traits of each specie grown under 6% B, 25% B, 35% B and 62% B light, Figure S2. Environmental conditions of temperature (A), air relative humidity (B) and light intensity as PPFD (C) from the field trial, Table S1: Raw average values by measured trial for each treatment and species; Table S2: p -values for the different measured traits in both experiments using normalised data.

Author Contributions

Conceptualization, C.C., D.B. and G.H.; methodology, C.C., D.B. and G.H.; validation, C.C.; formal analysis, C.C.; investigation, C.C.; resources, D.B. and G.H.; data curation, C.C.; writing—original draft preparation, C.C.; writing—review and editing, D.B. and G.H.; visualization, C.C.; supervision, G.H.; project administration, G.H.; funding acquisition, D.B. and G.H. All authors have read and agreed to the published version of the manuscript.

The presented work was supported by PlantHUB-European Industrial Doctorate funded by the H2020 PROGRAMME Marie Curie Action—People, Initial Training Networks (H2020-MSCA-ITN-2016). The programme is managed by the Zurich-Basel Plant Science Center.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Make a hypothesis about which color in the visible spectrum causes the most plant growth and which color in the visible spectrum causes the least plant growth.

How did you test your hypothesis? Which variables did you control in your experiment and which variable did you change in order to compare your growth results?

Analyze the results of your experiment. Did your data support your hypothesis? Explain. If you conducted tests with more than one type of seed, explain any differences or similarities you found among the types of seeds.

What conclusions can you draw about which color in the visible spectrum causes the most plant growth?

Given that white light contains all colors of the spectrum, what growth results would you expect under white light?

• Carry out an experiment to determine which colors of the light spectrum are used in photosynthesis as evidenced by plant growth.
• Measure plant growth under lights of different colors of the spectrum.

Teach Plant Growth Through Virtual Labs

Teaching Strategies

Teaching your students how to collect and analyze data while learning what healthy plants need to grow has never been easier or less messy! All it takes is an interactive  virtual lab simulation.  

There are a lot of questions about plant growth for students to investigate. How do plants grow? How do plants eat? How does sunlight affect plant growth? Some of those questions can be difficult to answer with the limitations of environmental conditions at school, such as classrooms without windows for sunlight. And what about the time it takes to grow plants?

Plant growth experiments through virtual labs don’t have the restrictions of other types of investigations. That’s why plant growth virtual labs save the day.  

  Using Online Labs to Teach Plant Growth

Plants are an integral part of human and animal life, and having a basic understanding of how they grow is important. Online labs, such as plant growth experiments , allow students to see results within minutes that would otherwise take weeks.

Virtual labs like ExploreLearning Gizmos allow students to experiment and manipulate throughout the stages of plant growth. Simulations make it possible to manipulate environmental conditions and analyze outcomes. Students deduce the best conditions for growing plants and can even create their own experiments.  

Plant Growth Experiments

Cornell Waste Management Institute ©1996 Department of Crop and Soil Sciences Bradfield Hall, Cornell University Ithaca, NY 14853 607-255-1187 [email protected]

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Plant Growth and Osmotic Potential

Water is a critical element for plant growth. All water used by land plants is absorbed from the soil by roots through osmosis. Osmosis is the movement of a solvent (e.g.water) across a semipermeable membrane from low solute (e.g.salt) concentration towards higher solute concentration. Excess levels of salts in soils makes soil water solute concentrations higher than in the plant root cells. This can limit plant water uptake, making it harder for plants to grow. (See Appendix A for more information)

About the Experiment

For this experiment, we’re going to test the effect that high salt soil concentrations have on plant growth and root development.

 What You'll Need

• 7 clear plastic cups (Solo cups)
• 7 non-clear plastic cups
• Potting soil (small bag)
• Wheatgrass or cat grass seed (250 seeds, can be found online or at local pet store)
• Baking soda
• Measuring spoons
• Drill & small bit

When using table salt (sodium chloride) and baking soda (sodium bicarbonate) to create saline and alkali soils, you can observe the germination and growth of grass leaves at increasing levels of salt and ph. Then you can treat the salt/alkali effected soils with "leaching" and observe plant growth.

Let's Do This!

1 . Drill 3 small holes in 7 clear plastic cups. Have an adult help with this step for safety.

2 . Fill 1 clear cup (with holes) with soil 1” from top of cup and place cup inside non-clear cup (without holes).

Pour ½ cup of water into the soil cup and allow to absorb. Pour another ½ cup of water into the soil cup.

Place 30 grass seeds on top of the wetted soil and cover with 1/8” of new soil and gently wet. Make sure seeds are covered with soil (Label cup “Control”).

3 . Fill 3 clear cups (with holes) with soil 1” from top. Add 1 teaspoon of salt to the soil of 1 cup (label cup “salt 1”). Add 1 tablespoon of salt to the 2nd cup (label cup “salt 2”). Add 3 tablespoons of salt to the 3rd cup (label cup “salt 3”).

Place each cup in a non-clear cup (no holes) and add ½ cup of water to each and let absorb. Add another ½ cup of water.

Place 30 grass seeds in each cup and cover with 1/8” of new soil and moisten new soil. Make sure seeds are covered with soil (Image 2).

4 . Fill 3 clear cups (with holes) ¼ full with soil. Add 1 tablespoon of baking soda to 1st cup and add more soil to fill cup 1” from the top. Hold your hand over the cup so soil does not spill and shake the cup to mix the baking soda with the soil (label cup “alkali 1”).

Add 2 tablespoons of baking soda to the 2nd cup and fill with soil 1" from top. Again, with hand over cup, shake to mix baking soda and soil (label cup “alkali 2”).

Add ½ cup of baking soda to the 3rd cup, fill with soil 1" from top and shake to mix (label cup “alkali 3”).

Place each cup in a non-clear cup (no holes). Add ½ cup of water to each and let absorb, then add another ½ cup of water. Place 30 grass seeds in each cup and cover with 1/8" of new soil and moisten new soil. Make sure seeds are covered with soil.

5 . Let grass germinate and grow for 1 week.

Let’s Look At The Results!

After 1 week count the number of plants in each cup and measure the tallest blades of grass in each cup. Record the numbers for each on the data sheet . Remove the clear cups and observe root growth.

After 1 week, remove “salt 2” and “alkali 2” clear cups from red cups and place in the sink or outside (where water can drain) and slowly pour 6 cups of water through each, making sure to not over-fill (pour ½ cup at a time and let drain).

Observe which cups drains fastest (alkali soils have poor drainage). Make sure seeds are still covered with soil (add some on top if necessary) and let them grow for 1 more week.

After 1 week (2 weeks total) observe if “leached” cups now have plants that are growing. Did leaching help the same for saline vs. alkali soils?

After 2 weeks , measure the height of plants in each cup and record the results. Again, observe the roots and record observations on the data sheet.

Summarize your data and observations.

• Why did plants grow or not grow in each cup?
• What effect did leaching have on plant growth and why?
• Did leaching work on both salt and baking soda equally and why?

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2nd Grade Science : Sunlight, Water, and Plant Growth

Study concepts, example questions & explanations for 2nd grade science, all 2nd grade science resources, example questions, example question #1 : sunlight, water, and plant growth.

What do plants need to grow?

Water and wind

Moonlight and wind

Sunlight and water

Sunlight and moonlight

For plants to grow and survive, they need sunlight and water. Water is collected through the roots of the plant and absorbed from the soil. Sunlight is collected through the leaves of the plant to use in the process of photosynthesis to make food. Plants cannot live without sunlight and water.

Justin wants to plan an investigation to see if a plant can live without sunlight or water. What list of materials is appropriate for this type of investigation?

Water, one plant, a dark closet, a ruler

Water, ten plants, three windows, two dark closets, a pencil, paper, ruler, and matches

Seventeen plants, a window, water, and a pencil

Water, two plants, a window, a dark closet, a pencil, paper, and a ruler

If Justin wants to plan an investigation, an important part is to have the right supplies or materials. Justin will need two plants, one to place in the sunlight and one in the dark, water for the plant in the sunshine, a ruler to measure growth, and paper and pencil to record observations. The other lists are incomplete or have materials that are not needed for this investigation.

Brandon is curious about what keeps plants alive and helps them to grow. Which answer choice is a testable, scientific question that relates to the topic?

Which plant is the prettiest, the daisy, or lilac?

Which plant, the rose bush or sunflower, grows the tallest?

How many different plants are there in a garden?

What would happen if plants did not receive water or sunlight?

Most investigations or experiments start with a question. In this case, Brandon is wondering about keeping plants alive and helping them grow. A scientific question must be testable, meaning someone can collect data and observations, and it should be related to what the person was wondering. The correct answer choice is testable and related. Water and sunlight are essential to a plant's survival, so they would be good variables to test.

Example Question #2 : Sunlight, Water, And Plant Growth

Virginia is planning an investigation to test what would happen if she does not give rose plants sunlight or water. Her teacher tells her to state her hypothesis or prediction of what she thinks the results will be. Which hypothesis is most reasonable?

"I believe the rose plants will start to die but then come back to life. They will survive and grow tall."

"I believe that the rose plants are prettier than most other flowers, so they will be fine."

"I believe the rose plants will not survive without sunlight or water. They will wilt and die over time."

"I believe that the rose plants will bloom beautiful flowers and have tall green stems."

A hypothesis is stated before an investigation or experiment is performed, and the evidence collected will either support or will not support the prediction. A hypothesis should be reasonable and related to the topic. Virginia is testing what would happen to plants without water or sunlight; these are two critical pieces to a plant's survival. Using background knowledge or personal experiment, a prediction can be made that the plants will not survive. "I believe the rose plants will not survive without sunlight or water. They will wilt and die over time." is the most reasonable hypothesis.

Nick is planning an investigation to see if plants grow better in the dark or somewhere sunny. Which data table set-up would be best for him to use during this investigation?

In this investigation, Nick is only testing if plants grow better somewhere dark or sunny. The water the plants receive should be the same, and there should be some plants in the sun and some in the dark so they can be measured and observed. There have to be plants in both locations to compare growth.

Christopher is planning an investigation to see whether plants need sunlight and water to grow and survive. Which title would be best for his experiment?

"Do Plants Need Sunlight and Water to Grow More Than One Foot Tall?"

"Do Plants Need Water to Grow?"

"Do Plants Need Sunlight and Water to Grow?"

"Do Plants Need Sunlight to Grow?"

The best title for Christopher's experiment would be "Do Plants Need Sunlight and Water to Grow?". It is short and includes all of the vital information. It doesn't leave anything out or have any parts that are irrelevant to the experiment.

Jennifer wants to test whether plants can survive without sunlight and water. Which answer choice represents the BEST plan to investigate this?

Get two identical pots. Fill one with dirt from the yard and fill the other with soil from the store—plant three sunflower seeds in one and three lima bean seeds in the other. Place one in the window and one in a dark room. Water the plant in the dark room daily and record the plant growth and observations.

Get four identical pots and fill each with the same amount of dirt—plant three sunflower seeds in each container. Place two in a window and the other two in a dark closet. Water one of the plants in the window daily, water one of the plants in the dark closet daily—record plant growth and observations.

Get two identical pots. Fill one with dirt from the yard and fill the other with soil from the store. Plant three sunflower seeds in each container. Place them both in a dark room, water the plants daily, and record plant growth and observations.

Get six identical pots and fill each with the same amount of dirt—plant three sunflower seeds in each container. Place two in the window, two in a dark room, and two outside. Water all six plants daily and record plant growth and observations.

Jennifer is testing whether plants can survive without sunlight and water, so those are the only two variables that will change. Everything else in the investigation should remain the same. The same pots, same dirt, same amount of water each day, same type of seeds, and the same amount of sunlight or darkness should be maintained throughout the investigation. "Get four identical pots and fill each with the same amount of dirt—plant three sunflower seeds in each container—place two in a window and the other two in a dark closet. Water one of the plants in the window daily, water one of the plants in the dark closet daily—record plant growth and observations." is the most controlled and complete investigational plan.

Andrew wants to investigate if plants need water to survive. Which data table set-up is most appropriate for this investigation?

The only test variable in this investigation is whether the plant gets water or not. Andrew is testing if water is needed for plants to survive. All of the test plants should be in the same location, and some should get water while others do not so they can be compared to one another. If all the plants get water, then Andrew will not know what would happen without water. If he has some plants in different locations, he won't know if it is the water or the area that stopped a plant from surviving.

Example Question #3 : Sunlight, Water, And Plant Growth

Morgan wants to investigate if plants need water AND sunlight to survive. Which data table set-up is most appropriate for this investigation?

Morgan has two test variables in her experiment. She wants to know if plants need water AND sunlight to survive. Morgan has to test both of these variables to see what plants need. The data table that best matches this investigation shows two plants in the sun and two plants in the dark. One plant from each group will get watered, and the other two will not. Morgan can collect information and observations about how the plants grow under each condition.

Example Question #4 : Sunlight, Water, And Plant Growth

What does it mean to investigate ?

A hinged barrier used to close an opening in a wall or fence

To place money into an account hoping to make more later

To discover, study, or research something

A sleeveless, close-fitting waist-length garment worn over a shirt

To investigate something or to participate in an investigation means to discover, study, or research. When scientists have a question, they will come up with a plan to try and answer it. If someone wanted to investigate a plant's growth, they could test the type of soil, amount of water or sunlight, or the type of plant. They would collect data and make observations about the plants; this would be an investigation.

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Practical Biology

A collection of experiments that demonstrate biological concepts and processes.

Observing earthworm locomotion

Practical Work for Learning

Published experiments

Investigating the effect of minerals on plant growth, class practical.

All of these techniques involve a long-term project – prepared in one lesson, left for about a month (see Note 2 ), then with results gathered in one or more lessons after that time. There is scope for focus on the scientific methods involved in planning, controlling variables, collecting and analysing data, as well as on the biology of plant nutrient requirements. The methods include several different dependent variables – percentage cover, harvested mass, dry mass, turbidity, population count with haemocytometer. Each method produces a qualitative outcome as well.

Lesson organisation

In the first lesson, present the biological problem – how to investigate the effects of different minerals on plant growth. Give each group of students a different option for following plant growth. Ask each group to plan in detail how they would set up an investigation. Evaluate the methods in terms of controlled variables, reliability, and ease of data collection. Decide which method to work with. If you can manage the practicalities of two different investigations, choose two.

In the next lesson, set up an investigation (or two). Get all students involved – for example, if using the radish method, each student could set up one pot of seeds, for a particular culture medium, all seeds could be grown together and results pooled.

In the final lesson, collect the results and collate for the group – showing how to calculate means and discussing reliability of results and validity of any conclusions drawn.

Apparatus and Chemicals

Mineral nutrient mixes ( Note 1 )

For each group of students:

Plant material to investigate and associated materials. Choose from A , B , C or D .

A Germinating barley

Healthy barley seedlings, approximately 6, germinated a week in advance ( Note 3 ) test tubes (1 per culture solution) cotton wool aluminium foil or black card/ polythene to surround test tubes dropping pipette

B Radish Seeds – 2 per container Growing medium – peat/ vermiculite mix ( Note 4 ) Small container (for example a film canister) with hole cut in bottom, 1 per set of seeds Wicks – a piece of capillary matting/ cloth cut into narrow diamond shape, 1 per container Capillary matting and water reservoirs – one per culture medium ( Note 5 )

C Algal culture Algal suspension – in full mineral salts medium ( Note 6 ) Conical flask, 1 per culture solution Cotton wool Syringe to dispense 1 cm 3 of algal suspension Disinfectant for syringe Measuring cylinder, 100 cm 3 Microscope Microscope slide Cover slip

D Lemna (duckweed)

Healthy Lemna plants of similar size, 10 per culture solution Beakers or jam jars, 1 per culture solution Plastic film to cover the beakers or jars

Health & Safety and Tehnical notes

Read our standard health & safety guidance

1 Solid media to prepare Long Ashton water culture, or Sach’s water culture solutions, are available from Timstar or Philip Harris (see Suppliers). It can be cheaper, and is certainly much easier, to buy the ready-prepared nutrient solutions if not all the chemicals are available in-house. But you could make up your own solutions using the recipe from the CLEAPSS Recipe card.

Sach’s culture solution (complete recipe): Dissolve the following salts in 1 litre of distilled water.

• 0.25 g of calcium sulfate(VI)-2-water
• 0.25 g of calcium phosphate(V)-2-water CaH 4 (PO 4 ) 2 .2H 2 O
• 0.25 g of magnesium sulfate(VI)-7-water
• 0.08 g of sodium chloride
• 0.70 g of potassium nitrate(V) (see CLEAPSS Hazcard – OXIDISING and DANGEROUS with some metals and flammable substances)
• 0.005 g of iron(III) chloride-6-water (see CLEAPSS Hazcard – HARMFUL as a solid)

For Sach’s culture solution with mineral deficiencies , make the following changes.

• Deficient in calcium: 0.2 g of potassium sulfate(VI) replaces calcium sulfate(VI)-2-water and 0.71 g of sodium dihydrogenphosphate(V)-2-water replaces calcium phosphate(V).
• Deficient in iron: Omit iron(III) chloride-6-water.
• Deficient in nitrogen : 0.52 g of potassium chloride replaces potassium nitrate(V).
• Deficient in phosphorus : 0.16 g of calcium nitrate(V)-4-water (see CLEAPSS Hazcard – OXIDISING and IRRITANT) replaces calcium phosphate(V).
• Deficient in sulphur : 0.16 g of calcium chloride (see CLEAPSS Hazcard – IRRITANT as solid) replaces calcium sulfate(VI) and 0.21 g of magnesium chloride-6-water replaces magnesium sulfate(VI).
• Deficient in magnesium : 0.17 g of potassium sulfate(VI) (Hazcard 98B – low hazard) replaces magnesium sulfate(VI).
• Deficient in potassium : 0.59 g of sodium nitrate(V) (Hazcard 82 – oxidising and harmful as solid and dangerous with some metals and flammable materials) replaces potassium nitrate(V).

