plant growth experiment hypothesis

Testing a Hypothesis—Plant Growth

Charles Darwin believed that there were hereditary advantages in having two sexes for both the plant and animal kingdoms. Some time after he wrote  Origin of Species , he performed an experiment in his garden. He raised two large beds of snapdragons, one from cross-pollinated seeds, the other from self-pollinated seeds. He observed, “To my surprise, the crossed plants when fully grown were plainly taller and more vigorous than the self-fertilized ones.” This led him to another, more time-consuming experiment in which he raised pairs of plants, one of each type, in the same pot and measured the differences in their heights. He had a rather small sample and was not sure that he could safely conclude that the mean of the differences was greater than 0. His data for these plants were used by statistical pioneer R. A. Fisher to illustrate the use of a  t -test.

Looking at Darwin’s Data

1. Open  Darwin.ftm  from the  Tutorial Starters   folder in the  Sample Documents   folder.  This document contains the data for the experiment described above: 1 attribute, 15 cases.

2. Make a case table, a dot plot, and a summary table similar to those shown here.

We see that most of the measurements are greater than 0, meaning that the cross-pollinated plants grew bigger. But two of the measurements are less than 0. Darwin did not feel justified in tossing out these two values and was faced with a very real statistical question.

Formulating a Hypothesis

Darwin’s theory—that cross-pollination produced bigger plants than self-pollination—predicts that, on average, the difference between the two heights should be greater than 0. On the other hand, it might be that his 15 pairs of plants have a mean difference as great as they do (21-eigths of an inch) merely by chance. You can write out these two hypotheses in Fathom in a text object to be stored with your document.

3. From the shelf, drag a text object into the document.

4. Write the null hypothesis and the alternative hypothesis. At right you can  see one way to phrase the hypotheses.

You can choose  Edit | Show Text Palette  to bring up a full suite of tools for formatting text and creating mathematical expressions.

Deciding on a Test Statistic

At the time of Darwin’s experiment, there was no very good theory for dealing with a small sample from a population whose standard deviation is not known. It was not until some years later that William Gosset, a student of Karl Pearson, developed a statistic and its distribution. Gosset published his result under the pseudonym Student, and the statistic became known as Student’s  t . When the null hypothesis is that the mean is 0, the  t -statistic is simply, x ̄/( s /√ n ), where x ̄ is the observed mean,  s  is the sample standard deviation, and  n  is the number of observations.

Let’s compute this statistic for Darwin’s data using one of Fathom’s built-in statistics objects.

5. Drag a test object from the shelf.  An empty test appears.

6. From the pop-up menu, choose  Test Mean .  As shown at right, the Test Mean test allows us to type in summary statistics. The blue text is editable. This is very useful when you don’t have raw data.

7. Try editing the blue text. You can, for example, enter the summary statistics for Darwin’s data.

Here are some things to notice.

• Changing something in one part of the test may affect other parts. For example, editing the AttributeName field in the first line also changes it in the hypothesis line and in the last paragraph.

• In the hypothesis line, clicking on the “is not equal to” phrase brings up a pop-up menu from which we can choose one of three options. For Darwin’s experiment, we want the third option because his hypothesis is that the true mean difference is greater than 0 . Notice that making this change alters the phrasing of the last line of the test as well.

• In addition to simple editing of numbers, we can also determine their value with a formula. For example, we might want to tie the sample count to a slider named n so that we could investigate the effect of different sample sizes. To show the formula editor, choose  Edit | Edit Formula  with the text cursor in the number whose value you wish to determine. These computed values display in gray instead of blue. Editing the value itself deletes the formula.

Checking Assumptions

Gosset’s work with the t -statistic relied on an assumption about the population from which measurements would be drawn, namely, that the values in the population are normally distributed. Is this a reasonable assumption for Darwin’s data?

Height measurements of living things, both plants and animals, are usually normally distributed, and so are differences between heights. But we might worry, because the two negative values give a decidedly skewed appearance to the distribution.

Fathom can help us determine qualitatively whether this amount of skew is unusual. We’ll generate measurements randomly from a normal distribution and compare the results with the original data.

8. Make a new attribute in the collection. Call it  simHeight  for simulated height.

9. Select  simHeight  and choose  Edit | Edit Formula . Enter the formula shown below.

This formula tells Fathom to generate random numbers from a normal distribution whose mean and standard deviation are the same as in our original data. We want to compare the distribution of these simulated heights with the distribution of the original data. We can do that directly in the dot plot that already shows  HeightDifferences .

10. Drop  simHeight  on the plus sign to add it to the horizontal axis.  The graph now shows the original data on top and the simulated data on the bottom.

One set of simulated data doesn’t tell the whole story. We need to look at a bunch.

11. Choose  Collection | Rerandomize .

Each time you rerandomize, you get a new set of 15 values from a population with the same mean and standard deviation as the original 15 measurements. Three examples are shown below.

A bit of subjectivity is called for here. Does it appear that the original distribution is very unusual, or does it fit in with the simulated distributions?

Testing the Hypothesis

Once we have decided that the assumption of normality is met, we can go on to determine whether the  t -statistic for Darwin’s data is large enough to allow us to reject the null hypothesis.

In step 7, we typed the summary values into the test as though we didn’t have the raw data. But we are in the fortunate position of having the raw data, so we can ask Fathom to figure out all the statistics using that data.

12. Drag   HeightDifferences  from the case table to the top pane of the test where it says “Attribute (numeric): unassigned.”

13. If the hypothesis line does not already say “is greater than,” then select that choice from the pop-up menu.

The last paragraph of the test describes the results. If the null hypothesis were true and the experiment were performed repeatedly, the probability of getting a value for Student’s  t  this great or greater would be 0.025. This is a pretty low  P -value, so we can safely reject the null hypothesis and, with Darwin, pursue the theory that cross-pollination increases a plant’s height compared with self-pollination.

Looking at the t -Distribution

It is helpful to be able to visualize the P -value as an area under a distribution.

14. With the test selected, choose  Test | Show Test Statistic Distribution .  The curve shows the probability density for the t -statistic with 14 degrees of freedom. The shaded area shows the portion of the area under the curve to the right of the test statistic for Darwin’s data. We’ve set this up as a one-tailed test; we’re only interested in the mean difference being greater than zero. The total area under the curve is 1, so the area of the shaded portion corresponds to the P -value for Darwin’s experiment.

Let’s investigate how the P -value depends on the test mean, which is currently set to 0.

15. Drag a slider from the shelf into the document.

16. Edit the name of the slider from  V1  to  TestMean .

17. Select the 0 in the statement of the hypothesis in the test. Choose  Edit | Edit Formula .

18. In the formula editor, enter the slider name   TestMe an  and click  OK .

Now the value of the null hypothesis mean in the test and the shaded area under the  t -distribution change to reflect the new hypothesis.

19. Drag the slider slowly and observe the changes that take place.

For what value of the slider is half the area under the curve shaded? Explain why it should be this particular value.

The illustration below shows something similar to what you probably  have. Note that the test has been switched to “nonverbose” (choose  Test | Verbose ).

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.

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

Related Posts

ORIGINAL RESEARCH article

Testing the growth rate hypothesis in two wetland macrophytes under different water level and sediment type conditions.

• 1 Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
• 2 School of Environment and Life Science, Nanning Normal University, Nanning, China
• 3 Dongting Lake Station for Wetland Ecosystem Research, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
• 4 College of Architecture and Urban Planning, Hunan City University, Yiyang, China

The growth rate hypothesis (GRH) states that a negative correlation exists between the growth rate and N:P and C:P ratios, because fast-growing organisms need relatively more phosphorus-rich RNA to support their high rates of protein synthesis. However, it is still uncertain whether the GRH is applicable in freshwater wetlands. Several studies have shown that water level and sediment type are key factors influencing plant growth and plant C:N:P characteristics in freshwater wetlands. Thus, this study aimed to elucidate the influence of these factors on plant growth and test the GRH under varying water levels and sediment conditions. We designed a controlled experiment at three water levels and under three sediment types using the two dominant plants ( Carex brevicuspis and Polygonum hydropiper ) in the East Dongting Lake wetland, and we further investigated the relative growth rate (RGR); concentrations of total carbon (TC), total nitrogen (TN), and total phosphorus (TP); and plant stoichiometry (ratios of C:N, C:P, and N:P) in the aboveground and belowground parts and whole plants in both species. Results demonstrated that the RGR and TC of both species decreased significantly with decreasing sediment nutrient supply and increasing water level. However, TN and TP of both species were markedly higher at high water levels than at low water levels; furthermore, these were significantly higher on clay than on the other two sediment types at each water level. The C:N and C:P ratios of both species decreased with increasing sediment nutrient supply and water level, whereas N:P decreased in both species with increasing sediment nutrient supply. The aboveground part of C. brevicuspis as well as the aboveground part and whole plant of P. hydropiper were negatively correlated with N:P, which is consistent with the GRH. However, the relationship between the belowground RGR and N:P of these species was inconsistent with GRH. Therefore, the water level and sediment type and their interaction significantly influenced plant RGR and C:N:P characteristics. The RGR and plant stoichiometry differed significantly between plant organs, indicating that the GRH needs refinement when applied to wetland macrophytes.

Introduction

The growth rate hypothesis (GRH) proposes that fast-growing organisms have low N:P and C:P ratios due to the relatively high demand for phosphorus-rich RNA to support rapid protein synthesis ( Acharya et al., 2004 ). Various comprehensive reviews confirmed that nutrient-rich plants tend to have low N:P ratios, and supported the validity of GRH in the realm of vascular plants, as N concentration in vascular plants tends to increase less than P concentration ( Wright et al., 2005 ; Kerkhoff and Enquist, 2006 ; Yu et al., 2012 ). However, opposite results were also reported ( Peng et al., 2010 ; Loladze and Elser, 2011 ). For instance, Matzek and Vitousek (2009) found that there was no link between growth rate and leaf N:P for pine species, because RNA comprises only a small proportion of total P (TP) to strongly influence leaf P concentration. To date, the GRH hypothesis has been tested in a variety of ecosystems, and at relatively large scales ( Güsewell, 2004 ; McGroddy et al., 2004 ; Lovelock et al., 2007 ); however, it is still uncertain whether it is applicable in freshwater wetlands.

Water level is the dominant factor influencing nutrient cycling and the structure of wetland plant communities ( Lowe et al., 2010 ; Sardans et al., 2012 ; Saaltink et al., 2018 ). It can constrain the growth and nutrient availability to wetland macrophytes mainly by limiting oxygen ( Casanova and Brock, 2000 ) and light ( Cronin and Lodge, 2003 ; Miao and Zou, 2012 ) availabilities and by changing soil nutrient cycling ( Steinman et al., 2012 ; Wang et al., 2015a ). For example, Carex brevicuspis , which has a relatively low growth rate, was reported to have high N:P ratio and high N and P concentrations at high water levels, both probably caused by anoxic stress ( Li et al., 2018a ). On the contrary, Li et al. (2013) found that increasing water level decreased the relative growth rate (RGR) of Potamogeton malaianu without affecting its N:P ratio and concentrations of N and P. This inconsistency indicates that the relationship between RGR and N:P ratio at different water levels and for different plant species is far from clear. Moreover, high water levels significantly affect soil nutrient availability by changing its geochemical cycle as well as the activity of soil microorganisms ( Niedermeier and Robinson, 2007 ; González Mace et al., 2016 ), thereby determining plant stoichiometry. For example, the soil mineralization process of organic N results in the accumulation of ammonium under anaerobic conditions, further affecting the N cycle of plants in wetlands ( Hefting et al., 2004 ). Soil P availability also increases due to the reduction of iron, which releases soluble P into the soil ( Bridgham et al., 1998 ; Saaltink et al., 2018 ). To date, many studies have focused on the effects of water level on plant growth and distribution ( Madsen et al., 2001 ; Li et al., 2012 ). However, the response of plant stoichiometry to varying water levels is still uncertain ( Cao et al., 2011 ; Yuan et al., 2013 ). Results from the few studies conducted so far are also inconsistent ( Miao and Zou, 2012 ; Li et al., 2013 ), indicating that changes in plant stoichiometry in response to water level might be species-specific and needs to be further studied.

Sediment type substantially affects plant growth rate and stoichiometry ( Luo et al., 2010 ; Li et al., 2018a ). Plants with high nutrient concentrations are able to extend their roots and enhance root uptake rate, thereby enhancing nutrient absorption abilities ( Fransen et al., 2001 ). For instance, plant RGR and concentrations of N and P in sandy sediments are lower than that in clay sediments due to the limited nutrient availability ( Li et al., 2015 ). However, the nutrient-rich sediment had no significant effect on the relative growth rates of Elodea canadensis and Callitriche cophocarpa possibly due to their low nutrient requirements ( Madsen and Cedergreen, 2002 ). Indeed, the relationship between sediment type and plant stoichiometry is often affected by water level in wetlands ( Xie et al., 2009 ; Li et al., 2017a ). The roots of wetland plants usually display contrasting properties to adjust to infertile or flooded environments, and higher water levels commonly further limit plant nutrient absorption ( Xie et al., 2009 ). Therefore, it is difficult to predict the effects of water level and sediment type on plant stoichiometry based on single factors. Although the changes in plant stoichiometry in different sediment types have been widely studied ( Morse et al., 2004 ; Li et al., 2018a ), few studies have focused on their interaction with plant C:N:P stoichiometry.

Carex brevicuspis and Polygonum hydropiper are dominant species in the vegetated zone of the East Dongting Lake wetland. C. brevicuspis is a perennial rhizomatous clonal plant widely distributed at low elevations (23–30 m). The belowground meristems of C. brevicuspis can produce long rhizomes (2–25 cm long), which are more capable of obtaining resources under stressful conditions, and short rhizomes (< 1 cm long), which are better at using resources in favorable patches. P. hydropiper is an annual herb forming patches embedded in stands of C. brevicuspis , generally sensitive to flooding stress and inhabiting elevated sites over shallow flooded habitats. Compared to P. hydropiper , C. brevicuspis has a wider optimal hydrological niche in the East Dongting Lake wetland ( Chen et al., 2014 ; Li et al., 2018a ). In this study, we investigated the interactive effects of water level and sediment type on the growth performance and stoichiometry of C. brevicuspis and P. hydropiper. These two dominant species were planted under three water levels (-30 cm, 0 cm, and 30 cm relative to the soil surface) and three sediment types (clay, sand, and a mixture of sand and clay at a 1:1 volume ratio) in a factorial design with five replicates. The RGR, total C (TC), total N (TN), TP, and C:N, C:P, and N:P ratios in the aboveground and belowground parts and in the whole plant of both species were measured for exploring the relationship between RGR and plant stoichiometry. As so, the present study aimed to (1) elucidate how differences in water level and sediment type affect plant growth and plant C:N:P characteristics; and (2) test whether the relationship between RGR and plant C:N:P stoichiometry is consistent with GRH under different water level and sediment type conditions.

Materials and Methods

Study site and plant materials.

Dongting Lake (28°30′–30°20′ N, 111°40′–113°10′ E) is the second-largest freshwater lake and the most typical river-connected lake in China; it is characterized by large seasonal fluctuations of the water level and sediment heterogeneity ( Xie et al., 2007a ). The wetlands are usually completely flooded from May to October, while being susceptible to drought from November to April. The mean annual temperature is 16.8°C, with hot summers (June–August, 27.3°C) and cold winters (December–February, 5.8°C). The mean annual precipitation is 1,382 mm, with more than 60% of the rain falling from April to August ( Li et al., 2017b ).

Carex brevicuspis (Cyperaceae) is a typical perennial rhizomatous sedge distributed in eastern mainland China. The plant is usually 20–55 cm in height, and it flowers and bears fruit from April to May, before flooding occurs in the Dongting Lake wetland ( Chen et al., 2011 ). Polygonum hydropiper (Polygonaceae) is an annual herb 40–70 cm in height. Both species experience periodic flooding that normally occurs between May and October ( Chen et al., 2014 ).

C. brevicuspis was collected in Xiaoxihu and P. hydropiper was collected in Dingzidi, both in East Dongting Lake, during March 2016. New ramets were dug up and transported to the Dongting Lake Station for Wetland Ecosystem Research, Chinese Academy of Sciences. The new ramets (about 15 cm in height) were placed in plastic basins (55 cm in length, 33 cm in width, 21 cm in height) filled to a depth of 15 cm with soil (4.01 mg g -1 soil organic carbon, 0.48 mg g -1 soil TN, and 0.57 mg g -1 soil TP) that was collected from a C. brevicuspis and P. hydropiper mixed community in the East Dongting Lake. After one month, similar-sized plants (4–5 leaves, about 25 cm in height) were selected for the experiment.

Experimental Design

Before the experiment, ten seedlings of C. brevicuspis and ten seedlings of P. hydropiper were divided into aboveground and belowground parts, oven-dried, and weighed for the calculation of plant RGR ( Li et al., 2016 ). The experiment combined three water levels (-30 cm, 0 cm, and 30 cm relative to the soil surface) and three sediment types (clay, sand, 1:1 clay–sand mixture) with the two species in a factorial design with five replicates ( Table 1 ). Clay was collected from the location described above for ramet germination, and sand was collected from the local river. In the Dongting Lake wetland, most roots of both species are distributed in the top 0–20 cm soil layer ( Chen et al., 2014 ). Therefore, the -30 cm water level was considered the drought treatment, the 0 cm water level was considered the control, and the 30 cm water level was considered the submerged treatment ( Figure 1 ). The three sediment types used in the experiment are the main sediment types present in the natural habitat of C. brevicuspis and P. hydropiper in Dongting Lake. We sampled the clay soil from the same location as plant samples while the sand was collected from the local Xiang River ( Table 1 ). On April 2, 2016, the 1,350 similar-sized ramets collected (675 for each species) were transplanted into PVC tubes (30 cm in height and 12 cm in diameter, bottoms enclosed with a nylon netting to prevent soil loss) filled with sediment. Thirty tubes (3 water levels × 2 plant species × 5 tubes) were placed into each of 15 cement pools (1 × 1 × 1 m, five pools per sediment). Three seedlings were planted into each tube for both species, and the experiment started 7 days after planting. Tap water (containing 0.51 μg L -1 NH 4 -N, 1.76 μg L -1 NO 3 -N, and 0.53 μg L -1 PO 4 3+ -P, pH = 7.2) was completely replaced every two weeks to prevent algal growth ( Figure 1 ).