2 Each system requires a different lead-in time, a different length of time for results to develop and a different method for measuring the effects.

3 If you germinate barley seeds on cotton wool or blotting paper, the roots may stick in the damp medium. Using OASIS or vermiculite avoids this – although it costs a little more. Refresh the mineral solution every couple of days by tipping out and replacing. Aerating the solution before applying to the roots may improve the general uptake of solution, and reduce the risk of the barley seedlings rotting.

4 The peat/ vermiculite mix must be low in nutrients – for example a seed compost, rather than multipurpose (which has added nutrients).

5 Water reservoirs and wicks: Set up a series of ice-cream containers containing each culture medium to be tested. Cut slots in the lids of the containers. Cut pieces of capillary matting as shown in diagram. Insert the capillary matting and pour enough culture medium into the ice-cream container to ensure that the matting remains moist at all times.

Place the wicks in the bottom of the small containers before filling (to within 5 mm of the top) with growing medium. Add 2 seeds to each container. Add 2-3 mm more growing medium and firm gently. Place the container on the capillary matting so that the wick can draw liquid mineral salts medium from the container.

6 Inoculate 500 cm 3 of complete medium with Scenedesmus quadricaudus or Micrasterias thomasiana var. notata or Chlorella – about one week before required. Aerate continuously using a filter pump, or aquarium airstone and pump and keep in a light place or illuminate 24 hours a day. Sciento and Blades Biological provide suitable algae to culture. Do not use algae cultured on agar slopes.

SAFETY: Follow good hygiene practice after handling pond water or plants removed from ponds.

Preparation:

If using barley seedlings, germinate about one week before use. If using algal suspension, start culturing alga about 4 weeks before use.

Method A and B:

a Set up the plants (barley in liquid culture solution, or radish watered with culture solution) and allow to grow for about 3 weeks (for radish with 24 hour illumination or for barley).

b After 3 weeks, make qualitative observations of plant growth in each medium.

c Collect sample plant material, remove any adhering growth medium (radish) or blot off any liquid (barley). Measure the mass of the living material.

d Place the material in an oven at 80 – 90 °C to dry. Measure the mass every day until 3 readings are constant.

e Record the dry mass of plant material in each culture medium.

f Observe the algal suspension by eye and make qualitative observations of which has grown best.

g Measure the turbidity of each sample using a colorimeter.

h Estimate the population of algae using a microscope and a small grid square, or a haemocytometer.

i Make qualitative observations of the growth of each sample.

j Estimate the area of cover in each beaker/ jar by placing a grid underneath and counting the number of squares covered.

Teaching notes

In summary, any mineral deficiency will result in poor plant growth. It may be difficult for inexperienced botanists/ horticulturists to appreciate the subtle differences between one kind of poor growth and the next. Overall productivity is a simple measure of growth. You could also measure the total height (or length) of a plant leaf or stem (radish/ barley), and note the colour, and the pattern of loss of colour. Several deficiencies result in death of leaf tissue – so you may also notice different patterns of damage to the leaves. It is worth identifying veins and leaf margins and noting any changes in those areas.

Calcium deficiency shows in soft, dead, necrotic tissue at rapidly growing areas – such as on fruits, the tips of leaves and the heart of crops such as celery. If the margins of the leaves grow more slowly, the leaf tends to cup downwards. Calcium deficiency also leaves plants with a greater tendency to wilt than non-stressed plants.

Iron deficiency shows in strong chlorosis at the base of leaves – leading to completely bleached leaves. Bleached areas may develop necrotic spots.

Nitrogen deficiency results in generally poor growth – short, spindly plants – and general chlorosis (lack of chlorophyll). Plants show more tendency to wilt under water stress and to die more quickly. Young leaves at the growing point may still be green but will be small. Other leaves may lack colour entirely. In some plants, the underside of the leaves, and petioles and midribs may develop a purple colour.

Phosphorus deficiency produces dwarfed or stunted plants – perhaps with some necrotic spots on the leaves. They grow more slowly than similar plants not lacking phosphorus.

Sulfur deficiency shows in an overall chlorosis with veins and petioles gaining a reddish colouration. This includes young leaves. Leaves may be twisted and brittle.

Magnesium is an essential part of the chlorophyll molecule. Plants deficient in magnesium frequently show interveinal chlorosis (a lack of chlorophyll).

Potassium deficiency shows first in marginal chlorosis (loss of colour at the tips of the leaves). As this progresses, the leaves may curl and crinkle. Potassium is required for formation of healthy flowers and fruit– beyond the timescale of this investigation.

Related experiments

Identifying the conditions needed for photosynthesis

www.saps.org.uk/primary/teaching-resources/216-adding-mineral-salt-do-radishes-grow-better A link to the SAPS teacher notes on a related practical – investigating the effect of different amounts of mineral fertiliser on plant growth using the ‘radish in canister’ method.

(Website accessed October 2011)

Plants Science Experiments & Teaching How Plants Grow

I love doing plant experiments and sprouting seeds with young children in the spring. Not only do they get excited to see how plants grow but planting seeds also teaches them patience and how to wait for gratification which is very important in this fast-paced, instant gratification world in which we now live.  In this post I’m sharing some of my favorites from over the years.

What Liquid is Best for Growing Seeds? Experiment

This experiment tests what type of liquid is best for growing seeds and can be done using a wide variety of liquids. Since we already discussed that plants need water to grow, we first tested different types of water to see if it made a difference. We decided to test tap water, bottled water, sugar water (1 cup of water with 1 Tbsp sugar), and salt water (1 cup water with 1 Tbsp salt). I used grass seed for this experiment because it sprouts fairly quickly but you can use bean seeds (lima beans soaked overnight in water work well) or any other type of seed you wish.

We added the same amount of soil and seed to each cup and labeled them.  We then measured out the same amount of water for each cup and watered the seeds with the different types of water and set them by the window. Students make predictions as to which one they feel will work best.

We observed the seeds for 5 days and were a little surprised that the bottled water didn’t grow as well as the tap water. The tap water grew the best, followed by the bottled water, the sugar water had a few blades come up, and the salt water did not have any.

When looking at the label of the bottled water we found that additional ingredients are added (calcium chloride, sodium bicarbonate, and magnesium sulfate) which most likely lead to mineral imbalances in the soil that slowed growth. Liquids with very high sugar or salt levels can actually pull water away from the plant or seed rather than allowing the water to be absorbed. In conclusion, simple pure tap water worked best.

We then do the experiment with liquids other than water to see if another type of liquid could be used if water isn’t available. You can use any liquids you have on hand, just make sure that one of them is water to use as the comparison. We have tried vinegar, oil, rubbing alcohol, lemon juice. As expected, water always works best.  Last year students had the idea to test liquids that we drink to see if plants would drink them too. I thought this was very creative! We tested vitamin water, pop (soda), and juice.

We added the same amount of soil and seed to each cup and labeled them.  We then measured out the same amount of liquid for each cup and watered the seeds with the different types of liquids and set them by the window. Students made predictions as to which liquid they feel would work best.

We observed them for a week. Our results were that water was best, followed by the vitamin water. Neither the juice or pop had any sprouts.

Using liquids that are very acidic or very alkaline lead to mineral imbalances in the soil that will kill plants or slow growth.  Liquids with very high sugar or salt levels can actually pull water away from the plant or seed rather than allowing the water to be absorbed.

I have students record their results.

How Plants Drink Science Experiment

This experiment has been around for years and is a great way to  demonstrate to students how plants get water from their roots all the way up to their leaves.

It is very simple to set up. Celery stalks that have leaves at the top work best. The stalks on the inside of the bundle of celery usually have the most leaves.

Cut about an inch or so off the bottom of the celery stalks.

Fill each container about halfway with water and drop 10-15 drops of food coloring in each glass.  Place the celery stalks in the water.

I also like to do a split stalk one. Cut one stalk in half part way up and place one half in one color and the other in a different color.

Observe the celery at the end of the school day.  You may see a little color in the stalk or the leaves. Observe them again the next day and you should see color in the leaves.  After 48 hours you will really notice changes and color in the stalks and leaves showing that the water traveled up through the stalk to the leaves.

The split celery stalk should show the separate colors on each side and then a mix of the colors in the leaves in the middle. In the pictures below the blue is on the left, red on the right, and some purple leaves in the center.

You can cut open the stalks to allow students to see the small tubes inside the stalks that carried up the colored water to the leaves.

After cutting open the celery we discuss the results. I introduce some bigger vocabulary to them when we talk about the science behind the experiment, but I basically just want them to understand that the water travels up the stem through tiny tubes to the leaves.  Here is a simple explanation:

The Science Behind It:

This experiment demonstrates how plants use capillary action to draw water up their stems. Capillary action is the process in which a liquid, like water, moves up something solid, like the tubes (xylem) in the stem.  The leaves help pull the water up the xylem through transpiration. The leaves have little holes that let out the water that the plant is done using. This makes room for more water to come rushing up through the stem.

I have students record their observations by coloring the celery on their recording page (I created pages with the celery already drawn to make it easier for my young students).  Then they write what they learned along the bottom.

Do Plants Need Light? Experiment

This experiment tests whether plants need light to grow.  You can choose to plant 2 containers of seeds and set one in direct sunlight near a window and one in complete darkness OR plant 3 containers and set one in complete sunlight near a window, one in partial light, and one in complete darkness (it is important that there is NO light).

Plant the same number of seeds in each container with the same amount of soil and label each container.

Have students help you decide the best places in the room to place each container (by a sunny window, in a closet that gets NO light, in a file cabinet drawer, on a shelf in partial light, inside a closed box, etc.)

Observe the containers for about 2 weeks (or however long it takes to see growth) watering as needed.  At the end of the experiment, put the containers side by side and discuss the results.

We do 3 containers – one by the window in full sunlight, one on a shelf that gets partial light, and one in the back of the closet behind a box.

The one near the window shows the most growth, the one in the partial light has growth on the side of the container that received partial light and grows towards the light, the container in total darkness has no growth.

Plants need light to grow because it is an important part of photosynthesis, the process plants use to convert carbon dioxide and water into food. Without light, photosynthesis does not work properly and therefore the plant does not get enough food.  However, not all plants need the same amount of sunlight. There are types of plants that need a lot of bright sunshine and some that can survive with only a little light, but in the absence of ALL light plants will not survive. If you had a seed sprout in the dark, it may have used energy stored up in the seed to begin growing but it will not continue to grow without light.

I have students record their results on recording pages.

Growing Grass Science Activity

Growing grass is a great activity to do with young children because it is easy to plant and grows fairly quickly. It also teaches them about the needs of plants and develops patience because they have to wait for the results and observe changes over time.

A fun option that I like to do is put faces on the cups or containers and have the grass be the face’s hair.  You can glue on actual photos of the students’ faces or have them draw faces on the cup or use accessories such as wiggle eyes. You can also do this activity around St. Patrick’s Day and put leprechaun faces on the containers and grow green leprechaun “hair”.

I have students use plastic spoons to fill their cups about ¾ full with dirt/soil. Then have them sprinkle grass seed on top of the dirt. There is no need to measure out the seed, however I usually tell students to cover the dirt with seed (the more seed, the more grass that will grow).  Then have them cover the seeds with a small amount of dirt.

Lastly, I have students water their seeds with a spray bottle. I like using a spray bottle because it prevents over watering (and then once the grass “hair” starts to grow, students pretend the water is hairspray lol).

I have students help determine the best location in the room for their grass seed (next to a sunny window) and guess how many days they think it will take for their grass to grow.

We usually see some type of growth by day 3 or so.

Once it sprouts the grass grows fairly quickly.

I’ve done several different activities with students. One is having them predict how long they think it will take their grass to grow and then recording the actual results.

We practice measurement skills by measuring how tall the grass has grown.

After students’ grass hair grows, I let cut their hair with scissors and then estimate how long they think it will be until it grows back.

Growing Bean Sprouts

This is another experiment that has been around for years but is a wonderful way for students to observe beans sprouting and see what happens underground when a seed is planted.

I have done this experiment 3 different ways.

Growing Beans in a Jar

This is a good method to use if you want to do a class experiment and you do NOT want each student to grow their own seeds.

Stuff a large jar with paper towels.  Students can help.

Slowly pour some water in the jar to wet the paper towels but do not flood it.  If you have any excess water at the bottom pour it out. You want the paper towels to be damp not soaking wet.

Push your seeds down in between the jar and paper towels and make sure they are firmly in place (a snug fit between the jar and towels).

Place several seeds around each side of the jar.  Place the jar near a sunny window.

Check on the jar daily.  You should see a root come out of the seed first within 3 days.  If you used bean seeds you should be able to observe the plant until it grows to the top of the jar.

I like having students keep plant journals because they improve their observation and recording skills and give them a record of the seed’s growth. Students do a recording page for each observation.

Sprouting Beans in Baggies on a Sunny Window

This method requires a bright sunny window on which you can hang baggies that contain the seeds.  You are making a plastic baggie “greenhouse” for the seeds.  You can choose to have each student plant their own beans in their own baggie or plant a few baggies as a class.  If you choose to have students do their own seeds and baggies, it’s a good idea to plant extra seeds in case some students’ seeds do not grow.  If this happens, switch out the seeds when students are not there to ensure that each child has at least one bean that sprouts.

If doing individual bags for each student, have students write their name on their baggie with a marker. Optional: you can also have them write the date. If doing a class experiment, you can write the date on the baggies.

For each baggie, place a dampened, folded paper towel along the bottom. It should have a fair amount of water but not be soaking or dripping wet.

Place one or several bean seeds between the paper towel and the baggie.

Tape or Sticky Tac them on a bright, sunny window.

Check them daily.  You should see a root come out of the seed first within 3 days.

I have students keep plant journals similar to the one shown above but the recording pages are slightly different. I have the baggie already drawn for them to make it easier. Students can also upload real photos to Pic Collage and complete their journals using the app.

Growing Seeds in a Greenhouse on a Window

This method is the same as the baggie method shown above except students make a greenhouse from construction paper and place their baggie in the opening.

Hang them on a sunny window and make daily observations.

The journal pages I use for this method have the greenhouse already drawn to make it easier for students to record results.

We take the bean plants that have grown to the top of the jar or baggies and carefully put them in soil. I explain to students that the plant needs the support and nutrients from soil to continue to grow larger.

What Do Plants Need to Grow? Pages

I like using these pages to check individual student understanding of what plants need to grow.  On the first page they have to circle the correct pictures. On the second page they unscramble the words and write the correct words on the lines.

If you would like to use the printables, activities, word wall cards, label cards, play dough recipe, and more with your students they are available in my  Plants & Flowers Science Activities resource .  It also includes experiments for plants & seeds, step by step directions with photos for easy set-up, plant journal pages, and more.  Click here  to see complete details and photos of each activity.

Have engaging science experiments and STEM activities throughout the entire school year with this money-saving Science & STEM Bundle !

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Flower science experiments & parts of a flower activities, water cycle, rain cycle science experiments and craftivity, share this:.

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I’m Tina and I’ve taught preK and K for 20+ years. I share fun and creative ideas that spark your students’ love for learning. 

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Sunflowers make small moves to maximize their Sun exposure − physicists can model them to predict how they grow

Postdoctoral Associate at the BioFrontiers Institute, University of Colorado Boulder

Disclosure statement

Chantal Nguyen acknowledges support from Human Frontiers Science Program (HFSP), Young Investigator Grant No. RGY0078/2019, and the U.S. Army Research Office Grant No. 78234EG. This research was a collaborative effort with Imri Dromi, Ahron Kempinski, Gabriella E. C. Gall, Orit Peleg, and Yasmine Meroz.

University of Colorado provides funding as a member of The Conversation US.

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Most of us aren’t spending our days watching our houseplants grow. We see their signs of life only occasionally – a new leaf unfurled, a stem leaning toward the window.

But in the summer of 1863, Charles Darwin lay ill in bed, with nothing to do but watch his plants so closely that he could detect their small movements to and fro. The tendrils from his cucumber plants swept in circles until they encountered a stick, which they proceeded to twine around.

“I am getting very much amused by my tendrils,” he wrote .

This amusement blossomed into a decadeslong fascination with the little-noticed world of plant movements. He compiled his detailed observations and experiments in a 1880 book called “ The Power of Movement in Plants .”

In one study, he traced the motion of a carnation leaf every few hours over the course of three days, revealing an irregular looping, jagged path. The swoops of cucumber tendrils and the zags of carnation leaves are examples of inherent, ubiquitous plant movements called circumnutations – from the Latin circum, meaning circle, and nutare, meaning to nod.

Circumnutations vary in size, regularity and timescale across plant species. But their exact function remains unclear.

I’m a physicist interested in understanding collective behavior in living systems. Like Darwin, I’m captivated by circumnutations, since they may underlie more complex phenomena in groups of plants.

Sunflower patterns

A 2017 study revealed a fascinating observation that got my colleagues and me wondering about the role circumnutations could play in plant growth patterns. In this study, researchers found that sunflowers grown in a dense row naturally formed a near-perfect zigzag pattern, with each plant leaning away from the row in alternating directions.