Table 1 Soil nutrient concentrations of each sediment type.

Figure 1 Experimental scheme, showing two plant species ( Carex brevicuspis and Polygonum hydropiper ), three sediment types (clay; mixture; sand) and three water levels (-30 cm; 0 cm; 30 cm). Five replicates were made of each treatment.

Harvest and Measurements

All plants were harvested after 4 months of treatment. The roots of each plant were carefully excavated from the PVC tubes, cleaned with tap water, and transported to the laboratory for measurements. Plants in each tube were divided into aboveground and belowground parts, oven-dried at 80°C for 48 h, and weighed.

The RGR (relative growth rate) of the aboveground and belowground parts and of the whole plant were calculated for each species using the following formula:

where X 1 and X 2 are the biomass of the aboveground or belowground parts or of the whole plant at the end and start of the experiment, respectively, and T is the duration of the experiment ( Yuan et al., 2016 ).

Total C, N, and P Concentrations

The aboveground and belowground parts and the whole plant of each species in each PVC tube were ground into powder and analyzed for TC and TN using an elemental analyzer (Vario EL III; Elementar, Hanau, Germany). Total P was measured with colorimetric analysis on a TU-1901 spectrophotometer (Beijing Purkinje General Instrument Co., Ltd., Beijing, China) after being pretreated by H 2 SO 4 –H 2 O 2 digestion ( Xie et al., 2007b ). Three replicates were used to determine plant C, N, and P concentrations.

Statistical Analyses

The mean values of the five replicates for each treatment in each pool were used for data analysis. The effect of water level and sediment type on RGR, TC, TN, and TP concentrations and the stoichiometry of the aboveground and belowground parts and whole plant of each species were assessed using a general linear model (GLM). Multiple comparisons of the means were performed using Tukey ’ s test at the 0.05 significance level. All statistical analyses were performed in SPSS 20.0 (SPSS Inc., Chicago, IL, USA).

RGRs of C. brevicuspis and P. hydropiper

The RGR of the aboveground and belowground parts and whole plants of C. brevicuspis and P. hydropiper were significantly affected by water level, sediment type, and their interaction ( Table 2 ; Figure 2 ). The RGR decreased significantly with increasing water levels in all sediment types, and the highest values of both species were found in the -30 cm water level + clay treatment while the lowest values were found in the 30 cm water level + sand treatment.

Table 2 Summary of general linear model (GLM) on plant relative growth rate (RGR), concentrations of TC, TN, and TP, and ratios of C:N, C:P, and N:P in C. brevicuspis and P. hydropiper growing in three water levels and three sediment types ( F -values).

Figure 2 Relative growth rate (RGR) in aboveground part, belowground parts and whole plants of C. brevicuspis (A, C, E) and P. hydropiper (B, D, F) in treatments with three sediment types (clay; mixture; sand) and three water levels (-30 cm; 0 cm; 30 cm). Values are means ± SE, with five replications. Different letters indicate significant difference among treatments at 0.05 significance level.

Both water level and sediment type had significant effects on TC, TN, and TP concentrations in the aboveground and belowground parts and whole plants of both species ( P < 0.001) ( Table 2 ). The highest TC concentrations in the aboveground and belowground parts and whole plants of both species were found in the -30 cm water level + clay treatment and they decreased significantly with decreasing sediment nutrient concentration and increasing water level. The TN and TP concentrations in aboveground and belowground parts and whole plants of both species were highest in the 30 cm water level + clay treatment, and they decreased significantly with decreasing sediment nutrient concentration and water level ( Figure 3 ).

Figure 3 Concentrations of TC (A–F) , TN (G–L) , and TP (M–R) (means ± SE) in aboveground part, belowground parts and whole plants of C. brevicuspis and P. hydropiper growing in three sediment types (clay; mixture; sand) and three water levels (-30 cm; 0 cm; 30 cm). Different letters indicate significant differences among treatments ( P < 0.05).

C, N, and P Stoichiometry Ratios

Water level and sediment type significantly affected C:N and C:P ratios in the aboveground and belowground parts and whole plants of C. brevicuspis and P. hydropiper ( Table 2 ). The C:N and C:P ratios in the aboveground and belowground parts and whole plants of both species decreased with increasing sediment nutrient supply and water level. The highest N:P ratios in the aboveground and belowground parts and whole plants of P. hydropiper were found in the 0 cm + sand treatment. The highest N:P ratio in the aboveground part of P. hydropiper was found in the 0 cm + mixture treatment and in the belowground part and whole plant were found in the -30 cm + mixture treatment ( Figure 4 ).

Figure 4 Ratios of C:N (A–F) , C:P (G–L) , N:P (M–R) (means ± SE) in aboveground and belowground parts and the whole plants of C. brevicuspis and P. hydropiper growing in three sediment types (clay; mixture; sand) and three water levels (-30 cm; 0 cm; 30 cm). Different letters indicate significant differences among treatments ( P < 0.05).

Relationships of RGR With C, N, and P Stoichiometry

In C. brevicuspis , the RGR of the aboveground part was positively correlated with TC and TP concentrations and negatively correlated with N:P ratio, while the RGR of the belowground part and whole plant were positively correlated with TC and TN concentrations and with C:P and N:P ratios ( Figure 5 ).

Figure 5 Relationships between relative growth rate (RGR) and concentrations of TC (A) , TN (C) , TP (E) , and ratios of C:N (B) , C:P (D) , N:P (F) (means ± SE) in aboveground and belowground parts and the whole plants of C. brevicuspis and P. hydropiper .

In P. hydropiper , the RGR of the aboveground part and whole plant were positively correlated with the TC, TN, and TP concentrations and negatively correlated with the C:N, C:P, and N:P ratios. The RGR of the belowground part was only positively correlated with TC concentration ( Figure 5 ).

The RGR of the aboveground and belowground parts and whole plants of both species decreased significantly with decreasing sediment nutrient concentrations and increasing water levels, indicating that water level, sediment type, and their interaction had a significant effect on plant growth performance ( Emery et al., 2001 ; Xie et al., 2009 ; Luo et al., 2010 ). The negative effect of high-water levels on plant growth has been reported in many studies, and it has been mainly attributed to the anaerobic environment and reduced soil redox potential, Eh ( Sorrell et al., 2000 ; Steinman et al., 2012 ). In some of the treatments conducted in the present study, e.g., 0 cm water level + mixture and 30 cm water level + clay, the similar growth performance of the aboveground parts of C. brevicuspis indicated that the negative influence of water level on plant growth could be ameliorated in nutrient-rich conditions, as supported by other studies ( Wheeler, 1999 ; Xie et al., 2009 ). Nutrient availability may increase plant root respiration and root diameter and help plants to acclimate to high water level conditions ( Xie et al., 2009 ; Chen et al., 2016 ).

The TC concentrations in the aboveground and belowground parts and whole plants of both species decreased significantly with increasing water levels, which was consistent with previous studies ( Li et al., 2013 ; Yuan et al., 2016 ). High water levels decrease plant photosynthesis, thus leading to a reduction in the synthesis of non-structural carbohydrates in plant tissues ( Cao et al., 2009 ; Su et al., 2016 ). Plant C balance can be characterized by tissue concentrations of non-structural carbohydrates. When C supply from photosynthesis exceeds the plant’s demand for growth, a large amount of non-structural carbohydrates will accumulate to support future growth. By contrast, when C demand exceeds the C supply, non-structural carbohydrates will only slightly accumulate ( Wang et al., 2018 ). Similar to RGR, plant C concentrations in both species were also higher in the clay treatment than in other sediment types, as soil nutrients are the main determinants of plant nutrient concentrations and therefore influence plant growth ( Li et al., 2017b ). Wang et al. (2015b) and Zeng et al. (2017) also reported that nutrient-rich sediment conditions result in high C concentration.

The TN and TP concentrations in the aboveground parts of both species were higher compared with those in the belowground parts and whole plants. As described in previous studies ( Li et al., 2013 ; Jing et al., 2017 ), this phenomenon can be explained by the presence of large amounts of rubisco in the photosynthetic organs ( Reich et al., 2004 ). The TN and TP concentrations in the aboveground and belowground parts and whole plants of both species increased, while C:N and C:P ratios decreased with increasing water level, which was consistent with previous studies ( Cronin and Lodge, 2003 ; Li et al., 2013 ). For example, TN and TP concentrations of Cladium jamaicense increased significantly when water levels increased from 20 to 60 cm ( Miao and Zou, 2012 ). In this study, plants were submerged in 30 cm of water, where light availability was low. The light conditions at the -30 cm water level lead to lower leaf N, probably due to the dilution of available N by increased amounts of fixed C ( Cronin and Lodge, 2003 ). Therefore, lower N and P availability for plant photosynthesis will lead to high plant N and P concentrations. Another study also confirmed that the biomass accumulation of C. brevicuspis increased with increasing elevation, while plant TN and TP concentrations decreased, which might have accounted for the dilution effect by which fast-growing plants allocate more N and P to their photosynthetic tissues to support high carbon dioxide assimilation ( Yan et al., 2006 ; Li et al., 2018b ). Water level can also influence plant nutrient absorption by changing soil biogeochemical processes ( Steinman et al., 2012 ; Recha et al., 2013 ). For instance, ammonification is the dominant process at high water levels ( Hefting et al., 2004 ), and it enhances the concentration of available N, promoting plant N absorption ( Kaštovská and Šantrůčková, 2011 ). In addition, soil anoxia can reduce iron plaque formation on roots at high water levels, and thus promote plant P uptake ( Saaltink et al., 2018 ).

At the same water level, the higher TN and TP concentrations and lower C:N, C:P, and N:P ratios in the aboveground and belowground parts and whole plants of both species on the clay sediment indicated that sediment nutrients mainly affect plant nutrients, which could further influence plant stoichiometry ( Garbey et al., 2004 ; Chen et al., 2013 ; Li et al., 2014 ). In this study, sediment N and P concentrations in the clay sediment were 2.0 and 1.6 times higher than those in the sand sediment, leading to higher plant N and P concentrations. Moreover, it has been reported that high sediment nutrient levels can promote plant growth and enhance plant nutrient concentrations ( Fraser and Feinstein, 2005 ; Güsewell, 2005 ). A high clay content would therefore promote soil N mineralization and plant N absorption, while a high sand content allows a higher rate of P leaching ( Cross and Schlesinger, 2001 ).

The N:P ratio in the aboveground parts of both species and whole plant of P. hydropiper were negatively correlated with their corresponding RGR, thus supporting the GRH and being consistent with previous studies ( Niklas et al., 2005 ; Niklas, 2006 ; Ågren, 2008 ; Cernusak et al., 2010 ). Ågren (2004) reported that P limited Betula pendula seedlings, which displayed decreased N:P at high RGR, supporting the GRH. As a possible explanation, Sterner and Elser (2002) proposed that organisms have to make a relatively large investment in P-rich ribosomes and rRNA to support the rapid protein synthesis associated with fast growth. However, opposite results were found in other studies ( Cernusak et al., 2010 ; Peng et al., 2010 ). One possible reason for these inconsistent results might be that some plants can store extra nutrients and thus change the relationship between the RGR and the N:P ratio ( Jing et al., 2017 ). Matzek and Vitousek (2009) also showed that plant protein:RNA ratio, but not leaf N:P ratio, was significantly negatively correlated with plant growth rate.

The relationship between RGR and plant stoichiometry in the belowground parts of both species and whole plant of C. brevicuspis suggests that the GRH is not valid in these cases, indicating that the applicability of this hypothesis might depend on plant organ and species. In fact, another study reported that the GRH was not consistent with the growth of various organs ( Jing et al., 2017 ). One probable reason might be that a change in environmental factors may lead to the allometric growth of different organs, and the stoichiometry of roots is more sensitive to environmental changes than that of leaves ( Minden and Kleyer, 2014 ; Schreeg et al., 2014 ). For instance, Jing et al. (2017) confirmed that N addition significantly increased the N:P ratio and RGR of Pinus tabuliformis roots in N-limited regions, resulting in a positive relationship between the RGR and N:P ratio of roots. Another reason might be that plants have developed survival strategies other than growth (e.g., storage and defense) that require N and P, in which case a decreasing N:P ratio with increasing growth rate should not necessarily be expected ( Matzek and Vitousek, 2009 ). In addition, plants can store P in vacuoles, allocate N to the production of chemical defenses, or invest different N:P ratios in different organs, all of possibly explaining why P concentration is not greater in fast-growing plants ( Méndez and Karlsson, 2005 ; Peñuelas and Sardans, 2009 ). However, our results were inconsistent with previous studies ( Ågren, 2004 ; Yu et al., 2012 ). For instance, Yu et al. (2012) confirmed that the GRH was valid for the roots of three grass plants in the grasslands of Inner Mongolia, and they also proposed that analysis of the relationship between RGR and N:P ratio should consider the N in ribosomes of vascular plants.

In addition, the RGR of the aboveground and belowground parts and whole plant of C. brevicuspis were lower than that of P. hydropiper , while the N:P ratios in the aboveground and belowground parts and whole plant of C. brevicuspis were relatively higher compared with those of P. hydropiper . These differences between the two species might be related to the higher tolerance of C. brevicuspis to water stress and drought stress compared with P. hydropiper ( Chen et al., 2014 ). Namely, stress tolerant plants (characterized by slow growth) have consistently higher N:P ratios than fast-growing plants in wetlands, as the former can focus on the uptake of nitrate while maintaining P reserves due to low internal P demands and efficient conservation ( Willby et al., 2001 ).

This study confirmed that water level, sediment type, and their interaction significantly influence plant growth and plant stoichiometry. Furthermore, we also established that the GRH is valid for the whole plant of P. hydropiper and the aboveground parts of both species, but not for whole plant of C. brevicuspis and the belowground parts of both species. These results indicate that the GRH needs to be refined for application to macrophytes. However, our study was primarily based on controlled incubation conditions with a relative short duration. Therefore, further studies are still needed to test this hypothesis under long-term natural conditions. In recent years, the area of C. brevicuspis and P. hydropiper communities in Dongting Lake wetland were seriously reduced due to reduced water levels and anthropogenic disturbances. Therefore, understanding plant growth and stoichiometry characteristics would contribute to the better understanding of macrophytes ecological processes and to establish effective measures for macrophytes’ protection and biodiversity maintenance.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding authors.

Author Contributions

CH and FL wrote the manuscript and conducted the technical assays and statistical analyses. NY and Y-HX designed the experiment and edited the manuscript. X-SC and Z-MD contributed to data collection and interpretation. All authors contributed to the article and approved the submitted version.

This study was supported by the Joint Fund for Regional Innovation and Development of NSFC (U19A2051), the Youth Innovation Promotion Association of CAS (201861), Key R & D Projects in Hunan Province (2019SK2336) and Changsha Science and Technology Project (kq1907072), the Youth Innovation Development Program of Changsha (kq1802026), and the National Natural Science Foundation of China (31570431).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer [X-TL] declared a shared affiliation, though no other collaboration, with several of the authors [FL, Y-HX, X-SC, Z-MD] to the handling Editor.

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Keywords: water level, sediment type, growth rate hypothesis, plant stoichiometry, Carex brevicuspis , Polygonum hydropiper

Citation: Hu C, Li F, Yang N, Xie Y-h, Chen X-s and Deng Z-m (2020) Testing the Growth Rate Hypothesis in Two Wetland Macrophytes Under Different Water Level and Sediment Type Conditions. Front. Plant Sci. 11:1191. doi: 10.3389/fpls.2020.01191

Received: 23 March 2020; Accepted: 22 July 2020; Published: 05 August 2020.

Reviewed by:

Copyright © 2020 Hu, Li, Yang, Xie, Chen and Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nan Yang, [email protected] ; Yong-hong Xie, [email protected]

† These authors have contributed equally to this work

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• Published: 28 April 2021

The relationship between plant growth and water consumption: a history from the classical four elements to modern stable isotopes

• Oliver Brendel   ORCID: orcid.org/0000-0003-3252-0273 1  

Annals of Forest Science volume  78 , Article number:  47 ( 2021 ) Cite this article

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Key message

The history of the relationship between plant growth and water consumption is retraced by following the progression of scientific thought through the centuries: from a purely philosophical question, to conceptual and methodological developments, towards a research interest in plant functioning and the interaction with the environment.

The relationship between plant growth and water consumption has for a long time occupied the minds of philosophers and natural scientists. The ratio between biomass accumulation and water consumption is known as water use efficiency and is widely relevant today in fields as diverse as plant improvement, forest ecology and climate change. Defined at scales varying from single leaf physiology to whole plants, it shows how botanical investigations changed through time, generally in tandem with developing disciplines and improving methods. The history started as a purely philosophical question by Greek philosophers of how plants grow, progressed through thought and actual experiments, towards an interest in the functioning of plants and the relationship to the environment.

This article retraces this history by following the progression of scientific questions posed through the centuries, and presents not only the main methodological and conceptual developments on biomass growth and transpiration but also the development of the carbon isotopic method of estimation. The history of research on photosynthesis is only touched briefly, but the development of research on transpiration and stomatal conductance is presented with more detail.

Research on water use efficiency, following a path from the whole plant to leaf-level functioning, was strongly involved in the historical development of the discipline of plant ecophysiology and is still a very active research field across nearly all levels of botanical research.