This pattern allowed the plants to avoid shade from their neighbors and maximize their exposure to sunlight. These sunflowers flourished.

Researchers then planted some plants at the same density but constrained them so that they could grow only upright without leaning. These constrained plants produced less oil than the plants that could lean and get the maximum amount of sun.

While farmers can’t grow their sunflowers quite this close together due to the potential for disease spread, in the future they may be able to use these patterns to come up with new planting strategies.

Self-organization and randomness

This spontaneous pattern formation is a neat example of self-organization in nature. Self-organization refers to when initially disordered systems, such as a jungle of plants or a swarm of bees, achieve order without anything controlling them. Order emerges from the interactions between individual members of the system and their interactions with the environment.

Somewhat counterintuitively, noise – also called randomness – facilitates self-organization. Consider a colony of ants.

Ants secrete pheromones behind them as they crawl toward a food source. Other ants find this food source by following the pheromone trails, and they further reinforce the trail they took by secreting their own pheromones in turn. Over time, the ants converge on the best path to the food, and a single trail prevails.

But if a shorter path were to become possible, the ants would not necessarily find this path just by following the existing trail.

If a few ants were to randomly deviate from the trail, though, they might stumble onto the shorter path and create a new trail. So this randomness injects a spontaneous change into the ants’ system that allows them to explore alternative scenarios.

Eventually, more ants would follow the new trail, and soon the shorter path would prevail. This randomness helps the ants adapt to changes in the environment, as a few ants spontaneously seek out more direct ways to their food source.

In biology, self-organized systems can be found at a range of scales, from the patterns of proteins inside cells to the socially complex colonies of honeybees that collectively build nests and forage for nectar.

Randomness in sunflower self-organization

So, could random, irregular circumnutations underpin the sunflowers’ self-organization?

My colleagues and I set out to explore this question by following the growth of young sunflowers we planted in the lab. Using cameras that imaged the plants every five minutes, we tracked the movement of the plants to see their circumnutatory paths.

We saw some loops and spirals, and lots of jagged movements. These ultimately appeared largely random, much like Darwin’s carnation. But when we placed the plants together in rows, they began to move away from one another, forming the same zigzag configurations that we’d seen in the previous study.

We analyzed the plants’ circumnutations and found that at any given time, the direction of the plant’s motion appeared completely independent of how it was moving about half an hour earlier. If you measured a plant’s motion once every 30 minutes, it would appear to be moving in a completely random way.

We also measured how much the plant’s leaves grew over the course of two weeks. By putting all of these results together, we sketched a picture of how a plant moved and grew on its own. This information allowed us to computationally model a sunflower and simulate how it behaves over the course of its growth.

A sunflower model

We modeled each plant simply as a circular crown on a stem, with the crown expanding according to the growth rate we measured experimentally. The simulated plant moved in a completely random way, taking a “step” every half hour.

We created the model sunflowers with circumnutations of lower or higher intensity by tweaking the step sizes. At one end of the spectrum, sunflowers were much more likely to take tiny steps than big ones, leading to slow, minimal movement on average. At the other end were sunflowers that are equally as likely to take large steps as small steps, resulting in highly irregular movement. The real sunflowers we observed in our experiment were somewhere in the middle.

Plants require light to grow and have evolved the ability to detect shade and alter the direction of their growth in response.

We wanted our model sunflowers to do the same thing. So, we made it so that two plants that get too close to each other’s shade begin to lean away in opposite directions.

Finally, we wanted to see whether we could replicate the zigzag pattern we’d observed with the real sunflowers in our model.

First, we set the model sunflowers to make small circumnutations. Their shade avoidance responses pushed them away from each other, but that wasn’t enough to produce the zigzag – the model plants stayed stuck in a line. In physics, we would call this a “frustrated” system.

Then, we set the plants to make large circumnutations. The plants started moving in random patterns that often brought the plants closer together rather than farther apart. Again, no zigzag pattern like we’d seen in the field.

But when we set the model plants to make moderately large movements, similar to our experimental measurements, the plants could self-organize into a zigzag pattern that gave each sunflower optimal exposure to light.

So, we showed that these random, irregular movements helped the plants explore their surroundings to find desirable arrangements that benefited their growth.

Plants are much more dynamic than people give them credit for. By taking the time to follow them, scientists and farmers can unlock their secrets and use plants’ movement to their advantage.

• Mathematics
• Plant science
• Charles Darwin

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• Published: 12 September 2024

Plant growth and nitrate absorption and assimilation of two sweet potato cultivars with different N tolerances in response to nitrate supply

• Wenxue Duan 1 , 2 , 3 , 4 ,
• Shasha Wang 1 ,
• Haiyan Zhang 1 , 2 , 3 , 4 ,
• Beitao Xie 1 , 2 , 4 &
• Liming Zhang 2 , 4 , 5  

Scientific Reports volume  14 , Article number:  21286 ( 2024 ) Cite this article

Metrics details

• Plant sciences

In sweet potato, rational nitrogen (N) assimilation and distribution are conducive to inhibiting vine overgrowth. Nitrate (NO 3 - ) is the main N form absorbed by roots, and cultivar is an important factor affecting N utilization. Herein, a hydroponic experiment was conducted that included four NO 3 - concentrations of 0 (N0), 4 (N1), 8 (N2) and 16 (N3) mmol L -1 with two cultivars of Jishu26 (J26, N-sensitive) and Xushu32 (X32, N-tolerant). For J26, with increasing NO 3 - concentrations, the root length and root surface area significantly decreased. However, no significant differences were observed in these parameters for X32. Higher NO 3 - concentrations upregulated the expression levels of the genes that encode nitrate reductase ( NR2 ), nitrite reductase ( NiR2 ) and nitrate transporter ( NRT1.1 ) in roots for both cultivars. The trends in the activities of NR and NiR were subject to regulation of NR2 and NiR2 transcription, respectively. For both cultivars, N2 increased the N accumulated in leaves, growth points and roots. For J26, N3 further increased the N accumulation in these organs. Under higher NO 3 - nutrition, compared with X32, J26 exhibited higher expression levels of the NiR2 , NR2 and NRT1.1 genes, a higher influx NO 3 - rate in roots, and higher activities of NR and NiR in leaves and roots. Conclusively, the regulated effects of NO 3 - supplies on root growth and NO 3 - utilization were more significant for J26. Under high NO 3 - conditions, J26 exhibited higher capacities of NO 3 - absorption and distributed more N in leaves and in growth points, which may contribute to higher growth potential in shoots and more easily cause vine overgrowth.

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Introduction.

The planting area and production of sweet potato in China rank first worldwide 1 . Nitrogen (N) is one of the essential nutrient elements in plant growth. In sweet potatoes, N plays crucial roles in dry matter accumulation, N absorption, and storage root formation and expansion 2 , 3 , 4 . Associations between N levels and the N uptake, yield, and quality of sweet potato have been reported 5 , 6 . Villagarcia et al. 7 showed that sweet potato presents genotypic differences in N uptake and assimilation. Long vine cultivars with rapid growth of aboveground parts exhibited higher N metabolism levels in leaves 8 . Kelm et al. 9 showed that the translocation efficiency of dry matter and N to storage roots is lower in cultivars with weak growth potential than in those with strong growth potential. Compared with early-maturity cultivars, cultivars with longer growth periods have higher N recovery efficiency and N physiological efficiency 10 . The N-tolerant cultivars showed higher root yields than N-sensitive cultivars under high N conditions 11 . Thus, cultivar is an important factor affecting yield and N absorption and assimilation. However, N utilization in sweet potato cultivars with different N tolerances has rarely been reported.

As the main inorganic N, nitrate (NO 3 - ) affects crop yield and photosynthetic physiology and has a significant impact on plant root growth and nutrient absorption 12 , 13 , 14 . After absorbing NO 3 - in plants, one part is assimilated in roots, and the other part is transported to the aboveground parts through the xylem to participate in the assimilation reaction. Nitrate reductase (NR) is an NO 3 - inducible enzyme, which is the first step of its participation in assimilation; that is, NO 3 - is reduced to NO 2 - under the action of NR. The generated NO 2 - is reduced to ammonia under the action of nitrite reductase (NiR) 15 , 16 . Numerous genes, such as nitrate transporters ( NRT ), NR and NiR, are involved in NO 3 - sensing and signaling networks 17 , 18 . In sweet potatoes, NRT and NR genes are involved in N metabolism and are regulated by N concentrations 19 . Root morphology is closely related to N absorption. NO 3 - can affect root growth and its absorption rate 20 . Previous researchers have reported the relationships between root growth and N absorption, such as the root growth response of different cultivars to N levels and the impact of N management on root morphology 21 , 22 . However, there are few reports on the effects of NO 3 - supplies on early plant growth and N absorption in sweet potato cultivars with different N tolerances. The relationship between root morphology and NO 3 - absorption in sweet potato seedlings also needs to be studied.

In actual sweet potato production in northern China, due to unreasonable N application, vine overgrowth easily occurs even in hilly regions, which results in yield reduction 11 . The rational N distribution between aboveground parts and roots is conducive to source-sink coordination and inhibition of vine overgrowth in sweet potato 23 . NO 3 - is the main form of N uptake by crops, and its distribution between aboveground and underground parts in crops depends on the cultivar, external NO 3 - concentration, and root growth 24 , 25 . However, in sweet potatoes, the information on the physiological and molecular mechanisms involved in NO 3 - absorption and assimilation of cultivars with different N tolerances is limited. In previous studies, we found that the N accumulation in the N-sensitive Jishu 26 (J26) was higher than that in the N-tolerant cultivar Xushu 32 (X32) under similar N conditions supplied by urea. High N nutrition led to a greater increase in N accumulation in the fibrous roots of J26 4 . In the present study, the seedlings of these two cultivars were cultured in modified Hoagland nutrient solution containing four NO 3 - concentrations. The following scientific hypotheses are proposed: (i) The N accumulation of the two cultivars under different nitrate supplies is related to their capacities for NO 3 - absorption and assimilation. (ii) Enzyme activities and the expression of genes such as NR , NiR and NRT1.1 in roots and leaves may vary between the two cultivars, thus affecting their influx rates of NO 3 - and NO 3 - assimilation. (iii) Root length and root surface area may also affect the influx rate of NO 3 - . Thus, in the present study, the responses of root growth, NO 3 - absorption and N distribution and the transcript levels of genes involved in NO 3 - assimilation were assessed. The results helped elucidate the differential mechanism of NO 3 - utilization in different cultivars and provide a theoretical reference for the mechanism of vine overgrowth in sweet potato.

Materials and methods

Experimental design.

Hydroponic experiments were conducted in an artificial climate room at Shandong Academy of Agricultural Sciences, China (36°7′ N, 118°2′ E), in 2021. Two sweet potato cultivars, Jishu 26 (J26, N-sensitive) and Xushu 32 (X32, N-tolerant) 4 , 11 , were selected for hydroponic culture using Hoagland’s modified solution. Four NO 3 - concentrations were set for each cultivar: 0 (N0), 4 (N1), 8 (N2) and 16 (N3) mmol L -1 . In the nutrient solutions with different NO 3 - concentrations, NO 3 - was supplied by Ca(NO 3 ) 2 and KNO 3 . CaCl 2 or KCl was added to maintain consistent concentrations across the treatments 19 . The NO 3 - concentration preparation method is shown in Supplementary Table S1. The concentration of other nutrient elements was referred to a modified Hoagland’s nutrient solution with the following chemical composition: 1 mmol L -1 KH 2 PO 4 , 2 mmol L -1 MgSO 4 , 1 μmol L -1 H 3 BO 3 , 0.74 μmol L -1 MnSO 4 , 0.5 μmol L -1 ZnSO 4 , 0.25 μmol L -1 CuSO 4 , 0.1 μmol L -1 Na 2 MoO 4 ·2H 2 O, 0.025 μmol L -1 KI, and 50 μmol L -1 Na-Fe-EDTA 13 , 19 . The culture conditions were a day/night temperature of 28/22 °C and 70% relative humidity with a light intensity of 300 μmol m -2  s -1 for 16 h. A completely randomized experimental design consisting of two factors was used in this study. The first factor was the two different cultivars, and the second factor was the four different NO 3 - concentrations. For each replicate of each treatment, twelve consistent seedlings (shoots with five to six functional leaves, approximately 20 cm in height) were selected and transplanted into a plastic container (50 cm × 36.5 cm × 14.5 cm) with 15 L of nutrient mixture. During the process of culture, the nutrient mixture was changed every 3 days, and the treatments were arranged completely at random, with four replicates.

Variable measurements

Seedlings were collected at 10 days after culture. The shoots and roots were divided, and the fresh weight (FW) was measured. In each replicate of each treatment, four seedlings were randomly collected and divided into leaves, petioles, stems, growth points (the part above the third expanded leaf from the shoot apex) and roots for dry matter and N content analysis. Another four seedlings were collected, and their third expanded leaves and roots were frozen in liquid N and stored at -80 °C for physiological index and gene expression analyses. The remaining seedlings were used for the flux measurements of NO 3 - and root scanning analysis.

Root growth characteristics

Root growth was evaluated by image analysis. The roots were floated on waterproof trays and scanned using a specialized EPSON V750 scanner (Seiko Epson Corp., Suwa, Nagano, Japan) at a resolution of 300 dpi. WinRHIZO Pro software (Regent Instruments Inc., Quebec, Canada) was utilized to analyse root images, which provided the total root length, root surface area, average root diameter and root volume 26 .

N content and N accumulation

The plant samples divided into different organs were oven-dried to a constant weight at 70 °C to measure their biomass. The total N content was determined with a Kjeldahl apparatus (Kjeltec 8100, FOSS, Hoganas, Sweden) 27 . The N accumulation amount of each organ was calculated by multiplying the element content by the dry weight of each organ. The N distribution ratio of each organ was calculated by dividing the N accumulation amount of each organ by the total N accumulation amount of all organs.

Nitrate content and activities of nitrate reductase (NR) and nitrite reductase (NiR)

Leaves and root samples of 0.1 g were added to 1 mL of distilled water in a centrifuge tube and boiled for 30 min to extract the nitrate. The samples were centrifuged at 12,000 ×  g for 15 min, and 0.1 mL of supernatant was mixed with 0.4 mL of 5% salicylic acid-sulfuric acid solution and incubated at room temperature for 30 min. The 8% sodium hydroxide solution was added, and nitrate was detected at 410 nm at room temperature.

NR and NiR activities were determined in accordance with the method described by Sun et al. 12 and Yu et al. 28 . In brief, leaf and root samples were homogenized in a cold mortar and pestle with extraction buffer. The homogenate was centrifuged at 10,000 ×  g for 20 min at 4 °C. The reaction mixture for determining NR activity contained extract, 25 mM potassium phosphate buffer (pH 7.5), 10 mM KNO 3 , 0.2 mM NADH and 5 mM NaHCO 3 . Controls were generated by adding potassium phosphate buffer instead of NADH. The assays were conducted at 30 °C for 15 min. The reaction was terminated by adding 0.5 M zinc acetate, and excess NADH was oxidized by adding 0.15 mM phenazine methosulfate. The mixture was centrifuged at 10,000 ×  g for 5 min. The NO 2 - level was colorimetrically measured at 540 nm after the addition of 1% sulfanilamide in 2 M HCl and 0.02% N-(1-naphthyl)-ethylene-diammonium dichloride (NNEDD). The assay mixture for NiR activity contained extract, 25 mM potassium phosphate buffer (pH 7.5), 0.5 mM NaNO 2 , and 1 mM methylviologen. The reaction was initiated by adding 0.12 M Na 2 S 2 O 4 and incubated at 25 °C for 60 min. The reaction was terminated by violently agitating the mixture in a vortex mixer. After 1 M zinc acetate was added, the mixture was centrifuged at 10,000 × g for 10 min. The NO 2 - level in the supernatant was determined at 540 nm after diazotation was performed using 1% sulfanylamide and 0.02% NNEDD.

Flux measurements of NO 3 -

The NO 3 - flow rate in roots was measured using a noninvasive microtest system (NMT 100 Series, USA) at Jiangsu Normal University, Jiangsu, China 28 . Under the microscope, the corresponding electrolyte (KON 3 ) was filled into the microelectrode, and the liquid ion exchanger (LIX:XY-SJ-NO 3 - , Younger, USA) was filled into the tip of the electrode. The ion-selective microelectrode was corrected by KNO 3 solutions with a certain concentration gradient. The root tips (3 cm long) from plants subjected to different treatments were collected and placed in a transparent dish. The root samples were fixed with sterile filter paper and resin in the dish. Ten millilitres of hydroponic solution was added, and the flux of NO 3 - was then measured. The location points of steady-state ion flow measurement were the meristem zone (300–600 μm from the root tip), elongation zone (1–3 mm from the root tip) and mature zone (10–15 mm from the root tip). Each root zone was measured for 5 min to obtain NO 3 - flow data, and the recording rate for the ion flux was one reading per 6 s. Positive values of the NO 3 - flow rate represented efflux, and negative values represented absorption. NO 3 - flux data were calculated with MageFlux1.

qRT–PCR assay

Total RNA was extracted using a total RNA extraction kit (Tiangen Biotech Co. Ltd. China) according to the manufacturer’s instructions. Reverse transcription was performed following the manufacturer's protocol (Takara Bio Inc. Japan). The expression levels of the target genes were determined by qRT–PCR using SYBR green fluorescent dye on a Bio–Rad CFX96 thermocycler (Bio–Rad, Hercules, CA, USA). The specific primers for the qRT-PCR assay were referred to Ren et al. 27 and Yu et al. 28 . The expression levels of the genes ( IbNR2 , IbNiR2 and IbNRT1.1 ) were normalized to the level of constitutive IbActin expression, and three biological replicates were used to calculate relative gene expression levels by the 2 − △ △ CT method 29 . The primers are listed in Supplementary Table S2.