1 Introduction

The ratio of biomass accumulation per unit water consumption is known today as water use efficiency (WUE) and is widely relevant to agriculture (e.g. Blum 2009 ; Tallec et al. 2013 ; Vadez et al. 2014 ), to forest ecology (e.g. Linares and Camarero 2012 ; Lévesque et al. 2014 ) and in the context of global climate change (Cernusak et al. 2019 ). This ratio can be defined at various levels, from the physiological functioning of a leaf to the whole plant and at the ecosystem level. This historical review starts at the whole plant level, where WUE can be simply measured by quantifying the amount of water given to a plant and the plant’s increase in biomass during the experiment. The ratio of biomass produced divided by the cumulative water lost during growth is termed whole plant transpiration efficiency (TE= biomass produced/water lost). Historically, the ratio has also been calculated in its inverted form (water lost/biomass produced) and various terms have been used to denote these ratios (see Box 1). As knowledge, concepts and technology advanced, it became desirable to measure TE also at the leaf level, where it is defined either as the ratio of net CO 2 assimilation rate to transpiration (or to the stomatal conductance for water vapour). Therefore, some history of the two leaf-level components of WUE is included here. Numerous articles have been published on the history of the development of research on photosynthesis, and other than the reviews cited in this article, the publications by Govindjee are notable, especially Govindjee and Krogmann ( 2004 ), as they include a long list of other writings on the history of photosynthesis. On the other hand, little has been written about the history of research on transpiration and stomatal conductance. Notable is Brown ( 2013 ), who wrote specifically on the cohesion-tension theory of the rise of sap in trees, including many writings from the late nineteenth century. Consequently, here, photosynthesis research is only broached briefly, whereas transpiration research is more detailed.

As the development of the research on WUE spans a very long period, starting with Greek philosophers, publications are in several languages. Classical writings were in Greek or in Latin, and for these translations are available. However, from the mid-seventeenth century onwards, national languages were more and more used, which can be seen in the number of French- and German-language publications. This review is also a tribute to these nowadays less known seventeenth, eighteenth and nineteenth century French and German natural philosophers and their contribution to the development of the science of plant ecophysiology. Also, towards the beginning of the twentieth century, publications became too numerous to allow a comprehensive review; thus, the author focussed on the use of the carbon stable isotopes methodology and on tree ecology.

Box 1 Short history of names for whole plant transpiration efficiency (TE)

2 What is plant matter made of?

Various Greek philosophers were interested in how substances can change from one thing into another. Thales (624–c. 546 BC) thought that all things come from water, whereas Anaximenes argued that “pneuma” (air) should be the basis of all things (Egerton 2001a ). These assertions were the basis of more than 2000 years of philosophical dispute.

In “De Causis Plantarum”, Theophrastos (371–287 BC) assumed that plants draw nutrition, which consisted of varying amounts of the four elementary humours, from the earth through their roots (Morton 1981 ). Some centuries later, in a Christian work translated in 400 AD from Greek into Latin and known as “Pseudo-Clement’s Recognitions”, an apparent thought experiment was described to “prove that nothing is supplied to seeds from the substance of the earth, but that they are entirely derived from the element of water and the spirit (spiritus) that is in it” (Egerton 2004c ). The author of this thought experiment suggested putting earth into big barrels, growing herbaceous plants in it for several years, then harvesting them and weighing them. His hypothesis was that the weight of the earth would not have changed, and the author used this as an argument that the vegetation biomass could have come only from water. This thought experiment revealed a progress in scientific thinking because the question was posed more precisely than before. It stood out at a time when botany mainly consisted of naming plants and “theoretical botany effectually went out of existence” (Morton 1981 ).

It appears that the question of how plant matter is produced was not pursued in Roman or Arabic writings, which were more concerned with agricultural (the former) and medical (the latter) aspects of plant sciences (Egerton 2001b , 2002 ). Not until the High Middle Ages was a renewed interest shown in plant growth. Adelard of Bath, a twelfth century English natural philosopher, devoted the first four chapters of “Questiones Naturales” (c. 1130–1140; Morton 1981 ) to the question of what plant matter is made of. He argued, within the concepts of the four elements theory, “by just as much as water differs from earth, by so much does it afford less nourishment to roots, I mean than earth does”, clearly being in favour of earth as the source for plant nourishment. His arguments were only theoretical and speculative.

A major step occurred in botanical sciences between the fifteenth and sixteenth centuries; scholars began making experiments to test antique and medieval hypotheses against observations in nature (Egerton 2003 ). In the mid-fifteenth century, and probably related to the translation and printing of the botanical books by Theophrastus (Morton 1981 ), the thought experiment from “Recognitions...” was taken up by Nicholas of Cusa in the fourth part of his “Idiota de mente”, “De staticis experiments”. At a time when the naming of plants for pharmacology was the major interest of savants, he proposed experimental investigations. Nicholas of Cusa described the same thought experiment as did Pseudo-Clement’s Recognitions ; he concluded similarly that “the collected herbs have weight mainly from water” (1450; translation into English by Hopkins 1996 ). Cusa additionally suggested that the plants should be burned at the end of the experiment and the ash weight be taken into account. It is not clear whether the thought experiment was ever physically done.

In the sixteenth century, botanical science began to separate from medical sciences, with the establishment of lectureships in universities (e.g. Padua in 1533) and the establishment of botanical gardens (Egerton 2003 ). The bases existed for advancing science in the seventeenth century of Enlightenment. Francis Bacon, an influential philosopher of his time, conducted a series of plant growth experiments which are reported in his “de Augmentis Scientiarum” (1623; Spedding et al. 1900 ). Bacon discovered that some plants sprouted more quickly in water than in soil (Egerton 2004b ). He concluded that “for nourishment the water is almost all in all, and that the earth doth but keep the plant upright, and save it from over-heat and over-cold” (Hershey 2003 ), thus still upholding the theory proposed by Thales and Nicholas of Cusa. In “The History of the Propagation and Improvement of Vegetable”, Robert Sharrock ( 1660 ) reported that some plants both rooted and grew entirely in water. Although he noted different amounts of transpiration over time, he did not discuss this in relation to plant growth.

In 1662, Johannes Baptista van Helmont published his now-famous willow experiments (van Helmont 1662 ). This may be the first report of an experiment that was based on the thought experiment of Nicholas of Cusa (Hershey 2003 ) with the minor differences of beginning with dried soil and not using herbaceous plants, but rather a willow tree. After weighing the soil, he irrigated it with rain water and planted the weighed stem of a willow tree. The experiment ran for 5 years. At the end, the tree was weighed again, as was the dried soil. He found the soil weighed about 2 ounces less than at the beginning of the experiment, whereas 164 pounds of wood, bark and roots was produced. He concluded that the organic matter could only have come out of the water. Helmont was unaware of the existence of carbon dioxide, but he did know of “gas sylvestre”. He also knew that burning oak charcoal would produce nearly the same amount of gas sylvestre and ash. However, he did not connect this information with the plant growth he had observed (Hershey 2003 ). Robert Boyle published similar experiments in “The sceptical Chymist” (Boyle 1661 ). Boyle claimed that he had done his experiments before he knew of Helmont’s (Egerton 2004c ), although he discussed Helmont’s results and arguments in detail in his book. Boyle doubted the direct transformation of water into plant matter. He admitted, however, that it might be possible that other substances contained in the water could generate new matter (Boyle 1661 ). In the 1660’s, Edme Mariotte also criticised van Helmont’s theory that water alone constituted the only element to produce plant matter. He thought similarly to Boyle that elements in the water could contribute to the plant matter. He also showed that nitrogen compounds were important for plant growth (Bugler 1950 ).

John Woodward, in his “Some Thoughts and Experiments Concerning Vegetation” (Woodward 1699 ), took up again the question of what comprised the source of plant growth. Woodward criticised Helmont’s and Boyle’s experiments, mainly on the precision of weighing the dry soil before and after the experiment, but also the contamination of the irrigation water by terrestrial vegetable or mineral matter. Consequently, he developed a series of hydroponics experiments, where by growing plants in sealed vials, in different types of water and weighing them regularly over the same time period, he could calculate how much biomass was gained over a set time period. He was able to draw a series of conclusions from these experiments by calculating the ratio of water lost to plant mass gained in the same period of time, thereby calculating the inverse of transpiration efficiency. This was probably the first time that the inverse of transpiration efficiency was calculated using experimental data. He showed that 50 to 700 times as much water was lost than biomass gained. He also reported that plants grown in water containing more terrestrial matter grew more and with less water consumed. From these observations, he concluded that water serves only as a vehicle for the terrestrial matter that forms vegetables and that vegetable matter is not formed out of water. He is still remembered more for his geological publications (Porter 1979 ) than for his contributions to botany (Stanhill 1986 ).