Statistical analysis

Two-way ANOVA (cultivars and NO 3 - concentrations) and Duncan’s multiple range test were conducted using the statistical analysis software SPSS (version 17.0 for Windows, SPSS, Chicago, Illinois, USA).

FW of shoots and roots

The effects of cultivar (C) and NO 3 - concentration (NC) on the FW of shoots and roots were significant (Table 1 ). Significant interactive effects of C × NC were detected only in the FW of the roots. With increasing NC to N3, for J26, the FW of shoots and roots first significantly increased but then decreased. However, for X32, there was no significant difference among N1, N2 and N3. Compared with X32, J26 presented significantly greater FW of roots under N2 and N3. These results suggested that the nitrate supply had more significant effects on the growth of J26 plants. However, the growth of X32 was insensitive to the nitrate supply. An excessive nitrate supply was not conducive to the growth of the roots of J26, and this cultivar presented greater growth vigour in its roots.

Root length, surface area, diameter and volume

The effects of C on the root surface area, diameter and volume were significant (Table 2 ). The effects of NC on root length, surface area and volume were significant. The interactive effects of C × NC on these four parameters were significant. With increasing NC to N3, the root length, surface area and volume of J26 significantly decreased. However, for X32, there was no significant difference in root length or surface area among N1, N2 and N3. Compared with X32, J26 presented a significantly greater root surface area, diameter and volume under N0 and N1. These data indicated that excessive nitrate application inhibited root growth, especially in J26. The regulatory effects of nitrate supply on the root growth of J26 were more significant than those on X32.

N content, N accumulation and distribution

For both cultivars, N accumulated mainly in the leaves, stems, and roots (Table 3 ). The effects of C on the N contents in leaves and roots and the N accumulation amounts in leaves, growth points and roots were all significant. The effects of NC on the N contents and N accumulation in all the divided organs were significant, whereas these regulatory effects on the N distribution ratio were not significant. With the exception of petioles, the interactive effects of C × NC on the N contents and N accumulation in other organs were significant. For both cultivars, the N content in all the divided organs increased with increasing NC to N2. N3 further significantly increased the N content in the leaves and roots of J26 and in the petioles of X32. The trends in the N accumulation amount were similar to those in the N content. Compared with X32, J26 presented significantly greater N contents and N accumulation amounts in leaves and roots under N1, N2 and N3. J26 also presented greater N accumulation at growth points under N1, N2 and N3. The results indicated that excessive nitrate increased the N contents and N accumulation amounts in the leaves and roots of J26 but did not further increase those of X32. J26 accumulated more N in the leaves, growth points and roots under relatively high nitrate levels.

NO 3 - content and activities of NR and NiR in leaves

The effects of C and NC on the NO 3 - content and activities of NR and NiR in leaves were significant (Fig.  1 ). The significant interactive effects of C × NC existed only for the NO 3 - content. For both cultivars, the NO 3 - content in leaves significantly increased with increasing NC to N3. A similar trend was observed for the NR activity in the leaves of J26. However, N3 did not further increase these values for X32. N3 did not further increase NiR activity in J26, whereas no significant difference was detected among N1, N2 and N3 for X32. Compared with X32, J26 presented significantly greater NO 3 - content and NR activity in leaves under N1, N2 and N3 and greater NiR activity in leaves under N2 and N3. These data suggested that excessive nitrate could further increase the NR activity in leaves only for J26. J26 had a greater ability to assimilate and metabolize nitrate in its leaves.

Nitrate content ( a ) and NR ( b ) and NiR ( c ) activities in leaves under different N treatments. Means denoted by different letters are significantly different at P  < 0.05 as determined by Duncan's multiple range test. N0, N1, N2 and N3 represent 0, 4, 8 and 16 mmol L-1, respectively. J26 and X32 represent cultivar Jishu 26 and Xushu32, respectively.

NO 3 - content and activities of NR and NiR in roots

The effects of C on the NO 3 - content and NR activity in roots were significant (Fig.  2 ). The effects of NC and the interactive effects of C × NC on the NO 3 - content and activities of NR and NiR in roots were significant. For both cultivars, the NO 3 - content in the roots significantly increased with increasing NC to N3. A similar trend was observed for NiR activity in the roots of both cultivars. N3 further increased the NR activity in the roots of J26, whereas no significant difference was detected between N2 and N3 for X32. Compared with X32, J26 presented significantly greater NO 3 - contents under N1, N2 and N3 and greater NR activity in roots under N2 and N3. The results suggested that excessive nitrate could further increase the activities of NR and NiR in roots only for J26. The regulatory effects of NC on the activities of NR and NiR in roots were more significant for this cultivar, which presented a greater ability for nitrate assimilation and metabolism in roots.

Nitrate content ( a ) and NR ( b ) and NiR ( c ) activities in roots under different N treatments. Means denoted by different letters are significantly different at P  < 0.05 as determined by Duncan's multiple range test. N0, N1, N2 and N3 represent 0, 4, 8 and 16 mmol L -1 , respectively. J26 and X32 represent cultivar Jishu 26 and Xushu32, respectively.

For both cultivars, the mean flux of NO 3 - exhibited an influx under the NO 3 - -supplemented treatments in all the tested root regions (Fig.  3 ). The effects of C on the mean flux of NO 3 - in the meristem and mature zones were significant. The effects of NC and the interactive effects of C × NC on the mean flux of NO 3 - in all the tested root regions were significant. For both cultivars, the mean influx of NO 3 - in the meristem zone significantly increased with increasing NC from N1 to N3. A similar trend was observed in the elongation zone for J26. In the mature zone, the mean influx of NO 3 - was the highest under N2 for this cultivar. For X32, there was no significant difference in the mean influx of NO 3 - in the elongation and mature zones between N2 and N3. Compared with X32, J26 presented a significantly greater mean influx of NO 3 - in the meristem and mature zones under N1, N2 and N3. These data indicated that excessive nitrate could further increase the mean influx of NO 3 - in the meristem and elongation zones of J26 but did not further increase NO 3 - in the elongation and mature zones of X32. The roots of J26 presented a greater ability to absorb nitrate under the same NC.

The mean rate of NO 3 - flux at the meristem (300 ~ 600 μm from the tip), elongation (1 ~ 3 mm from the tip), and mature (10 ~ 15 mm from the tip) root zones. Means denoted by different letters are significantly different at P  < 0.05 as determined by Duncan's multiple range test. N0, N1, N2 and N3 represent 0, 4, 8 and 16 mmol L -1 , respectively. J26 and X32 represent cultivar Jishu 26 and Xushu32, respectively.

Gene expression of NiR2 , NR2 , and NRT1.1 in roots

The effects of C and NC and the interactive effects of C × NC on the gene expression of NiR2 , NR2 , and NRT1.1 in roots were significant (Fig.  4 ). With increasing NC to N3, the expression level of NR2 significantly increased for both cultivars. Similar trends were observed in the expression of NiR2 and NRT1.1 . Compared with X32, J26 presented significantly greater expression levels of NR2 under N2 and N3 and greater expression levels of NiR2 and NRT1.1 under N1, N2 and N3. The results suggested that excessive nitrate could further increase the gene expression of NiR2 , NR2 , and NRT1.1 in the roots of both cultivars. The regulatory effects of NC on the expression of NiR2 , NR2 , and NRT1.1 in J26 were more significant than those in X32. J26 presented increased expression of genes related to nitrate assimilation under higher NC.

The expression levels of the genes involved in nitrogen metabolism in roots under different N treatments. Means denoted by different letters are significantly different at P  < 0.05 as determined by Duncan's multiple range test. N0, N1, N2 and N3 represent 0, 4, 8 and 16 mmol L -1 , respectively. J26 and X32 represent cultivar Jishu 26 and Xushu32, respectively.

High NO 3 - supply could impair the FW of roots and shoots in plants 30 . However, in wheat, a high NO 3 - supply revealed no significant effects on the dry weight (DW) of roots in cultivar BTS but decreased these in cultivar GE 18 . Similar results of FW of shoots were observed in sweet potato seedlings, and the FW of roots increased initially and then decreased for most of the tested cultivars 19 . In the present study, there were differences in plant growth between the two cultivars with different N tolerances in response to nitrate supply. The growth of the roots and shoots of J26 was significantly regulated by the NO 3 - supply, whereas that of X32 was relatively stable, indicating that the regulatory effects of the nitrate supply on the growth of J26 plants were more variable. For sweet potato seedlings, Yao et al. 19 reported that the total N content in roots and shoots was increased by high N concentrations. In the present study, for both cultivars, increasing NO 3 - concentrations to 8 mM increased the N contents and N accumulation amounts in the leaves and roots. For J26, 16 mM NO 3 - further increased the N contents in leaves and roots and the N accumulation amounts in the organs of leaves, growth points and roots. However, these parameters showed no significant increase for X32. Although higher NO 3 - concentrations caused a decrease in the FW of roots for J26 or showed no significant increase for X32, higher N accumulation amounts in roots were obtained for both cultivars (Table 3 ), suggesting that higher N content was the reason for the higher N accumulation. Compared with X32, J26 may have a higher N assimilation capacity, which resulted in higher N contents in leaves and roots, and more N was accumulated in these organs. Moreover, the higher N accumulation amount at the growth points of J26 suggested that high N accumulation in this metabolic centre may increase the source-sink distance and promote shoot growth 8 .

High NO 3 - supplies could inhibit root growth 25 . According to Adavi and Sathee 18 , high NO 3 - supply reduced the total root length but showed no significant effects on the root surface area for wheat cultivar BTS. However, it decreased the root surface area for cultivar GE. In the present study, increasing NO 3 - concentrations decreased the total root length and surface area of the N-sensitive cultivar J26. However, for X32, the NO 3 - supply had no significant effect on these two parameters. Thus, the regulatory effects of nitrate supply on root length and surface area were more significant for J26 than for X32. The NO 3 - supply had significant effects on the NO 3 - influx in roots, and the regulatory effects were related to NO 3 - concentrations in nutrient solution and plant species 31 , 32 . In the present study, for J26, the highest mean influx of NO 3 - in the meristem and elongation zones was both obtained under 16 mM NO 3 - while that in the mature zone was obtained under 12 mM NO 3 - . For X32, 16 mM NO 3 - further increased the mean influx of NO 3 - merely in the meristem zone. Moreover, compared with X32, J26 presented a significantly greater mean influx of NO 3 - in the meristem and mature regions. Thus, the relatively high NO 3 - content in the roots and leaves of J26 was related to the relatively high mean influx of NO 3 - in the roots. The N-sensitive cultivar J26 presented a high capacity for nitrate uptake, especially under relatively high nitrate levels. Crop plants may expand their absorption area by maintaining a higher total root length and surface area, aiming to absorb a larger amount of N nutrients under low-N conditions 32 . With sufficient N supply, the increased N uptake was achieved mainly by the increased root density and total root length, rather than by the N uptake rate 33 , 34 . In the present study, high NO 3 - concentrations decreased the total root length and surface area for J26 and showed insignificant effects on these two parameters for X32. However, a higher NO 3 - influx rate was obtained under high NO 3 - concentrations. Thus, the higher NO 3 - influx rate in roots for these two sweet potato cultivars was not associated with the total root length and surface area. The assimilation of N genes and upregulation of NRT may contribute to a stronger N uptake capacity for plant N accumulation. In apple seedlings, a higher NO 3 - influx rate in roots was associated with higher NR activities and upregulated transcription of MdNRT1.1 in roots 35 . For the sweet potato seedlings in this study, the high NO 3 - concentrations upregulated the expression levels of NRT1.1 , NR2 and NiR2 in roots and increased the activities of NR and NiR in roots (Figs.  2 ), indicating a higher N uptake capacity 36 , which was the main reason for the higher NO 3 - influx rate in roots. Similarly, compared with X32, J26 exhibited higher N uptake capacity in roots and higher levels of leaf N metabolism, especially under higher NO 3 - concentrations, thus leading to the accumulation of more N in roots and shoots (Table 1 ). Thus, for the two cultivars, the relatively high NO 3 - influx rate was related mainly to the upregulated expression of NRT1.1 , NR2 and NiR2 and the relatively high activities of NR and NiR but not to the parameters of root length and surface area.

NRT1.1 is a well-known NRT gene involved in the uptake and translocation of NO 3 - . When NO 3 - is sufficient in the environment, NRT1.1 displays robust nitrate transport activity to meet the N demand 37 . In cucumber, a high NO 3 - supply upregulated NRT1.1 gene expression in roots 38 . In sweet potato seedlings, the coding genes for NRT were regulated by N concentration, and the expression of the NRT1.1 gene in roots was upregulated under higher NO 3 - concentrations 19 . In the present study, with increasing NO 3 - levels, the expression of the NRT1.1 gene was upregulated for both cultivars. J26 exhibited higher expression levels of this gene, i.e., NO 3 - induced higher expression levels of NRT1.1 , which was conducive to a higher NO 3 - influx rate and higher NO 3 - content in roots for this cultivar 34 . Moreover, the range of increase in the NRT1.1 gene for J26 was greater than that for X32, suggesting that the regulatory effect of NO 3 - supplies on the expression of NRT1.1 was more significant for J26. This may be one reason that the NO 3 - absorption of J26 was sensitive to the NO 3 - supply. The activities of N-assimilating enzymes play a significant role in maintaining growth and development 39 . An appropriate amount of N increased the activities of NR and NiR in the organs of leaves and roots 40 , 41 . In the present study, for J26, the activities of NR and NiR in roots and leaves were significantly increased when increasing NO 3 - concentrations to 16 mM. However, for X32, this concentration merely increased the root NiR activity but did not further increase the NR activity in roots and leaves. NO 3 - supplies did not affect the leaf NiR activity for this cultivar. The changes in the enzyme activities of J26 were relatively more variable than those of X32. Compared with X32, J26 presented greater NR and NiR activities in roots and leaves under higher NO 3 - concentrations, suggesting that J26 presented greater NO 3 - assimilation capacity and greater demand for N. Enzyme activity is subject to regulation at the level of gene expression 42 . According to Liao et al. 41 , for citrus cultivar ‘Huangguogan’, the trends in the activities of NR and NiR in roots were consistent with those of HgNR and HgNiR transcription, respectively. In sweet potato, the expression level of the NR gene in leaves was upregulated by high N conditions, and the leaf NR activity was increased 43 . In the present study, for both cultivars, the expression levels of NR2 and NiR2 in roots were upregulated by NO 3 - concentrations. The trends in the activities of NR and NiR were subject to regulation of NR2 and NiR2 transcription, respectively. Moreover, the range of increase in the NR2 and NiR2 genes for J26 was greater than that for X32. Combined with the expression data of the NRT1.1 gene, the regulatory effects of NO 3 - supply on the expression of these genes in J26 were more significant, accordingly, the changes in NO 3 - transportation and in enzyme activities related to NO 3 - assimilation were more variable, which may explain why the N availability of this cultivar was sensitive to NO 3 - supply. Although NO 3 - induced NR protein and activity, it is important to note that NR transcripts were present in the roots and leaves of plants grown in NO 3 - -free medium 44 . This phenomenon also existed in the present study and was related to the NO 3 - already contained in the sweet potato seedlings.

As the external NO 3 - concentration increases, NO 3 - assimilation into amino acids in plants is increasingly achieved, which become the main sites of NR activity 45 . Since the NR activity is much higher than that of NiR, there is almost no accumulation of NO 2 - in plants, so NR is the rate-limiting enzyme. The NO 3 - contents were enhanced both by increasing NR activity and upregulating NRT genes 27 . In the present study, NO 3 - supplies induced higher NR activity in roots and leaves and upregulated the expression level of NRT1.1 in roots, which promoted more N absorption in roots and N assimilation in leaves for both cultivars. For J26, 16 mM NO 3 - further increased the NR activities in roots and leaves and the NO 3 - influx rate in roots. However, for X32, this concentration exhibited no significant increase in these parameters. J26 maintained higher N absorption and assimilation capacity under higher NO 3 - supplies. On the one hand, this was related to the higher N absorption and assimilation capacity for this cultivar, as indicated by upregulated expression of NRT1.1 and higher NR activity, on the other hand, this may be related to the higher N distribution in leaves and at growth points (Table 1 ), which may transmit feedback signals from long-distance NO 3 - transport and contribute to higher NO 3 - influx in roots 34 . Plants with higher growth rates may require more N metabolites, and growth can be a driving force for the N metabolism of plants 46 . Higher root growth can lead to greater N demand 47 . The N accumulation amount of J26 was higher than that of X32 (Table 1 ), suggesting that more N was needed for plant growth of J26 and that the N demand for this cultivar was higher, as indicated by the higher FW of roots (Table 1 ) and higher source-sink average distance observed in our previous research 11 .