In his “history of ecology” series, Egerton ( 2004c ) nicely sums this period thusly: “each of these authors (Bacon, Boyle, Helmont, Sharrock) built upon the work of his predecessors and improved somewhat the understanding of plant growth and how to study it. However, they still fell short of a basic understanding of plant growth. Before that could be achieved, chemists would have to identify the gases in the air”. This series of studies shows that from the end of the seventeenth century onwards, experiments replaced speculation (Morton 1981 ), in botany as well as in many other areas of science.

From the end of the seventeenth century, the question of how plants grow was still unresolved, although it was known that nutrients were conducted from the roots in the ascending sap to the leaves. A major improvement in the understanding of how transpiration and its variations work was the discovery of cells by Robert Hooke towards the middle of the seventeenth century (Egerton 2005 ) and subsequently the discovery of stomata on leaf surfaces. One of the first to describe stomata may have been Malpighi in “Anatomy of Plants” (Malpighi 1675 in Möbius 1901 ). Based on Malpighi’s and Grew’s ( 1682 ) studies, John Ray suggested in “Historia Plantarum” (Ray 1686 in Lazenby 1995 ) that the apertures in the leaves, when open, would give off either breath or liquid. Ray may have been the first to have connected stomata with transpiration. He also suggested that the loss of water by evaporation is compensated constantly by water from the stem, and thus transpiration results from a constant water flux. He also observed that sap ascends the stems of trees in sap-bearing vessels which do not contain valves. He did, however, admit that it cannot be capillary forces that make water go up tall trees.

Ideas on photosynthesis developed slowly from the middle of the seventeenth century onwards. Malpighi ( 1675 ) suggested that leaves produce (“concoct and prepare”) the food of plants and from leaves this food passes to all parts of the plant. Similarly, Claude Perrault in “Essais de Physique” (Perrault 1680 ) defended the hypothesis that the root acts as the mouth of the plant and that the leaves serve to prepare the food arriving with the sap from the root so that it can be used in the rest of the plant. John Ray in “History Plantarum” (Ray 1686 in Lazenby 1995 ) concurs with this, however adding in “The wisdom of God” (Ray 1691 in Lazenby 1995 ) that “not only that which ascends from the Root, but that which they take in from without, from the Dew, moist Air, and Rain”. He also thought that light could play a role in this preparation of the plant sap. At this time, most authors (Malpighi, Perrault, Mariotte, Ray) knew about the circulation of sap, up as well as down, and that leaves served somehow to transform the upcoming sap into food for the plant.

In 1770 , Lavoisier published “Sur la nature de l’eau” (“On the nature of water”, translation by the author) and reviewed the literature on the possibility of water changing into earth to nourish plants. Lavoisier cited the Van Helmont experiment and later works which tested Van Helmont’s idea by growing plants in water (e.g. Boyle, however he did not cite Woodward). He was critical of the idea that it could be a transformation of water that would constitute plant material. This was based mainly on experiments by himself and others, showing even distilled water would contain traces of “soil”. However, he also defended the idea, based mainly on Charles Bonnet’s observations, that leaves absorb vapours from the atmosphere that contribute to plant growth.

Helmont had coined the term “gaz” in the mid-seventeenth century and had been able to distinguish different gazes from air (Egerton 2004a ). It was only in the middle of the eighteenth century that gases were studied in the laboratory and several observations by different researchers would finally lead to an understanding of respiration and photosynthesis (Tomic et al. 2005 ; Nickelsen 2007 ). Richard Bradley seems to be one of the first to clearly state (in letters from 1721 to 1724) that plant nourishment can be drawn from the air. Hales ( 1727 ) agreed with this theory, which was not yet widely accepted (Morton 1981 ), and suggested that light might be involved, which helped to pave the way for the discovery of photosynthesis. Black ( 1756 ) was able to identify carbon dioxide (which he called fixed air) using a lime water precipitation test. He demonstrated that this “fixed air” did not support animal life or a candle flame (Egerton 2008 ). Charles Bonnet ( 1754 ) made an important observation, i.e. branches with leaves that were submerged under water would produce air bubbles on their surfaces when sunlight shone on them, but not after sunset. Senebier refined these experiments in 1781 (Morton 1981 ), by showing that the leaves produced no oxygen in the sunlight when the surrounding water was free of carbon dioxide and that the rate of oxygen production was higher with carbon dioxide-saturated water. Tomic et al. ( 2005 ) present nicely the steps leading up to the term photosynthesis. This began with Priestley ( 1775 ) demonstrating that the air given off by animals and by plants was not the same, Ingen-Housz ( 1779 ) observed the important role of light, and the dispute between Senebier and Ingen-Housz from 1783 to 1789 resolved more clearly the functions of carbon dioxide emission (respiration) and absorption (photosynthesis). Based on these results and his own very detailed observations, de Saussure reported in 1804 that the carbon necessary for plant growth is absorbed mainly by green leaves from atmospheric carbon dioxide and he estimated that the largest part of the accumulated dry matter of plants is made of this carbon. Thus, the dispute of what the plant matter is made of that began in antique Greece was resolved at the end of the eighteenth century.

3 How much water do plants need to grow?

The late eighteenth century marked the beginning of applied agricultural science and the rise of plant physiology (Morton 1981 ). Work continued on transpiration and stomata, with a large number of experiments. Burgerstein ( 1887 , 1889 ) managed to assemble 236 publications on transpiration of plants from 1672 to 1886, citing short abstracts of each and comparing them critically. Also, Unger published in 1862 a major review article covering such subjects as the relationship of transpiration to temperature and humidity; daily cycles, including night; differences in adaxial and abaxial leaf surfaces; the impact on transpiration of type, number, size and distribution of stomata; the structure of the epidermis (cell layers, cuticle, hairs and wax); development of the mesophyll; size of intercellular spaces and cell turgor; and the impact of plant transpiration on the atmosphere (Unger 1862 in Burgerstein 1887 ). Scientists started to reflect on the interaction of plants, or more specifically their leaves, with their environment, and experimentation included the responses of stomata to light quantity (Möldenhawer 1812 ) and quality (Daubeny 1836 in Burgerstein 1887 ). Based on inconsistent observations by e.g. Banks, Möldenhawer and Amici, advances were also made on the functioning of stomata (Mohl 1856 ). However, progress was mainly based on a comment in von Schleiden ( 1849 ) that the state of the stomata would be the result of the water in- or outflow of the pore cells (called “Schliesszellen”) and he showed experimentally that stomata close when the pore cells lose water. As knowledge of transpiration, stomatal opening and their dependence on environmental variables increased, new questions arose about the water consumption of plants.

Another milestone along the way to understanding the transpiration of plants in the nineteenth century was the publication by Sir John Bennet Lawes ( 1850 ), “Experimental investigation into the amount of water given off by plants during their growth; especially in relation to the fixation and source of their various constituents”. He described experiments on wheat, barley, beans, peas and clover using differently fertilised soils. He was using plants in closed containers and an especially designed balance to “estimate the amounts of water given off” (Fig. 1 ). He observed increased evapotranspiration with higher temperatures during the growing season, and asked whether “this increased passage of water through the plants, carrying with it in its course many important materials of growth from the soil, and probably also influencing the changes in the leaves of these, as well as of those derived from the atmosphere, will not be accompanied with an equivalently increased growth and development of the substance of the plant”. This was followed by an important discussion of the influence of temperature on evaporation and growth as well as the resultant ratio. He discussed in the introduction “the relationship of the water given off to the matter fixed in the plants”; he gave his results in this ratio and in the inverse ratio, and applied these ratios to different scientific questions. The first ratio (transpired water divided by plant matter, the inverse of today’s TE) was used to interpret his results in terms of water use compared to field available water, and the latter’s ratio (plant matter divided by transpired water, equivalent to today’s TE) was used to discuss his results in terms of functional differences among species. From the observed functional differences, he concluded that there was “some definite relationship between the passage of water through the plants and the fixation in it of some of its constituents”. He was, thereby, introducing a new question about the link between dry matter accumulation and transpiration, which will be treated in the next chapter.

Illustration from Lawes ( 1850 , p. 43) of the special balance constructed for weighing plants in their “jars” to estimate the amounts of water given off and also the “truck” on which a series of jars was moved to the balance

Towards the end of the nineteenth century, research interest started to include agricultural questions of water use. Marié-Davy ( 1869 ) measured transpiration (standardised by leaf surface) of over 30 plant species, including eight tree or shrub species as well as herbaceous and agricultural plants. He estimated transpiration per soil area, thereby establishing that a prairie would transpire more than trees. von Höhnel ( 1879 ) estimated long-term transpiration of branches of 15 tree species (standardised on leaf surface or leaf dry weight). He used these data of branch transpiration to upscale to whole trees and concluded that compared to agricultural plants, the amount of rain seemed sufficient for tree growth. Hellriegel ( 1871 ) had already similarly concluded for cereals in the Mark Brandenburg (Germany) region that rainfall would not be sufficient, as had Marié-Davy ( 1874 ) for wheat in the Paris (France) region. In parallel with these more quantitative interrogations about water use, from the mid-nineteenth century, scientists started to ask more functional questions about the relationship between transpiration and dry matter accumulation, in a context of vigorous growth of botanical sciences and the complex relation between organisms and their environment (Morton 1981 ).