For J26, NO 3 - supplies exhibited obvious regulatory effects on root length, root surface area, and NO 3 - absorption and assimilation. 16 mM NO 3 - still yielded significant increases in the NR activities in leaves and roots and the NO 3 - influx rate in roots for this cultivar. The N accumulation was also increased in leaves, growth points and roots. However, for X32, NO 3 - supplies showed a more moderate influence. Under higher NO 3 - nutrition, compared with X32, J26 exhibited higher expression levels of the NR2 , NiR2 , and NRT1.1 genes and promoted a higher net influx of NO 3 - in roots. The higher NO 3 - influx rate was mainly associated with the upregulated expression of NRT1.1 , NR2 and NiR2 and higher activities of NR and NiR. Based on statistical analysis (C, NC, C × NC), J26 is a cultivar to have higher N availability and this contributes biomass production, especially below ground. The key differentially expressed genes related to N utilization in the roots of the two cultivars need further exploration under different NO 3 - concentrations.

Data availability

All data generated and/or analyzed during the current study are included in this article.

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The research program was sponsored by the Natural Science Foundation of Shandong Province (ZR2021MC092), the China Agriculture Research System of MOF and MARA (CARS-10-GW09), the Tubers and Root Crops Innovation Team of Modern Agricultural Technology System in Shandong Province, China (SDAIT-16–09) and the Key Research and Development Program of Shandong Province, China (2023TZXD001).

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Wenxue Duan, Haiyan Zhang and Liming Zhang conceived and designed the study. Wenxue Duan, and Shasha Wang performed the experiments. Wenxue Duan and Beitao Xie analyzed the data. Wenxue Duan wrote the paper. Liming Zhang reviewed and edited the manuscript. The final version was read and approved by all authors.

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Duan, W., Wang, S., Zhang, H. et al. Plant growth and nitrate absorption and assimilation of two sweet potato cultivars with different N tolerances in response to nitrate supply. Sci Rep 14 , 21286 (2024). https://doi.org/10.1038/s41598-024-72422-y

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Research Article

Comparative analysis of growth cycles among three weedy Avena species: Insights from field observations

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Plant Protection, Faculty of Agriculture, Ege University, Bornova, İzmir, Turkey

• Süleyman Gürdal Türkseven

• Published: September 13, 2024
• https://doi.org/10.1371/journal.pone.0307875
• Reader Comments

Avena species, especially A . fatua , A . sterilis and A . ludoviciana , are among the most problematic weed species in many crops worldwide. The growth cycles of these three species could be helpful in understanding their growth cycle and their implications for agriculture and weed management. The growth cycles of these species were studied altogether or in combination with any single or double combinations of the other species in cereal fields in Türkiye, using two populations of each in a common garden experiment in Bornova district, Izmir, Türkiye. Germination and growth experiments were conducted in the laboratory and screen house, respectively. Various phenological parameters were recorded during the experiment and data were analyzed using R software. There were no significant differences in germination, emergence, SPAD values, leaf width, plant height, or plant dry weight among the species or populations. The SPAD values and width of the flag leaf and the leaf before the flag leaf were strongly correlated. Plant weight increased with increasing tiller number. The length of the ligule in a population of A . sterilis was significantly greater than that in populations of two other species, and it was concluded that the species is not A . fatua or A . ludoviciana if the length of the ligule is greater than 10 mm. The length of the spikelets of A . sterilis was greater than 65 mm with awn and greater than 35 mm without awn; these values were significantly greater than those of two other species that were shorter than 55 with awn for A . fatua and 30 mm without awn for A . ludoviciana , respectively. Avena ludoviciana had fewer tillers than the other two species. The plants emerged at 37.58 GDD at the soil surface temperature, which corresponds to 7 days after sowing. The growing cycles of the species differed: 196 days for A . sterilis , 201 days for A . fatua , and 209 days for A . ludoviciana after emergence, although there were no clear differences in earlier growth stages. This study provides initial basic information about the Avena spp., and it is concluded that even if a field has mixed Avena populations, herbicides can be applied simultaneously because the early development stages of the three species are very similar. In future prospects, there is a need for proper studies about the management of all Avena spp. on the basis of growth stages and growing degree days in regional context.

Citation: Türkseven SG (2024) Comparative analysis of growth cycles among three weedy Avena species: Insights from field observations. PLoS ONE 19(9): e0307875. https://doi.org/10.1371/journal.pone.0307875

Editor: Anwar Hussain, Abdul Wali Khan University Mardan, PAKISTAN

Received: February 3, 2024; Accepted: July 14, 2024; Published: September 13, 2024

Copyright: © 2024 Süleyman Gürdal Türkseven. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The author confirms that the data supporting the findings of the article are inside the paper. The raw data that support the paper are available as a supporting file separately.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Avena species are among the most important weeds worldwide [ 1 ] due to their ability to occupy complex habitats via genetic flexibility under climate change scenario [ 2 ]. Among the nine weedy Avena species, A . fatua (AVEFA), A . sterilis (AVEST) and A . ludoviciana (AVELU) have the largest distribution as compared with other Avena spp. and are distributed in more countries [ 3 ]. In Australia, AVEFA and AVELU are found together in several fields [ 4 ]. In Türkiye, Avena species became more important weeds in winter cereals in several regions of the country during the 1980s [ 5 ]. AVEFA, AVEST and AVELU can be found together in a field, and combinations of any two species are more common. Avena ludoviciana (this name will be used in the text) is accepted as a subspecies of A . sterilis by many authorities [ 6 , 7 ].

Avena species cause yield and quality losses in many crops, especially in winter cereals, and lead to decrease in biodiversity [ 8 – 16 ]. The foremost method for controlling Avena species is the use of herbicide, but their extensive use causes herbicide resistance [ 9 ]. Herbicide-resistant Avena species have caused problems that are becoming more complex in Türkiye as well as in other countries [ 17 – 20 ].

Successful and sustainable control of Avena species requires increased knowledge of their ecology and biology as well as accurate prediction of growth stages [ 21 – 25 ]. Previously germination and emergence studies included the effects of temperature, water potential, dormancy, and burial depth, but conflicting results were observed [ 22 ]. AVEFAs were able to germinate at a much greater percentage and at deeper levels in greenhouses than in fields [ 26 ]. It showed better emergence at the 2–8 cm depth than at deeper depths. A large quantity of seeds was also emerged from deeper depths, although germination rate was higher at shallower depths [ 22 ]. The varying dormancy patterns among the AVEFA accessions were attributed to location and season [ 24 ]. The germination temperature for AVEST in petri dish experiments was observed and 2°C was the minimum temperature, 30°C was the maximum temperature while the optimum temperature was 10°C [ 27 ]. The emergence temperatures for AVELU were reported to be -1°C as the base temperature, 5.8°C as the optimum temperature and 18°C as the maximum temperature [ 28 ]. AVEST and AVEFA germinated at 5–30°C and osmotic potentials of -25 to -1400 KPa, but AVEST germinated and emerged better at 10°C and above 20°C [ 29 ]. In another study with AVEFA, neither germination nor emergence occurred at 32°C, but germination was greater between 10–21°C, and germination was slower at 10°C than at higher temperatures [ 22 ]. The field capacity was not allowed for the emergence of AVEFA, but between 50 and 75% of the field capacity was the best for emergence [ 22 ].

The growth characteristics of Avena spp. have also been widely studied. The AVEFA accessions showed variation in plant height, number of tillers, days to panicle emergence, grain yield, and response to certain herbicides, such as diclofop and flamprop [ 24 ]. In another study, variations in the tiller and seed numbers of AVEFA between years were detected, although these differences were attributed to the greater precipitation that caused greater numbers of plants. However, there was also a difference among individuals [ 26 ]. The plant height and number of leaves per plant of the AVEFA increased almost linearly during the first 6 weeks after emergence, and tillering occurred mainly between the 2 nd and 4 th weeks after emergence; however, the dry weight accumulation was slow in the first three weeks and then increased until the 8 th week [ 23 ]. The optimum temperature for growth was 20/12°C day/night, although at 28–20°C plants performed better in the initial growth stages [ 23 ]. Low fertilizer amounts and light intensities caused lower levels of growth parameters, and the photoperiod also affected AVEFA growth [ 23 ].

To date, the development stages of Avena species, including their connection with emergence times have been studied for more than 100 years. However, important weedy Avena species have not been compared in common garden experiments, although some studies have compared two of them. The present study aimed to investigate the differences in growth stages among three common Avena species through a common field experiment.

Materials and methods

Seed source.

Seeds from six different populations were collected from wheat fields in different parts of Turkey during June 2013. These seeds were collected from Mediterranean, Aegean and Southeast Anatolia region as these are the major region of Turkey and represent whole country. The author confirms that permission for seed collection was taken from the respective farmers and study did not involve protected or endangered plant species, hence, there was no need to take permission from specific authority. Seeds were cleaned and kept in a refrigerator at 4°C until use ( Table 1 ). All populations were used in all the experiments.

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https://doi.org/10.1371/journal.pone.0307875.t001

Germination experiments

Germination experiments were performed to determine whether there was a difference in germination patterns among the three Avena species. The six populations mentioned earlier were used and experiment was conducted using completely randomized design (CRD). Four replications were performed in an incubator adjusted to 10°C according to the study [ 29 ] on 10 November 2014 and the same pattern was repeated afterward. Seeds were kept in 5% NaClO solution for 30 seconds to sterilize the seed surfaces, followed by rinsing twice with distilled water for 60 seconds. For each Petri dish (90 mm in diameter), 15 seeds were placed into two layers of Whatman No: 1 filter paper, and then 5 ml of distilled water with 0.01% KNO3 was added. Germinated seeds whose radicles became longer than the diameter of the seeds was counted and removed from the Petri dishes daily. The experiment terminated on the 15 th day, and the remaining ungerminated seeds were subjected to the triphenil tetrazolium chloride test (TTC) using 0.01% TTC solution. The seeds were kept at 40°C for one and a half hours, and seeds with red color were considered alive.

Growth experiment of Avena species

A screen house experiment was conducted in the Plant Protection Department, Faculty of Agriculture, Ege University, Bornova, Izmir (38° 27′ 26″ N and 27° 13′ 47″ E) using all six populations. The experimental area was disinfected to avoid any additional species interference. Small plots of one square meter were established and fertilized similarly to common farmer practices in the wheat field in the region i.e., NPK 150–50–60 kg/ha. Temporary screen houses were established on plots using 40 meshes of screen material. Avena seeds were sown at a 10 cm inter-row distance on 12 November 2014, and the soil was good for wheat sowing, as farmers have done to simulate growth in a wheat field. During the observation period, other weed species were removed. The growth stages were recorded daily according to the BBCH scale [ 30 ]. As soon as the first ten plants in all plots emerged (BBCH 10), these plants were marked in each plot to follow the growth and other parameters.

After the flag leaf opened, SPAD values were measured from the flag leaf and leaf before the flag leaf on 10 April 2015. On the same day, the width and ligule length of the leaf before the flag leaf were measured.

Although the observed species did not mature on the same date, all the populations were harvested by cutting them from the soil on 25.05.2015. After separating 10 seeds with or without awn, the plant height was measured, and the number of tillers was counted. The harvested plants were subsequently placed in a dryer at 65°C for 48 hours and their dry weight was recorded using measuring scale.

Growth data were explained using growing degree days (GDD) as well as calendar days. The GDD for emergence was calculated according to [ 31 ]:

If the soil temperature was between Tb and To, then,

DTD = T − Tb

If the soil temperature was between To and Tc

DTD = (T − Tb)*[1 − (T − Tb)/(Tc − Tb)]

and if T < Tb or T > Tc, then TT = 0

where T is the mean daily temperature; Tb is the base temperature; To is the optimum temperature; and Tc is the maximum temperature; and Tb = −1.0, To = 5.8, and Tc = 18.0 C for AVELU (31). In other published studies, different values of Tb = 2, T0 = 10 and Tc = 30 were found for AVEST (27); moreover, Tb = 2.2 was found for AVEFU [ 32 ].

For the growth equation, DTD = T–Tb was used. In this case, Tb was set to 0, as in [ 33 ].

The data were subjected to ANOVA and descriptive statistics using R software (R Core Team, 2023) [ 34 , 35 ]. Correlation analysis was also done for comparing all recorded parameters.

There was no statistically significant difference in germination among the populations. All three species germinated within seven days after sowing and the germination rate ranged from 97–100% (median = 100.00%; average = 99.50%), and the aliveness of the seeds ranged from 88–100% (median = 95.50%; average = 94.67%).

The height of the spikelet with awn and the spikelet itself showed the highest correlation value ranging between 0.91 and 0.51 respectively. The SPAD values and width of the flag leaf and the leaf before the flag leaf were strongly correlated and were 0.71 and 0.65, respectively. The number of tillers and the dry weight of the plants also exhibited a greater correlation (0.66). Lower positive correlations were found for spikelet (with and without awn) and width of flag leaf, which were 0.47 and 0.51, respectively. Ligule length was the correlated length of the spikelet with awn (0.53) and without awn (0.65). There was only a slight negative correlation between the number of tillers and the number of leaves before the flag leaf (-0.38) ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0307875.g001

The SPAD value of the flag leaves did not significantly differ among the species, although AVELU generally had lower SPAD values ( Fig 2A ). Populations ada03 and diy04 had higher SPAD values and were significantly different from diy03, which had lower SPAD values ( Fig 2B ; Table 2 ).

(A), species level; (B), population level.

https://doi.org/10.1371/journal.pone.0307875.g002

https://doi.org/10.1371/journal.pone.0307875.t002

There was no significant difference among the species in terms of the SPAD values of the leaf before the flag leaf, although AVEST mostly had higher SPAD values and AVELU had lower SPAD values ( Fig 3A ). Similar differences were observed between populations ada03 and diy03 ( Fig 3B ; Table 2 ). In addition, the SPAD value of population izm01 was significantly different from that of ada03 before the flag leaf was removed ( Fig 3B ).

https://doi.org/10.1371/journal.pone.0307875.g003

The length of the ligules on AVEST was greater in general, and ada03 had significantly greater ligule lengths than did the other populations ( Fig 4 ; Table 2 ).

(A), species level with awn; (B), population level with awn.

https://doi.org/10.1371/journal.pone.0307875.g004

The width of the flag leaf exhibited high variation among individual populations ( Fig 5A ). There was no significant difference among the species or populations, although the average width was greater for AVEST ( Fig 5B ; Table 2 ).

https://doi.org/10.1371/journal.pone.0307875.g005

Although the AVEFA had lower width values, there was no significant difference among the species or populations ( Fig 6A ; Table 2 ). There was considerable variation in the population of 013ada01 individuals ( Fig 6B ).

https://doi.org/10.1371/journal.pone.0307875.g006

AVEFA had the highest number of tillers (mean 15.20, minimum 8.00, maximum 25.00), while AVELU had the lowest tiller numbers (mean 6.15, minimum 3.00, maximum 13.00) ( Fig 7A ; Table 2 ). The average number of tillers of AVESTs was 9.95 (min 5.00, max 16.00). The population 013ada03 from the AVEST was similar to both AVELU population ( Fig 7B ). However, only the AVELU populations were significantly different from the AVEFA populations.

https://doi.org/10.1371/journal.pone.0307875.g007

The plant height did not differ between species or at the population level, but the AVELU populations exhibited greater intrapopulation variation ( Fig 8(A) , 8(B) ; Table 2 ). The tallest and shortest individuals were in the AVELU group.

https://doi.org/10.1371/journal.pone.0307875.g008

Species did not differ in dry weight ( Fig 9A ), but two populations, Diy04 and Diy03, were significantly different ( Fig 9B ).

https://doi.org/10.1371/journal.pone.0307875.g009

One week after sowing (19 Nov), all the populations were observed at the soil surface, for which the GDDacc was 32.37 ( Table 3 ). A day later, the first true leaf emerged only at population diy03, and the following day, all the populations had a true leaf. In addition, the first true leaf unfolded only at pop ada02.

https://doi.org/10.1371/journal.pone.0307875.t003

The development stages and their relationships with GDDacc are shown in Fig 10 , and their starting dates are given in Table 4 . All the populations developed five leaves except diy04, which had 6 leaves. The tillering stage started on 30 December with GDDacc 473.52 in four populations with one or two tillers. Tillering started four days later for the Ada03 and diy03 populations at GDDacc 489.05. The populations had different number of tillers, as follows: ada02 and diy04 eight, diy03 six and the remaining populations had seven tillers. Stem elongation started on 17 March (GDDacc 1183.45) for three populations, and the other populations exhibited one-day differences: ada03 on 18 March, diy03 on 19 March and izm01 on 20 March (GDD acc 1205.80). Up to six nodes were visible (ada03), the lowest was four (izm01), and the remaining had five nodes. Booting started between 01 April (ada03) and 07 April (ada02) and between these two AVESTs in the other populations. The first heading occurred on diy03 on 07 April (GDDacc 1393.80), followed by four other heading events on 09 April (GDDacc 1412.85) and ada02 15 April (GDDacc 1493.70). The first flowering population was ada03 on 15 April (GDDacc 1493.70), which was immediately followed by other populations except ada02, which was on 30 April (GDDacc 1724.65). Ripening started between 8 and 12 May, and all populations reached the dough stage between 19 and 21 May. AVEST completed its life cycle on 03 June (GDDacc 2423.82), AVEFA on 8 June (GDDacc 2543.97) and AVELU at 16 June (GDDacc 2735.72).