4 Are transpiration and dry matter accumulation linked?

Lawes ( 1850 ) had already reflected on a functional relationship between water flux and plant matter accumulation. In the following years, there were several publications on the transpiration of trees, and although no transpiration efficiency was estimated, the understanding of tree transpiration advanced. Many comparative studies were published. Lawes ( 1851 ) on “Comparative evaporating properties of evergreen and deciduous trees” considered twelve different tree species. He provided measurements of the variation in transpiration with temperature and hygrometry data. With these, he concluded that “evaporation is not a mere index of temperature but that it depends on vitality influenced by heat, light and other causes”. In the late nineteenth century, several researchers estimated and compared values of the ratio of transpiration and dry matter accumulation for different plants (Burgerstein 1887 ). With the growing evidence of variation in this ratio, scientists started to reflect on the relationship between transpiration and dry matter accumulation, aided by the development of new measurement techniques. A major question was if there would be a tight coupling between transpiration and dry matter accumulation, resulting in a constant transpiration efficiency, or if variation could be observed.

Dehérain ( 1869 ) studied evaporation and the decomposition of carbonic acid in leaves of wheat and barley. Using an ingenious apparatus, he was probably the first to directly measure evaporation of water in parallel with carbonic acid decomposition. He studied the effect of variously coloured light, and although he did not calculate the ratio between evaporation and carbonic acid decomposition, he did conclude that light of different colours had a similar effect on carbonic acid decomposition and on water evaporation from the leaves. His final conclusion was that “it is likely that there is existing between the two main functions of plants, evaporation and carbonic acid decomposition, a link, of which we need to determine its nature” (translation from the original French by the author). Several other scientists also commented on the relationship between transpiration and dry matter production. Fittbogen ( 1871 ) supposed, similarly to Lawes ( 1850 ) before him, but with more experimental evidence, that there should be a positive relationship between transpiration and production of dry matter. Dietrich ( 1872 in Burgerstein 1887 ) supposed that this relationship would be linear, whereas Tschaplowitz ( 1878 in Burgerstein 1887 ) introduced the idea that there should be an optimum transpiration at the maximum production of matter. Therefore, when the transpiration would increase over this optimum, this would lead to a decrease in assimilation rate. He was one of the first to suggest a non-linear relationship between transpiration and assimilation. Sorauer in “Studies on evaporation” ( 1880 ) defended the hypothesis that transpiration was not only a physical phenomenon but was also physiological. He stated that “It is not possible as yet to study the plant internal processes which regulate the transpiration, however it is possible to quantify the relationship between dry-matter and transpiration” (translation from German by the author), suggesting thereby TE as a means to advance the understanding of plant internal processes. Sorauer was probably at the cutting edge of science of his time. He pointed out specifically that variability among plants of one species was due to genetics (German, “erbliche Anlagen”), a startling and even daring assertion for his time. He asserted that for comparative studies, genetic variability needed to be minimised. To achieve this, he used, when possible, seeds from the same mother plant, grown in the same environmental conditions and a large number of repetitions. Using these protocols, he was probably one of the first to estimate TE on tree seedlings, showing that there was within species diversity in transpiration and growth, but that their ratio was more constant. He concluded from experiments on pear and apple trees that the pear trees used less water for the same biomass growth. He was able to go one step further and demonstrate that this difference was due to less transpiration per leaf area. By comparing different woody and herbaceous plants with different growth types, he postulated that when plants had a small leaf area combined with high transpiration, they had either a very strong growth increment, a high dry matter percentage, or a large root system. Overall, he observed relationships between dry matter production and transpiration; he concluded that there must be some regulation of the transpiration per unit leaf area by the co-occurring dry matter production.

Hellriegel ( 1883 ) argued that one cannot estimate a constant ratio between transpiration and production as there were factors which influence each independently. He also commented that it might make sense to estimate mean values of transpiration for various agricultural plants, as this would be for practical and scientific value. He thought that the most logical standardisation would be by the mass of the dry matter produced during the same time period. He called this “relative Verdunstungsgrösse” which can be translated into English as “relative transpiration”. He was probably one of the first to give a name to the ratio between whole plant transpiration and dry matter production. He proposed a theory that for a long-term drought, plants would acclimate their morphology to decrease their “relative transpiration”. He provided additional experimental evidence that barley had decreased in relative transpiration over as many as seven levels of soil water deficit, relative to field capacity. Using his own observations, he proposed that when calculating a mean “relative transpiration” for a single species, variation of transpiration should be minimised and that plants should be tested together only under optimal conditions. Given the relatively small differences in relative transpiration that he observed among different crops, Hellriegel suggested that these differences would not explain why some crops grow better in wet locations and others on dry locations. Hellriegel was thus probably one of the first scientists to point out that the relationship between drought adaptation and “relative transpiration” might not be straightforward.

Understanding how biomass and water loss were connected was studied by Iljin ( 1916 ) on a newly detailed level. He measured simultaneously water loss and carbon dioxide decomposition and reported his data as grammes of water lost per cubic centimetre of carbon dioxide decomposed. He concluded from studying more than 20 plant species that “...it is generally agreed that the rates of water loss and of CO 2 assimilation are directly proportionate to stomatal aperture, and that consequently there exists a close connection between these two processes”.

At the end of the nineteenth century, the ratio of transpiration versus dry matter accumulation was recognised as an important plant trait, which varies among and within species in a complex interaction of each component with the other and with environmental factors.

5 How do plants differ in water requirement and how do they respond to variations in environmental factors?

In the late nineteenth century, several researchers estimated and compared values of the ratio of transpiration and dry matter accumulation for a range of cultivated plants (Fittbogen 1871 ; Dietrich 1872 ; Farsky 1877 , cited in Burgerstein 1887 ), giving evidence of the growing interest of agricultural scientists. The number of studies of transpiration efficiency greatly increased, thereby driving a new standardisation in terminology. King ( 1889 ) studied the inverse of transpiration efficiency and described it as “the amount of water required for a ton of dry matter”, and promulgated this terminology by using it in the titles of his publications between 1892 and 1895. Similarly, Leather ( 1910 ) published “Water requirements of the crops of India”, in which he defined the “transpiration ratio” as “the water transpired to the weight of dry plant produced”. The shift from a purely descriptive use of “water requirement” to a clearly defined one was provided by Kearney and Shantz ( 1911 ) as “… the degree to which a plant is economical in its use of water is expressed in its water requirement, or the total quantity of water which it expends in producing a pound of dry matter”. The term “water requirement” is the inverse of the modern transpiration efficiency, and was used by a rapidly increasing number of publications which were published on the water use of crops in the early twentieth century. Montgomery ( 1911 ) may have been the first to use the term for a plant trait in “Methods of determining the water requirements of crops”.

At the beginning of the twentieth century, the importance of gaining knowledge on the water requirements of plants can be seen in the technical effort that went into the measuring equipment. von Seelhorst ( 1902 ) presented a system of growing boxes on rails, placed belowground, including the balance, so that the top of the growing boxes was at the same level as the surrounding soil (Fig. 2 ). In the now well-known studies on “The water requirement of plants. I. Investigations in the Great Plains in 1910 and 1911”, Briggs and Shantz ( 1913a ) measured the water requirement for 21 crop and weed species, sometimes for different varieties of the same crop and under controlled and field conditions. In the same year, they reviewed the available literature on water requirement (Briggs and Shantz 1913b ), increasing their dataset to 31 different crop species. They discussed in detail studies from 29 different authors, many of which had only published once or twice on this subject. A few researchers were notable for their number of publications on the water requirement of crop plants: King with 6 publications between 1889 and 1905, and von Seelhorst with 9 publications between 1899 and 1907. The largest contributions came from Hellriegel ( 1883 ; 10 species) and Leather ( 1911 ; 15 species). Kiesselbach ( 1916 ) also reviewed 59 publications from 1850 to 1915 “which had studied transpiration in relation to crop yield, based upon plants grown beyond the seedling stage”. There were regular publications of original work from 1870s onwards, with more than one publication per year from 1890 onwards. The difference among species and the impact of environmental factors on water requirement was one of the main questions raised. These reviews and the increasing amount of newly published work per year are evidence of the growing interest in the “water requirement” of plants as a trait of increasing importance in agricultural sciences.

Illustration from von Seelhorst ( 1902 ), showing the quite sophisticated outdoor installation “Vegetationskasten” (growing boxes, translations by the author) to weigh plants in small waggons, with a “Kastenwagen” (boxwaggon), b “Waagebalken” (scale beam), c “Deckbretter” (cover board) and d “Waagentisch” (weighing table)

With regard to species differences in water requirement among crops, Schröder ( 1895 , cited in Maximov 1929 ) found two groups, among seven cereals, which differed in water requirement by a factor of 2. Millet, sorghum and maize were known to be drought resistant, and showed a lower water requirement than the remaining plants. These differences were confirmed by Kolkunov ( 1905 , cited in Maximov 1929 ), Briggs and Shantz ( 1914 ), Briggs and Shantz ( 1917 ) and Shantz ( 1927 ). Millet, sorghum and maize are now known to use the C4 carbon pathway of photosynthesis.