https://doi.org/10.1371/journal.pone.0307875.g010

https://doi.org/10.1371/journal.pone.0307875.t004

All the populations emerged seven days after sowing, similar to the previous study which had reported the emergence of Avena spp. in 7–10 days [ 24 ]. The reason for the uniformity in germination is their ability to adopt a wide range of environmental conditions [ 12 ]. Also, the seed viability and persistence are widely dependent on environmental conditions soil factors [ 24 ]. Avena spp. are capable of germinating at a temperature range of 5–30°C and -0.025–1.4 MPa solute potential [ 29 ]. Tillering started 41 DAE, but two populations AVEST and AVELU (13ada03 and 13diyo3) were 4 days late. Tillering lasted 77 (74–80) days [ 22 ], which found that 2–4 weeks after emergence was the main tillering time for AVEFA; however, in another study, tillering started at 32 nd DAE and completed 70 th to 84 th DAE regarding the emergence time of cohorts [ 25 ]. However, the accumulated GDD difference were only 15.53. The reason for a slight change in tillering duration is that the origin of these populations, as population from southern grain region produced tillers 7–8 days earlier as compared with northern grain region of Australia [ 36 ]. Similarly, the beginning of stem elongation occurred between 17 and 20 March (118–121 days after emergence); again, the difference in accumulated GDD was 22.35 only among the populations. Booting started on 01 April (133 days after emergence) for ada03, and the latest one was ada02 on 07 April; there was a greater difference (GDDacc 78.9) between these AVEST populations. Ada02 was also the most retarded population during heading and flowering, and the difference in accumulated GDD was greater than that in the earliest populations (99.9 and 230.95, respectively). Retardation of ada02 cells continued in the early stages of maturation, but the difference in accumulated GDD decreased (82.75). The fastest population was mostly the other AVEST, ada03. Even in the dough stage, there were two days of retardation, during which the difference in accumulated GDD decreased. The end of the life cycle of these two AVEST populations was the same at 196 days. AVEFA completed its life cycle 201 days after emergence, and AVEFU completed its life cycle 209 days after emergence. The variability in these parameters and differences in the life cycle is strongly correlated with the soil conditions and environmental factors [ 2 , 24 ]. All these parameters are helpful for adopting management strategies for these spp. For example, previous data showed that December will be a good time to apply herbicides because early applications (1–2 leaf stages) to control AVEFAs causes higher wheat yields even though herbicides are more effective in the tillering stage [ 36 ]. Early applications of herbicides to control Avena species were also recommended [ 37 ]. On the other hand, AVELU emergence flush occurs between November and February under Mediterranean conditions (28). The ability to catch later flush herbicide applications can be delayed until early spring. Even if a field has mixed Avena populations, herbicides can be applied at the same time because the early development stages are very similar among the three species. In addition, there was a difference in phenology between the southern and northern sides of Spain for AVELU, where tillering occurred earlier in southern sites, between November 25 and the end of December, but it occurred at the end of January. In both these regions, both temperature and rainfall pattern are different so there is a change in biology of these species in order to adapt to these conditions [ 12 ]. The experimental site in Bornova was located between Spain and parallels, where tillering started in late December and early January, which is in agreement with the findings of the Spain study. Tillering started at 48 days after sowing in Spain and Bornova, but it lasted longer in Bornova, up to 104-110 th day for AVELU, 106-123 rd day for AVEFA, and 117-125 th day for AVEST after sowing, which was the 79 th day in Spain.

The SPAD values were not significantly differed among species, but the AVELU generally had lower SPAD values. The reason for the decline in SPAD values was the difference between study sites and species type. Previously it has been observed that the SPAD values were strongly affected by the location of the study site [ 38 ]. This difference significantly influences competitive interactions with crop plants and overall weed management strategies.

There were differences between two populations of the same Avena species in many features in the present study. According to reference [ 24 ], among 302 AVEFA accessions, tillers per plant ranged from 10 to 42, with an average of 21 to 31 tillers per plant. There were five leaves on the main stem (except for diy03 of AVEFA, which had 6 leaves), and the number of tillers was 15 for AVEFA, 10 for AVEST, and 6 for AVELU; these numbers are greater than those in an earlier study in which AVEFA had 3–4 leaves and 10–15 tillers [ 23 ]. In a field study in which 25 individuals were selected each year, the average number of AVEFA tillers was 14 and 24 per plant in the first and second years of the experiment, respectively [ 26 ]. In addition, the tiller number per plant varied between 11 and 39, which was 8–25 for AVEFA in the present study.

The average plant height was approximately 2 m, which is roughly double the values reported in earlier studies in which the maximum height of the AVEFA was 79 cm [ 26 ] and 65 to 95 cm [ 25 ]. The duration of flag leaf emergence exceeded 130 days for all populations, and heading occurred 3–8 days after booting, whereas the duration of booting started was 42–51 DAE, and heading started one week later in an earlier study [ 23 ]. The difference between [ 25 ] and the current study is the time of the experiments, which were spring and winter growth experiments. In addition, the flora of Turkey was 30–130 (-150) cm for AVEST and AVELU and 45–80 (-150) cm for AVEFA [ 6 ]. In an experiment with AVEFA accessions, height ranged from 61 to 148 cm, but the average height was 90 to 118 cm [ 24 ].

The length of the spikelets with awn or without awn for AVEST was significantly different from that for AVEFA and AVELU. The Flora of Turkey has spikelet lengths of 22–27 mm for AVEFA, 20–30 mm for AVELU and 30–45 mm for AVEST, which supports the results of current study. Ligule length for AVEFA was 5.36 mm (2.73–7.73 mm), that for AVELU was 4.56 mm (1.97–9.58 mm), and that for AVEST was 9.15 mm (4.08–15.05 mm) in the experiment, but the flora of Turkey had 4–6 mm for AVEFA and 3–8 mm for AVEST and AVELU without discriminating subspecies [ 6 ]. Although there was no clear cutoff point for identifying species in our study, any individual with more than 10 mm of ligule was identified as having AVEST (i.e., A . sterilis subsp sterilis ). This partly helps differentiate species in earlier stages. The flagellum of Turkey can be lengthened with articulation to allow more precise differentiation of these three species.

Apart from phenology, some ecological features are also helpful in determining the specific Avena species. For example, germination of A . ludoviciana is grown in temperate zone and favored more by low temperature as compared with the A . fatua [ 12 ]. Moreover, A . ludoviciana has a wider ability to adapt drought conditions than A . fatua [ 29 ]. These variations in species could be helpful in determining the specific species and adopting a methodological approach to control these species. Moreover, studying these factors could be helpful in determining the dynamics of these species in different cropping systems.

In the current study, growth cycles of three Avena species have been studied. There were three significant differences among the studied species. The spikelet length, which is currently used to distinguish AVEST from AVELU; the ligule length, which is suggested for partial differentiation; and the length of the growing cycle, which is considered to be 196 days for AVEST, 201 days after emergence for AVEFA, and 209 days for AVELU.

Supporting information

S1 raw data..

https://doi.org/10.1371/journal.pone.0307875.s001

Acknowledgments

I would like to thank Ahmet ULUDAĞ for his support in the statistical analysis.

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• Published: 11 September 2024

Physiological Basis of Plant Growth Promotion in Rice by Rhizosphere and Endosphere Associated Streptomyces Isolates from India

• Dhivya P. Thenappan   ORCID: orcid.org/0009-0007-0450-8257 1 , 2 ,
• Rakesh Pandey 3 ,
• Alkesh Hada 4 ,
• Dinesh Kumar Jaiswal 5 ,
• Viswanathan Chinnusamy 3 ,
• Ramcharan Bhattacharya 6 &
• Kannepalli Annapurna 2  

Rice volume  17 , Article number:  60 ( 2024 ) Cite this article

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This study demonstrated the plant growth-promoting capabilities of native actinobacterial strains obtained from different regions of the rice plant, including the rhizosphere (FT1, FTSA2, FB2, and FH7) and endosphere (EB6). We delved into the molecular mechanisms underlying the beneficial effects of these plant-microbe interactions by conducting a transcriptional analysis of a select group of key genes involved in phytohormone pathways. Through in vitro screening for various plant growth-promoting (PGP) traits, all tested isolates exhibited positive traits for indole-3-acetic acid synthesis and siderophore production, with FT1 being the sole producer of hydrogen cyanide (HCN). All isolates were identified as members of the Streptomyces genus through 16S rRNA amplification. In pot culture experiments, rice seeds inoculated with strains FB2 and FTSA2 exhibited significant increases in shoot dry mass by 7% and 34%, respectively, and total biomass by 8% and 30%, respectively. All strains led to increased leaf nitrogen levels, with FTSA2 demonstrating the highest increase (4.3%). On the contrary, strains FB2 and FT1 increased root length, root weight ratio, root volume, and root surface area, leading to higher root nitrogen content. All isolates, except for FB2, enhanced total chlorophyll and carotenoid levels. Additionally, qRT-PCR analysis supported these findings, revealing differential gene expression in auxin ( OsAUX1 , OsIAA1 , OsYUCCA1 , OsYUCCA3 ), gibberellin ( OsGID1 , OsGA20ox-1 ), and cytokinin ( OsIPT3 , OsIPT5 ) pathways in response to specific actinobacterial treatments. These actinobacterial strains, which enhance both aboveground and belowground crop characteristics, warrant further evaluation in field trials, either as individual strains or in consortia. This could lead to the development of commercial bioinoculants for use in integrated nutrient management practices.

Introduction

According to the United Nations, the global population will grow from 7.5 billion to 9.7 billion by 2050, requiring modern agriculture to produce more eco-friendly and sustainable food (Rouphael and Colla 2018 ). Nearly half the world’s population consumes rice ( Oryza sativa L.), a primary staple food. India is the world’s second-largest rice producer in terms of area and quantity (Hada et al. 2020a ; Chatterjee et al. 2021 ). The extensive use of chemical fertilizers, which may negatively impact human health and the environment, is a significant issue related to rice production (Zafar et al. 2012 ). The demand for agrochemical alternatives has increased interest in using microorganisms for environmentally sustainable agricultural management. Microbial inoculants that act as biofertilizers are biostimulants that promote plant growth by increasing nutrient supply, root biomass or root area, and nutrient uptake capacity (Vessey 2003 ). Many of the plant growth-promoting (rhizo) bacteria (PGPB or PGPR) that are isolated from plants and crops around the world are used as agricultural inoculants (biofertilizers) (Kloepper et al. 1989 ; Bhardwaj et al. 2014 ). Effective bacterial biofertilizers stimulate plant growth and nutrient uptake by fixing atmospheric nitrogen, solubilizing nutrients, sequestering iron through siderophores, and producing volatile organic compounds and phytohormones (Ryu et al. 2003 ; Beneduzi et al. 2012 ; Backer et al. 2018 ).

Actinobacteria of the genus Streptomyces are effective rhizosphere and rhizoplane colonizers. They can also colonize the inner tissues of the host plant as endophytes (Sousa and Olivares 2016 ). Several plant growth-promoting streptomycetes (PGPS) inoculants have been shown to increase biomass in crops such as rice, wheat, sorghum ( Sorghum bicolor ), and tomato ( Solanum lycopersicum ) (Gopalakrishnan et al. 2013 ; Hu et al. 2020 ; Zhu et al. 2023 ). Actinobacteria are thus frequently utilized as bioinoculants (Boukhatem et al. 2022 ).

One of the suggested mechanisms to explain growth promotion induced by PGPR is phytostimulation, either through the microbial production of phytohormones like auxins, cytokinins (CKs), gibberellins (GAs), and ethylene (ET) or by modulating their homeostasis in plants (Lugtenberg and Kamilova 2009 ). Most studies on IAA-producing microorganisms have revealed a link between root development, morphology, and IAA production, the major auxin present in plants. Several Streptomyces species, including S. olivaceoviridis and S. viridis , can produce IAA and enhance plant growth by improving seed germination, root elongation, and root dry weight (Khamna et al. 2010 ). Numerous investigations have documented the production of gibberellins by actinobacterial species, including those producers of gibberellin-like compounds by Streptomyces olivaceoviridis , S. rochei , and S. rimosus cultures, which promoted plant development in wheat and eggplant by influencing the growth parameters of the plant, such as root length and fresh or dry root weight (Rashad et al. 2015 ). Inoculating plants with cytokinin-producing bacteria boosted shoot growth and reduced the root-to-shoot ratio (Arkhipova et al. 2007 ). Nevertheless, these hormones have been reported less frequently in strains of actinobacteria.

While the plant growth-promoting effects of Streptomyces have been well-established in various greenhouse studies, further research is necessary to comprehend how rice responds to PGPS bacteria at both physiological and molecular levels. Consequently, this study sought to assess the physiological foundation underlying the plant growth-promoting potential of native rhizospheric and endospheric actinobacterial strains of rice. Additionally, the effect of these isolates on the expression of a comprehensive set of marker genes associated with phytohormone pathways involved in rice architecture modifications was examined.

Materials and Methods

Bacterial strains and their molecular characteristics.

The actinobacterial isolates used in the present study were selected from our laboratory’s bacterial collection. Isolates FT1, FB2, FTSA2, and FH7 were originally obtained from the rhizosphere, and strain EB6 was derived from the endosphere of paddy fields cultivated with the Indian rice genotype Vardhan. The glycerol stocks of the cultures were revived and purified through successive subculturing on the International Streptomyces Project-2 (ISP2) medium, incubated at 30 °C for 5 days (Ali 2022 ). The pure cultures were maintained in 30% glycerol (v/v) at -20 °C for future use.

The nearly full-length 16S rRNA gene was amplified in the isolates using the universal primers 27 F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 1522 R (5′-AAGGAGGTGATCCAGCCGCA-3′) (Lane 1991 ), following the PCR conditions described by Thenappan et al. ( 2022 ). The nearly complete 16S rRNA gene sequences obtained were analyzed and compared against the EZbiocloud database ( https://www.ezbiocloud.net ) for potential genus identification (Kim et al. 2012 ). The nucleotide sequences were then submitted to NCBI GenBank (GenBank accession: MN955410, MN955412, MZ736625, MZ736626, and MZ736627). Furthermore, the sequences of the isolates and associated type strains were aligned in MEGAXI using MUSCLE (Tamura et al. 2013 ). The phylogenetic tree was built using the maximum likelihood approach (Felsenstein 1981 ) and the Kimura 2-parameter model (K2+I) in MEGA XI (Kimura 1980 ). Bootstrap analysis with 1000 replications was used to analyze the tree topology (Felsenstein 1985 ). Suitable models with the lowest BIC scores (Bayesian Information Criterion) and the highest AICc values (Akaike Information Criterion, corrected) were selected in MEGA XI.

In vitro plant growth promotion assays

The method outlined by Gordon and Weber ( 1951 ) was employed to evaluate IAA production. The actinobacterial isolates were grown in ISP2 broth with 0.2% L-tryptophan and incubated at 30 °C for 6 days with shaking at 150 rpm. After samples were centrifuged at 10,000 rpm for 15 min, 2 ml of Salkowski reagent was added to 1 ml of cell-free supernatant. The development of a pink-red hue indicated the production of IAA. The absorbance of IAA at 530 nm was measured using a spectrophotometer (Labman, India) against a standard curve to determine the quantity of IAA in µg/ml.

The phosphate solubilizing potential of isolates was tested by spot inoculating them on Pikovskaya’s agar medium and incubating them for seven days at 30 °C; colonies with clear zones around were termed phosphate solubilizers (Donate-Correa et al. 2005 ). The isolates were spotted onto a chrome azurol S (CAS) plate and cultured at 30 °C for 5 days for the siderophore production assay (Alexander and Zuberer 1991 ). The appearance of an orange-yellow halo surrounding the colonies showed that siderophores were detected. Qualitative evaluation of hydrogen cyanide (HCN) generation was done using Lorck’s method ( 1948 ). Isolates were inoculated onto ISP2 agar plates with 4.4 g of glycine/L. A Whatman No. 1 filter paper soaked in 0.5% picric acid in a 2% sodium carbonate (w/v) solution was placed under the lids of Petri dishes, covered with parafilm, and incubated at 30 °C for 7–10 days. The change in color of the filter paper from yellow to orange-brown indicated the release of HCN.

Seed Germination Assay

Rice seeds (cv. Pusa Basmati 1509) were surface sterilized for 5 min with 2% sodium hypochlorite (v/v) and rinsed four times with sterile distilled water (SDW). Sterilized seeds were immersed in a culture medium (10 8 cells/ml) of each given actinobacterial isolate (EB6, FT1, FTSA2, FB2, and FH7) under investigation and continuously stirred (150 rpm, 6 h). SDW-soaked seeds were used as a control. Ten seeds were placed on each sterile plate using moist filter paper (Whatman No. 1). Plates were incubated at 30 °C for 3 days. Each treatment was replicated three times. On the second day, the percentage of germination was calculated. On the third day, the plumule and radicle lengths were measured.

The vigor index was calculated using the following formula established by Abdul-Baki and Anderson ( 1973 ):

Vigor index = germination (%) x total seedling length (cm)

Pot Experiment Assay

For the pot experiment, surface-sterilized rice seeds were soaked for 6 h in actinobacterial cultures (1 × 10 8 CFU ml − 1 ) prepared in 1% carboxymethyl cellulose (CMC). Ten seeds were planted at a depth of 5 cm in each pot (20 cm x 15 cm x 10 cm) filled with 5 kg non-sterile sandy loam soil (ICAR-Indian Agricultural Research Institute, New Delhi, India) with the following characteristics: pH, 8.21; EC (ds/m), 0.35; organic C, 0.41%; total nitrogen, 250.5 kg ha − 1 ; total phosphorus, 93.15 kg ha − 1 ; total potassium, 436 kg ha − 1 . The treatments included T1: Uninoculated Control, T2: Streptomyces sp. EB6, T3: Streptomyces sp. FT1, T4: Streptomyces sp. FTSA2, T5: Streptomyces sp. FB2, and T6: Streptomyces sp. FH7.

Each treatment consisted of five pots set in a completely randomized design (CRD) and watered regularly with tap (non-sterile) water. The pots were kept at 30 °C ± 2 °C and 90% relative humidity in a greenhouse bench (National Phytotron Facility, ICAR-Indian Agricultural Research Institute, New Delhi, India). Plant samples were taken twice, 30 and 45 days after sowing (DAS). At each harvest, observations were taken on dry matter production (shoot, root, and leaf) and leaf area. The mean of these observations (at 30 and 45 DAS) was used to assess the plant’s performance in terms of dry matter production.

The automated root image analysis program WinRhizo (Regent Instruments Inc., Quebec City, Canada) was used to estimate root morphological parameters such as total root length (cm), root surface area (cm 2 ), average root diameter (mm), number of root points, forks, and total root volume (cm 3 ), volume and surface area of roots with diameters between 0 and 1 mm (superfine roots), 1–2 mm (fine roots), and larger than 2 mm (thick roots) (de Sousa et al. 2021 ). Subsequently, shoot height (cm), leaf area, and root and shoot dry weights were oven-dried at 65˚C until a constant weight (g) was measured to calculate growth indices (Table S3 ).

To assess N content in the CHNS/O analyzer, oven-dried plant tissue samples were crushed to pass through a 0.2-mm sieve (CHN-O-RAPID; EuroEA element analyzer, Germany). Photosynthetic pigments such as chlorophyll a and chlorophyll b were quantified using Hiscox and Israelstam’s techniques ( 1979 ). Furthermore, the carotenoid content was determined using the method of Kirk and Allen ( 1965 ).

qRT-PCR Analysis of Phytohormone-Responsive Genes

Total RNA was extracted from 0.1 g of crushed rice leaves (30 days old) using the SV Total RNA isolation system kit following the manufacturer’s protocol (Promega, WI, USA). RNA quantity and purity were determined using a NanoDrop-1000 spectrophotometer (Thermo Scientific, MA, USA). Approximately 1 µg of total RNA was reverse transcribed to cDNA using the Superscript VILO kit (Invitrogen, USA) following the manufacturer’s instructions. qRT-PCR was performed in a Realplex2 thermal cycler (Eppendorf, Germany) using the SYBR Green PCR master mix kit (Eurogentec, Liege, Belgium), with OsActin1 as the reference gene. To assess the specificity of amplification, a melt curve analysis was conducted with a program consisting of 95 °C for 15 s, 60 °C for 15 s, and a slow ramp from 60 to 95 °C. Cycle threshold (Ct) values were obtained using Realplex software (Eppendorf, Germany), and relative expression levels were calculated using the 2 –ΔΔCt method (Livak and Schmittgen 2001 ), normalized with the OsActin1 gene. Data were generated from three independent biological replicates and three technical replicates, and the analysis was conducted according to the methodology described by Hada et al. ( 2020b , 2021 ). The qRT-PCR primers for the genes of the auxin, gibberellic acid, and cytokinin biosynthesis pathways in rice are illustrated in Table S1 .

Statistical Analyses

The packages ‘ggplot2’ and ‘factoextra’ of R software version 4.0.2 (R Core Team 2019) were used to create bar graphs and PCA biplots. The data was tested for normality using the Shapiro-Wilk test. In the case of a normal distribution, the one-way ANOVA test and post hoc Tukey test were used; in all other cases, the non-parametric Kruskal-Wallis and post hoc Dunn’s tests were used. The ‘corrplot’ package (Wei and Simko 2017 ) generated correlation matrix plots. All analyses were conducted with p  ≤ 0.05 as the significance level. Further, for statistical validation of qRT-PCR data, one-tailed t-tests and Tukey’s HSD tests were conducted on biological replicates ( n = 3) for each treatment.

Molecular Characterization

The analysis of the nearly full-length 16S rRNA gene sequences (Table  1 ) revealed that all isolates were members of the genus Streptomyces of the family Streptomycetaceae. Strains EB6 and FT1 shared 98.9% sequence similarity (16 nt difference in 1415 and 1414 sites, respectively) with strain Streptomyces violascens ISP5183 T , while strains FTSA2 and FH7 shared 98.7% (19 nt difference in 1419 sites) and 98.9% similarity (15 nt difference in 1418 sites) with strain Streptomyces araujoniae ASBV1 T . Our study revealed that strain FB2 exhibited a similarity of 98.7% to Streptomyces longispororuber NBRC 13488 T (19 nt difference at 1416 sites). A comparison of 16S rRNA identity between the two isolates was also performed using a pairwise BLAST analysis, which indicated that EB6 and FT1 (96.7%), followed by FTSA2 and FH7 (97.2%), did not have nearly identical gene sequences.

Phylogenetic Analysis

By the maximum likelihood method of phylogenetic analysis of 16S rRNA gene sequences, strains FT1 and EB6 formed separate clusters, with the latter forming a monophyletic clade with S. violascens ISP 5183 T , S. daghestanicus NRRL B-5418 T , and S. albidoflavus DSM 40455 T (Fig.  1 ). Similarly, strains FTSA2 and FH7 formed an independent clade with the closest relatives, such as S. rhizosphaericola 1AS2c T and S. araujoniae ASBV-1 T ; strain FB2, on the other hand, formed a cluster with S. nigra 452 T with high bootstrap values in all three phylogenetic studies (Fig.  1 ).

Maximum likelihood tree based on the 16S rRNA showing the phylogenetic position of isolates within the genus Streptomyces . The type strain Micronomospora viridifaciens DSM43909 T was used as the outgroup. Numbers at branching points are percentage bootstrap values based on 1,000 replications. The scale bar shows 0.04 nucleotide changes per site

In vitro Plant Growth Promotion Assay

The PGP traits of the actinobacteria are summarized in Table  1 . All the isolates showed diverse abilities to synthesize IAA, with strains FH7 (27.10 ± 0.72 µg ml − 1 ) and FTSA2 (19.05 ± 1.11 µg ml − 1 ) confirming the highest and lowest IAA production in medium supplemented with 0.2% tryptophan, respectively. In the absence of tryptophan, FH7 and FT1 strains produced more IAA (13.09 ± 2.01 and 13.96 ± 1.51 µg ml − 1 , respectively), and FB2 strains produced the least (5.28 ± 2.21 µg ml − 1 ). In the CAS medium, all identified Streptomyces isolates produced siderophores, with FB2 recording the maximum orange halo (17 ± 1.5 mm) and FT1 producing the least (7.25 ± 0.25 mm). Tri-calcium phosphate solubilization was found to be absent in all isolates. Only the FT1 isolate tested slightly positive for HCN production (Fig. S1 ).

Seed Germination Bioassay

Seed bacterization treatment with the specified isolates for three days resulted in significant changes ( p  < 0.05) in seedling length and seed vigor index. However, no significant changes in germination percentage were observed. Isolate FT1 exhibited 100% germination, but isolate FTSA2 marginally decreased seed germination (95.6%). In terms of seed vigor index, isolates FTI and FB2 exhibited about the same effect as the control, whereas isolates EB6, FTSA2, and FH7 exhibited significant adverse effects by -6.4, -27.9%, and 46.8%, respectively (Table  1 ; Table S2 ).

Effect of Actinobacterial Isolates on rice above- and below-ground Growth

Shoot dry matter and leaf morphological traits.

Treatment with strains FTSA2, FB2, and EB6 substantially enhanced leaf dry weight (LDW), shoot dry weight (SDW), and total dry weight (TDW), whereas strain FH7 showed a reduction in these parameters. Strain FTSA2 increased leaf dry weight and shoot dry weight, resulting in a 30.7% rise in total dry weight ( p  < 0.05). Strain FB2 followed with an 8.3% increase, while strain FT1 increased total biomass by 1.5% (Table S3 ; Fig. S1 ; Fig.  2 A, B).

Plants inoculated with strain FTSA2 exhibited the highest shoot C and N content, with leaf N content increasing by 46.7% (Table S6 ; Fig.  2 H). Although not statistically significant, FTSA2 showed an increase in leaf carbon content, consistent with their overall substantial improvement in leaf dry weight (LDW). Concerning photosynthetic pigments, the maximum total chlorophyll content observed in plants treated with the strain FTSA2 was 33.1% higher, whereas FB2-treated plants showed lower total pigments than the untreated control (Table S4 ; Fig.  2 G).

No statistically significant differences ( p  > 0.05) were observed between the treatments and the control for leaf area (LA; Fig.  2 D), plant height, specific leaf area (SLA; Fig.  2 E), total plant carbon per unit leaf area (Fig.  2 F), leaf area ratio (LAR; Fig.  2 I), and root:shoot (R:S) ratio (Table S7 ). Nonetheless, leaf area was observed to increase in all strains compared to the control, excluding FH7. The leaf weight ratio increased only in strains EB6 and FH7. The specific leaf area increased in all strains; total plant carbon per unit leaf area increased in FB2, FT1, and EB6-treated plants; and the leaf area ratio increased in all strains except FB2 (Table S5 ).

Root dry Matter and Morphological Traits

Strain FTSA2 considerably lowered root morphological features such as root weight ratio (RWR; –16.3%), total root length (TRL; –36.2%), root length ratio (RLR; –51.0%), and specific root length (SRL; –37.1%). Furthermore, the number of tips (NTips), root surface area, root:shoot ratio, and the number of forks were also decreased. However, it significantly improved leaf area to root length ratio (LA/RL; +106.5%), plant nitrogen content per unit root dry weight (N content/RDW; +33.9%), and root diameter (RD; +23.3%).

In contrast, FB2 and FT1 increased root dry weight (+17.7% and +16.3%, respectively). Notably, FB2 positively influenced most root morphological features, including total root length (+18.5%), root surface area (+40%), root diameter (+12.4%), root volume (+53.2%), and number of forks (+16.0%), in comparison to control plants. Strain FT1 showed a similar trend for the root mentioned above features, but to a lesser amount than FB2, except for RLR and Ntips, which increased by 13.4% and 62%, respectively. Strain FT1 demonstrated a non-significant rise ( p  > 0.05) in root weight and root:shoot ratios. Although all treatments increased the length, surface area, and volume of thick roots, rice plants inoculated with FT1 and FB2 produced more fine and superfine roots than control plants. While EB6 and FH7 drastically decreased the majority of belowground root properties, they increased leaf area/root length by 36.4% and 54.6%, respectively (Tables S7 – S10 ; Fig.  2 J–P; Fig. S2 ).

Because strains FT1 and FB2 produced a superior total root system, FB2 substantially increased C (+123%) and N (+28.5%) content in roots (Table S6 ). Except for FH7, which displayed a decrease in C and N content in both the root and shoot, the remaining strains increased ( p  < 0.05) total nitrogen uptake and total carbon assimilated in comparison to the control. In terms of total carbon partitioning, FB2 excelled in the root zone (24.9%), while FTSA2 outperformed in the shoot zone (91.3%) (Table S11 ; Fig.  2 C, L).

The maximum total chlorophyll and carotenoid content for photosynthetic pigments were reported with the strain FTSA2 at 33.1% and 41.4%, respectively. In contrast, strain FB2 dramatically decreased the total pigments in treated plants compared to the control. Overall, strain FB2 exhibited a moderate to pronounced response to both above- and below-ground characteristics, whereas strain FH7 dramatically reduced both above- and below-ground characteristics and displayed the lowest total biomass. Strain FTSA2, which showed the highest proportion of dry matter in the shoots (89.1%), outperformed other treatments in total dry biomass (Table S11 ).

Effect of Streptomyces strains on above-ground ( A – I ) and below-ground ( J – P ) plant growth parameters in rice. Values were expressed as mean ± standard error. Different letters indicate significant differences at p  < 0.05 by Tukey’s HSD test. A : total dry weight; B : shoot dry weight; C : total C content; D : leaf area; E : Specific leaf area; F : total plant C/LA; G : total chlorophyll; H : total leaf N; I : leaf area ratio (LAR); J : root dry weight; K : total root length (TRL); L : specific root length (SRL); M : number of root tips; N : total N content; O : total plant N/Root DW; P : leaf area/root length (LA/RL)

Principal Component Analysis (PCA)

Principal component analysis (PCA) examined the relationship between the measured parameters and treatments. Based on this multivariate analysis, the first two principal components, PC1 and PC2, explained 91.3% of the total variation and were utilized to construct the PCA biplot for the aboveground growth metrics. PC1 accounted for 59.1% of the phenotypic variation and was substantially linked with shoot and total dry weight, leaf area, and shoot nutritional characteristics. The second principal component (PC2) explained 32.2% of the phenotypic variability, with leaf characteristics (leaf area ratio, specific leaf area, leaf weight ratio, specific leaf weight, and pigments) and plant height accounting for the majority. Along the PC1 and PC2 axes, the bacterial treatments were clearly distinguished from the control in the biplot. While the non-inoculated control and FH7 treatments in the lower and upper left quadrants exhibited lower shoot dry weight and nutrient content, the treatments in the right quadrants displayed greater shoot, total dry weight, and nutrient content, highlighting the performance of FTSA2, FB2, and EB6. The FT1 strain, located close to the center of the quadrant, produced intermediate outcomes but a better shoot system than the uninoculated control (Fig.  3 A).

For belowground growth characteristics, the first two dimensions of PCA explained 91.6% of the total variation, with principal component 1 (PC1) explaining 66.9% of the variation and principal component 2 (PC2) accounting for 24.7% of the variation. While PC2 is linked to root dry weight, leaf area/root length, root:shoot ratio, and root morphology traits (root length, specific root length, root diameter, thick roots), PC1 was primarily represented by the other root morphology characteristics examined in this study. Both dimensions led to the distribution of inoculation treatments over the quadrants, with strains FB2 and FT1 in the upper left quadrant exhibiting greater root system development than the control (Fig.  3 B).

All results obtained for the tested actinobacterial strains were included in the correlation analysis, confirming the PCA-generated data. The findings of the correlation analysis between the above- and below-ground growth metrics of the treated plants are depicted in Fig.  4 A and B. Significant positive relationships ( p  < 0.05, shown in blue) were discovered between plant biomass (leaf, shoot, and total dry weight), leaf area, leaf nutrients, and photosynthetic pigments for aboveground characteristics. In addition, a negative correlation ( p  < 0.05, shown in red) was seen between plant height and photosynthetic pigments, leaf weight, and leaf area ratios. For belowground characteristics, root morphology demonstrated strong positive relationships ( p  < 0.05, shown in blue) between root morphological variables, including total root length, root surface area, root volume, number of tips and forks, and several root classes of length, surface area, and volume. Moreover, root morphological features demonstrated a positive association with root dry weight. However, it showed a negative correlation ( p  < 0.05, shown in red) with the leaf area/root length ratio. In addition, root weight ratio, root length ratio, and specific root length were negatively correlated with the leaf area/root length ratio but positively correlated with the root:shoot ratio. There was a positive and significant correlation between root dry weight and root macronutrients, root carbon, and root nitrogen ( p  < 0.05).

A biplot display of principal component analysis (PCA) of the above ( A ) and below ( B ) ground parameters analyzed in Streptomyces-treated rice plants. A : LWR: leaf weight ratio; LAR: leaf area ratio; SLA: specific leaf area; ChlTot: total chlorophyll; LA: leaf area; Shoot N: shoot nitrogen; TDW: total dry weight; SLW: specific leaf weight; PH: plant height. B : RD: root diameter; L.TR: length of thick roots; SA.TR: surface area of thick roots; V.TR: volume of thick roots; V.FR: volume of fine roots; L.FR: length of fine roots; SA.FR: surface area of fine roots; SA.SFR: surface area of superfine roots; RSA: root surface area; RWR: root weight ratio; RL: total root length; R:S: root-to-shoot ratio; RV: root volume; RW: root weight; LA.RL: leaf area to root length

Corr-plot representing Pearson’s correlation analysis between the variables in ( A ) above and below ( B ) ground characteristics. Here, the size of the square is proportional to the absolute value of correlation coefficients, whereas their color represents the value in positive or negative. A box with a cross indicates non-significant correlations ( p  < 0.05). A) LWR: leaf weight ratio; LAR: leaf area ratio; SLA: specific leaf area; ChlTot: total chlorophyll; Carot: carotenoids; SLW: specific leaf weight; PH: plant height; LA: leaf area; LW: leaf dry weight; TDW: total dry weight; Shoot N: shoot nitrogen; Shoot C: shoot carbon; Leaf N: leaf nitrogen; Sh.DW: shoot dry weight. B) R:S: root-to-shoot ratio; RLR: root length ratio; SRL: Specific root length; Root C: root carbon; Root N: root nitrogen; RSA: root surface area; RWR: root weight ratio; RL: total root length; RV: root volume; RW: root dry weight; RD: root diameter; LA.RL: leaf area to root length; L.TR: length of thick roots; SA.TR: surface area of thick roots; V.TR: volume of thick roots; L.FR: length of fine roots; SA.FR: surface area of fine roots; V.FR: volume of fine roots; L.SFR: length of superfine roots; SA.SFR: surface area of superfine roots; V.SFR: volume of superfine roots

Gene Expression Studies

The relative expression (log 2 fold change) of the identified genes in Pusa Basmati 1509 inoculated with the selected strains was evaluated by qRT-PCR. First, the expression patterns of genes involved in auxin biosynthesis were investigated. Except for FT1, the expression level of OsYUCCA1 , a member of the YUCCA family of genes involved in IAA biosynthesis, was significantly elevated in all the isolates, with FH7 exhibiting the most significant upregulation (4.31-fold increase) (Fig.  5 A). A mixed pattern of expression was observed for OsYUCCA3 , another member of the YUCCA family in rice (Fig.  5 B). No significant change in OsYUCCA3 gene expression was observed in EB6-, FTSA2-, and FT1-treated samples. In contrast, it was significantly up- and down-regulated by FB2 and FH7, respectively. The genes involved in auxin influx carrier and auxin signaling, OsAUX1 and OsIAA1 , were considerably upregulated with inoculation of all studied strains, except FT1, which down-regulated OsIAA1 with no significant difference compared to the control (Fig.  5 C, D). In the GA biosynthetic pathway, OsGA20ox-1 , the gene encoding the gibberellin 20-oxidase enzyme in GA biosynthesis, was significantly induced in response to FH7 (1.28-fold increase), whereas the remaining strains non-significantly suppressed the gene expression. While OsGID1 , the gene encoding the gibberellic acid receptor in rice, was stimulated by most strains, highest in FB2 (3.43-fold increase), its expression was shown to be down-regulated by FT1 (fold decrease of –0.68) (Fig.  5 E, F). We also analyzed the transcriptional responses of two genes involved in cytokinin production, OsIPT3 and OsIPT5 . While OsIPT3 was found to be elevated by more than one-fold increase following inoculation with FH7, EB6, and FT1, OsIPT5 was found to be upregulated by all the strains except FTSA2 (–1.41-fold decrease) (Fig.  5 G, H).

Effect of actinobacterial inoculation on the expression levels of genes involved in phytohormone metabolism, transport, and signaling in rice ( A – D ): auxin; ( E , F ): GA; ( G , H ): CK. The actin ( Os03g0718100 ) gene was used as the reference gene. Bars represent the mean ± SE of n  = 3. Significant differences between control and treated samples are indicated in an asterisk (*). The sign * represents p  ≤ 0.05, and ** represents p  ≤ 0.01

In recent years, the use of actinobacteria in agriculture has expanded due to their potential action as PGPR and their widespread distribution in plants. Rhizospheric and endophytic actinobacteria from rice have been employed in studies to combat crop diseases and enhance rice growth (Naik et al. 2009 ; Gao et al. 2021 ; Saikia and Bora 2021 ). In the present study, the plant growth regulation effect of five native actinobacterial isolates from rice rhizosphere and endosphere niches was studied.

The in vitro assessment of plant growth-promoting characteristics, such as IAA production, showed that adding tryptophan to the bacterial broth significantly increased IAA production, similar to the findings of Spaepen et al. (2011). Thus, all the tested isolates preferred a tryptophan-dependent IAA production pathway. The production of IAA varies among species and strains (19–27 µg/ml) and is affected by culture conditions, growth stage, and substrate availability. This result is consistent with those of Khamna et al. ( 2010 ) and Djebaili et al. ( 2020 ), who showed that the IAA production levels of rhizospheric actinomycetes varied from 11.03 to 144 µg/ml and 7.44 to 21.4 µg/ml, respectively. Also, all the isolates produced siderophores, as indicated by the formation of an orange halo zone on the CAS agar medium. It has been observed that Streptomyces sp. from rhizosphere soil produces siderophores that boost plant growth by building a complex with iron (Fe 3+ ) in the rhizosphere, rendering iron inaccessible to phytopathogens and inhibiting their growth. On the other hand, none of the isolates could solubilize phosphate in Pikovskaya’s agar medium. A siderophore-producing endophytic Streptomycete isolated from a Thai jasmine rice plant ( Oryza sativa L. cv. KDML105) yielded comparable results (Rungin et al. 2012 ). Previous studies demonstrated that the phosphate solubilizing bacteria chelate iron from Fe-P complexes in the soil by converting insoluble inorganic forms of phosphate into soluble forms via the secretion of organic acids or siderophore-like compounds (Hamdali et al. 2008 ; Ben Farhat et al. 2009 ; Rungin et al. 2012 ). This suggests that the selected isolates in this study may use the siderophore-mediated phosphate solubilization mechanism to promote plant development. However, this must be validated through in vitro experiments. Similarly, the isolates did not exhibit HCN synthesis, which has been associated with the biocontrol mechanism (Keel 1997 ), except for FT1, which produced negligible amounts of HCN.

Effect of Streptomyces Strains on rice seed Germination

Coating of seeds with plant beneficial microbes (PBM) provides precise application of inoculum at the seed-soil interface (Scott 1989 ), ensuring that the PBMs are readily accessible at germination and early plant developmental phases, boosting healthy and speedy establishment and increasing crop yield (Colla et al. 2015 ). In this seed germination bioassay, all treatments resulted in a germination percentage greater than 95% compared to the control, and none of the Streptomyces strains had any phytotoxic effect on rice seeds (cv. Pusa Basmati 1509). However, the decline in seed vigor index in the treatments with FTSA2 and FH7 was significant. This is explained by the fact that plant roots either operate as filters of rhizosphere bacteria adhering to root surfaces (Reinhold-Hurek et al. 2015 ) or by the overproduction of IAA by the plant growth-promoting bacteria-induced stress hormone ethylene in plants, resulting in lower colonization, as reported in rice treated with endophytic Klebsiella pneumoniae S2 (Shabanamol et al. 2018 ).

Streptomyces Strains Display Different Effects on Plant Growth Physiology

The study explores the physiological basis of plant growth promotion in Streptomyces strains based on their (i) enhancement in total dry matter production, (ii) root proliferation and N uptake, (iii) carbon partitioning, and (iv) changes in the expression of phytohormone-related pathways augmenting root-shoot growth. It provides a framework for understanding the effects of these strains on plant growth (Fig.  6 ). These strains, isolated from the rice rhizosphere and endosphere, have been acclimated to the native ecology of the host plant, which may make their effects more consistent (Meldau et al. 2012 ).

The conceptual framework underlying plant growth promotion by Streptomyces strains

Dry Matter Production and Partitioning

An increase in total dry matter production is the most significant factor influencing plant growth performance (McDonald et al. 1996 ). The distribution of dry matter between the root and the shoot, however, might differ (Marcelis 1996 ; Hunt and Lloyd 2008 ). The study evaluated the performance of different treatments in terms of dry matter production and dry matter partitioning to roots and shoots. Results showed three categories of responses in treated plants: increased biomass production (FTSA2, EB6, FB2, and FT1), increased partitioning to roots (FB2 and FT1), and no growth promotion (FH7).

Inoculation with strains FTSA2 and EB6 may have higher total biomass and lesser partitioning to roots

In the FTSA2-inoculated roots, higher root thickness resulted in a slightly higher allocation of biomass to roots (9.1%). Plant N uptake is a product of total root production (in terms of root dry matter and length) and the assimilatory capacity of roots (in terms of total plant N per unit root dry matter) (Lawlor 2002 ). In FTSA2-treated plants, higher total plant N uptake in roots was a result of increased total plant N uptake per unit root dry weight and improved root dry biomass functioning together, corroborating with other findings by Shaharoona et al. ( 2008 ) and Nguyen et al. ( 2019 ). This assimilated N led to a rise in leaf N that can lead to increased photosynthesis (Evans et al. 1983 ; Osaki et al. 1995 ). Plant carbon assimilation is a function of the photosynthetic capacity of the leaf and total leaf area production (Watson 1952 ). More capacity for carbon fixation in FTSA2-treated plants can also be associated with their higher leaf area (LA), the increase in specific leaf area (SLA), leaf area ratio (LAR), and higher leaf area to root length (LA/RL), which also suggested a higher proportion of carbon was partitioned to leaves. The C assimilatory capacity of the leaf is also related to higher chlorophyll content and leaf N content, which may be associated with higher Rubisco content (Parry et al. 2013 ). This was supported by our findings, which showed that the shoot and root received 91.3% and 8.7% increases in carbon allocation, respectively, compared to the control. So, among other treatments, the FTSA2 strain enhanced shoot carbon, increasing shoot and total dry matter.

The strain EB6 also showed higher biomass production than the control. However, it varied from FTSA2-treated plants in that it exhibits a little increase in plant height and a lesser decline in carbon partitioning in roots. Overall, we observed that root endophyte inoculation shifted plant resource allocation patterns without impacting the accumulation of total plant biomass (Henning et al. 2016 ). Thus, treatment with FTSA2 and EB6 strains improved the total biomass through root traits associated with carbon assimilation and partitioning to shoot, although partitioning to roots (RWR) decreased in both.

Furthermore, gene expression studies revealed that plants treated with EB6 and FTSA2 strains may have produced fewer auxin molecules due to auxin homeostasis. This resulted in an increase in the expression of genes for the influx carrier ( OsAUX1 ) and IAA biosynthesis ( OsYUCCA1 ), but the levels were insufficient to counteract the action of the repressor ( OsIAA1 ). Studies suggest that nitrate concentrations and auxin homeostasis in plants interact on multiple levels (Guan 2017 ). Thus, we propose that shoot-derived auxin stimulates crown roots and nitrate transporters, leading to increased nitrate uptake and root dry matter (Guo et al. 2005 ; Yamamoto et al. 2007 ; Poupin et al. 2016 ; Hsu et al. 2021 ). In GA signaling, while OsGA20-ox-1 gene transcript abundance was down-regulated in both treatments, GID1 gene transcript abundance was up-regulated in EB6 rice plants. This suggests that exogenous gibberellins produced by bacterial isolates may have upregulated GIDI. Subsequently, the 26S proteasome pathway degrades the GA-GID1-DELLA complex, activating the GA response. While both treatments showed down-regulation of GA biosynthesis genes, OsIPT3 and OsIPT5 were up-regulated in EB6 plants and down-regulated in FTSA2 plants. It is conceivable that KNOTTED-like homoeobox (KNOX) proteins regulate the ratio of CK to GA in rice plants by suppressing GA20ox-1 and activating IPT genes (Jasinski et al. 2005 ). Overexpression of IPT genes in EB6 rice plants might increase shoot nitrogen content. In FTSA2 plants, shoot-derived auxin probably slightly inhibited KNOX function, causing a decrease in the CK:GA ratio. Nevertheless, bacterial effects on the concentration of all phytohormones (IAA, GA, and CK) in plants should be confirmed by direct measurements.

Inoculation with strains FB2 and FT1 may have moderate to higher total biomass and more significant partitioning to roots

The study found that an increase in total dry weight (TDW) was linked to increased root dry weight (RDW) and partitioning to roots (RWR) in strains FB2 and FT1. This is due to improved root architectural and morphological characteristics, such as total root length, branching volume, surface area, and diameter. The increase in root length ratio (RLR) improves nutrient (total N) and water uptake by increasing root surface area (increased soil area explored by finer roots) in conjunction with a moderate to marginal increase in fine and superfine roots (Eissenstat 1992 ). The high metabolic cost for root growth in early plant development may increase shoot growth once the plant is established, compensating for the initial cost of plant/bacteria protocooperation (Lynch and Brown 2012 ; de Sousa et al. 2021 ). The leaf nitrogen concentration increased with total chlorophyll content in FT1-treated leaves (Gholizadeh et al. 2017 ), whereas the results of FB2 treatment are inconclusive due to decreased chlorophyll b . The partitioning of more C to roots in FB2 and FT1 compared to the control resulted in increased root dry matter.

Plants treated with FB2 and FT1 strains showed a high level of IAA production due to the overexpression of both the YUCCA genes in FB2 and moderate upregulation of OsYUCCA3 gene in FT1. This led to an increase in the expression of OsAUX1 in both strains. However, in FT1-treated plants, OsYUCCA3 and OsAUX1 were slightly upregulated, allowing less free auxin to enter cells. Like FTSA2 and EB6, increased shoot-derived auxin concentrations improved root characteristics. Our results were confirmed by the finding that volatile compounds produced by Bacillus amyloliquefaciens SQR9 promoted lateral root formation in Arabidopsis , which involved the auxin signaling system, polar auxin transport, and (YUCs)-mediated auxin synthesis (Li et al. 2021 ). In FB2-treated plants, host-derived GAs or bacterially synthesized GAs led to the degradation of DELLA repressor proteins, resulting in auxin maxima concentration. This stimulated KNOX-mediated downregulation of OsIPT3 and upregulation of OsGA20ox-1 genes, resulting in increased biomass allocation to shoots and a slight increase in total dry weight. Both gibberellin-producing and non-producing PGPR stimulate shoot growth and induce GA biosynthetic gene expression, supporting this notion (Kang et al. 2014 ; Lee et al. 2015 ). OsIPT3 and OsIPT5 , which synthesize CK, are slightly more upregulated than OsGA20-ox1 , which would have reduced shoot elongation but not dry biomass.

Inoculation with Strain FH7 may have a Negative Effect on Total Biomass and Plant Growth

The FH7 strain, despite producing more IAA in vitro, did not increase plant biomass above or below ground, contributing to its poor growth response in treated plants. The regulation of IAA production in PGPR liquid culture is substantially different from that of natural soil due to the presence of environmental factors, soil properties, root exudates, and soil microbial interactions (Spaepen and Vanderleyden 2011 ). In treated plants, the leaf morphology (leaf area ratio, specific leaf area, leaf weight ratio, and total chlorophyll) improved due to increased plant nitrogen per unit root dry matter, resulting in increased carbon allocation to roots. However, this caused a decrease in root dry weight because growth respiration uses some of the carbon (C) partitioned to roots to produce energy to convert into new biomass (Weraduwage et al. 2015 ). As this strain has reduced seed vigor in the germination tests, an additional possibility is the formation of strain-mediated inhibitory secondary metabolites.

FH7-treated plants showed poor root trait performance due to overexpression of the OsIAA1 auxin repressor gene, while increased shoot N and total chlorophyll content were produced by KNOX-mediated upregulation of IPT genes. The apparent discrepancy between IAA biosynthesis gene expression profiles and root traits may indicate that gene expression levels do not necessarily translate to phenotypic traits, which is consistent with Zhang et al. ( 2007 ) reporting lower auxin accumulation in Bacillus subtilis GB03-exposed leaves despite increased ASA1 expression.

The current study aimed to select plant growth-promoting actinobacterial isolates from the rice environment that were both efficient in promoting plant growth and could contribute to enhanced nutrient uptake. Our findings highlight the potential of native Streptomyces strains, particularly FTSA2 and FB2, as effective bioinoculants for sustainable agriculture. These strains exhibited superior plant growth promotion (PGP) potential, with FTSA2 enhancing total dry matter production and FB2 stimulating root development. The observed enhancements in plant growth were linked to improvements in both nitrogen (N) and carbon (C) assimilation capacities, with these strains significantly increasing N assimilation capacity in roots and associated C assimilation in shoots. This was accompanied by notable increases in key root traits such as total root length and surface area, as well as enhanced leaf area per plant and increased partitioning to roots. Furthermore, our study sheds light on the role of plant hormones in mediating these growth-promoting effects. We observed an upregulation of phytohormone biosynthesis-related genes, including OsYUCCA1 , OsYUCCA3 , OsIPT3 , and OsIPT5 , suggesting a potential mechanism by which these strains modulate plant growth and development. These PGP strains, particularly FB2 and FT1, which demonstrated the ability to increase belowground biomass, could be particularly advantageous in soils with a low nitrogen (N) supply. These strains effectively increase the root surface area and volume of soil foraged by the root, resulting in greater nutrient uptake and growth-promoting effects. Thus, strains FTSA2 and FB2 can be promising bioinoculants for enhancing rice growth and nutrient uptake. Further research is necessary to elucidate the underlying mechanisms of plant growth promotion and optimize the application of these strains in the field.

Data Availability

The near full-length 16 S rRNA gene sequences of the actinobacterial isolates reported in this study were submitted to GenBank under the accession numbers MN955410, MN955412, MZ736625, MZ736626, MZ736627.

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Acknowledgements

DPT is grateful to ICAR-IARI for the PhD scholarship. This work was supported in part by funding received from the NAHEP-CAAST project (NAHEP/CAAST/2018-19/07), ICAR-IRA-BNF and Indo-UK IUNFC project of Department of Biotechnology (BT/IN/UK/VNC-41/DLN/2015-16), Government of India, New Delhi. The valuable technical assistance of Loitongbam Ashakiran (ICAR- National Institute for Plant Biotechnology) and Tarun Kumar (Division of Plant Physiology, ICAR- Indian Agricultural Research Institute) is greatly acknowledged.

This work was supported in part by funding received from the NAHEP-CAAST project (NAHEP/CAAST/2018-19/07), ICAR-IRA-BNF and Indo-UK IUNFC project of the Department of Biotechnology (BT/IN/UK/VNC-41/DLN/2015-16), Government of India, New Delhi.

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DPT, RP, VC: Conceptualization, Supervision and Funding Acquisition. DPT and AH: Data curation, Investigation, Methodology. KA and VC: Funding acquisition. RP, VC, and RB: Resources and Project administration. DPT and AH: Software run and Formal analysis. AH and DKJ: Validation. DPT: Writing original draft. RP, AH, DPT and DKJ: Writing – review & editing.

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Thenappan, D.P., Pandey, R., Hada, A. et al. Physiological Basis of Plant Growth Promotion in Rice by Rhizosphere and Endosphere Associated Streptomyces Isolates from India. Rice 17 , 60 (2024). https://doi.org/10.1186/s12284-024-00732-w

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• Streptomyces sp.
• Actinobacterial strains
• Plant growth promotion
• Phytohormone pathways
• Integrated nutrient management
• Oryza sativa