With regard to external environmental influences on plants, Briggs and Shantz ( 1913b ) distinguished between soil, atmosphere and plant factors. Soil factors which were investigated were soil moisture content, soil type, cultivation, soil volume, soil temperature, effect of fertilisers in soil or water cultures and effect of previous crops. Weather factors considered were air temperature and humidity, shade and carbon dioxide content. Other factors studied in direct relationship to the plants were parasite attacks, relative leaf area, cutting frequency, defoliation, planting density and the age of plants.

A critique of the term “water requirement” was not long in coming. Dachnowski ( 1914 ) wrote, “It is assumed by many writers that a definite and quantitative relation exists between transpiration and growth, and that hence the ratio of the weight of water absorbed and transpired by a plant during its growth to the green or dry substance produced is an adequate and simple measure of growth.”, followed by an argument why this was not the case.

6 Why do plants differ in transpiration efficiency?

The adaptations of plants to dry environments were an important ecological topic at the beginning of the twentieth century, as the discipline of “physiological ecology” (Iljin 1916 ; Moore 1924 ) began to develop. Iljin ( 1916 ) studied more than 20 different plant species in situ from different ecological locations, e.g. wet bottom soils and variously facing slopes of ravines with different aspects. Iljin proposed that “the water requirements of the different species should be very different, and consequently the amounts of water available should differently affect their processes of life”. Using his observations, he was able to show that “… in no case was the water loss per unit of decomposed CO 2 found to be equal to or more in xerophytes than in mesophytes”, thus suggesting a higher transpiration efficiency. He argued that mesophytes would have to close stomata “… in dry places in order to reduce evaporation, thus diminishing the rate of assimilation as well, whereas in the case of xerophytes, which are adapted to extreme conditions of existence, assimilation in similar circumstances proceeds actively”. He then tried to confirm his hypothesis by transplanting mesophytes from wetter sites to the drier environment of xerophytes. Iljin showed experimentally that in all cases, a higher water requirement was measured for mesophytes transferred to a drier site compared to their original site and compared to xerophytes at the dry site. He interpreted his observations as “plants growing in dry places are adapted to a more economical consumption of water”. He held this to be true for among- and within-species variation.

A milestone in forest “physiological ecology” was Bates’ ( 1923 ) study of the physiological requirements of Rocky Mountain trees. Bates wrote that for foresters, knowledge of demands of tree seedlings for moisture, light, heat and soil fertility was important for planning reforestation. He started a large investigation of six forest tree species, combining field studies to describe ecosystems, with experiments in controlled environments in order to determine species differences in relative transpiration and other water flow-related traits. Bates concluded from the comparison among species that trees of low water requirement would be trees that have a superior control over their water supply. He was however critical of a direct relationship between water requirement and drought resistance in trees. Moore ( 1924 ) commented that in correlating physiological measurements with the habitat characterisation of the species, Bates “... has opened new fields to forest investigations”. He also stressed that the results were counterintuitive in that the most xerophytic species had the highest water requirement, whereas the most mesophytic species had the lowest water requirement.

A similar discrepancy was observed by Maximov ( 1929 ) in the chapter “Efficiency of transpiration” in his book The Plant in relation to water , which was translated from Russian into English rapidly after its publication. Maximov preferred “efficiency of transpiration” to “water requirement”, arguing that the former would be more logically correct, because the determining process (transpiration) should be in the denominator, which also would have the effect that “… an increase in the figure denoting the value of the ratio actually corresponds to an increase of the efficiency per unit of water used”.

In his book, Maximov ( 1929 ) described experiments done at Tiflis Botanic garden (today in Georgia) by Maximov and Alexandrov ( 1917 ), where they studied local xerophytes for 3 years. They found xerophytes with a high efficiency of transpiration, particularly drought-resistant annuals. They also found that plants with a low efficiency of transpiration appeared to be the most typical semi-arid xerophytes. The mesophytes all displayed a medium efficiency. Maximov noted from other observations on the same plants that the “… majority of xerophytes with a low efficiency of water expenditure possess very extensive root systems, far exceeding in length the sub-aerial portions of the plant”. He also observed that these plants showed a strong transpiration and that this transpiration might constitute the “pump” which could draw water through such an extensive root system. He also observed that “members of the group of annual xerophytes with a high efficiency of transpiration are characterised by a relatively large leaf surface, which develops very rapidly”. He argued that this would confer a high intensity of assimilation. From these observations, he concluded a “lack of direct proportionality between efficiency of transpiration and the degree of drought resistance”, but also that “the magnitude of the efficiency of transpiration affords one of the most satisfactory tests of the ecological status of a plant”. Maximov applied the ecological classification developed by Kearney and Shantz ( 1911 ), which they had based on plants of the arid and semi-arid regions of North America: (1) drought-escaping with an annual growth cycle restricted to favourable conditions; (2) drought-evading, delay by various means the exhaustion of soil moisture; (3) drought-enduring, can wilt or dry but remains alive; and (4) drought-resisting, can store a water supply. It should be noted that the ecological definitions behind these concepts have changed with time and are used slightly differently today. Shantz ( 1927 ) argued that many of the drought-evading plants had a low water requirement and Maximov noted that this group included the highly efficient xerophytes with a large leaf area. Maximov also observed that xerophytes from the third group (drought-enduring) could show a very low efficiency of transpiration and belonged to the group of xerophytes with large root systems. Without concluding directly, he suggested a relationship between the transpiration efficiency of a xerophyte and its ecological strategy when facing limited soil water content. These studies by Maximov are among the most complete concerning the relationship between a plants’ resistance to drought and their transpiration efficiency, reflecting the interest of scientists in ecological questions of plant functioning, especially in relation to drought.

Although work on crop plants advanced greatly in the early twentieth century, results were scarcer for tree species. Raber ( 1937 ) concluded his book on “Water utilization by trees, with special reference to the economic forest species of the north temperate zone” with detailed discussions of available data for forest trees. He commented that “much more work on the water requirements of trees of all ages and under varying site conditions is needed”. And he continued that “In view of the importance of planting drought-resistant species in regions where the water supply is below the optimum for most tree species, it is extremely urgent to know more about what qualities make for drought resistance and what species possess these qualities to the greater degree.” These conclusions by Raber show that from the beginning of the twentieth century, the estimation of transpiration efficiency had taken an important place in ecological studies on forest tree species, however not without some critical thoughts on the subject.

7 What is the functional importance of transpiration?

Already in the 1870s and 1880s, the role of stomata in the diffusion of carbon dioxide into the leaf (during the day) and out of the leaf (during the night) was discussed in the scientific literature, as shown by the extensive literature review by Blackman ( 1895 ) (see also section 4 above). Especially the functional importance of transpiration was an open question. There were two opposing lines of thought. As summarised by Iljin ( 1916 ), one defended the line of inquiry that transpiration was important only in the process of transporting mineral salts from roots to leaves; the other held that the opening of stomata was necessary for absorbing the carbonic acid from the atmosphere, which leads to a loss of water and is described as an “inevitable evil”. Iljin ( 1916 ) preferred the second line of investigation and attributed a major role to the stomatal aperture, which controlled both the absorption of carbonic acid from the atmosphere and the loss of water. He concluded that in “physiologico-ecological” investigations, assimilation should be studied together with transpiration. Maskell published a series of papers in 1928, where especially “XVIII.—The relation between stomatal opening and assimilation.” (Maskell and Blackman 1928 ) used an apparatus to estimate apparent CO 2 assimilation and transpiration rate in parallel (Fig. 3 ), and was therefore able to study in detail their interdependence, developing the first mathematical descriptions, based on the development of the theories about the diffusion of gases (Brown and Escombe 1900 ). Methodological advances intensified research on the leaf-level relationship between assimilation and transpiration and allowed the study of plant functioning in more detail. The major step forward was the construction of an infrared gas analyser (URAS: in German “Ultrarotabsorptionsschreiber”, IRGA, infrared gas analyser) by Lehrer and Luft in 1938 (Luft 1943 ) at a laboratory of BASF, IG Farbenindustrie. Normally used in industry and mining, Egle and Ernst ( 1949 ) may have been the first to describe the use of the URAS for plant physiological measurements. By 1959, the URAS was routinely used for measuring stomatal resistance or transpiration in parallel and simultaneously with CO 2 assimilation, on the same leaf (Rüsch 1959 ). This was a great improvement on previous methods and led rapidly to a set of equations for calculating assimilation and stomatal conductance (Gaastra 1959 ).

Two figures taken from Maskell and Blackman ( 1928 ): on the top, Figure 1 (p. 489) showing a “Combined assimilation chamber and porometer for simultaneous investigation of apparent assimilation and stomatal behaviour. A. Section of leaf chamber passing through porometer chamber. B. Back view of leaf chamber showing also air-flow meter attached by pressure tubing to porometer and to leaf chamber”. On the bottom, Figure 5 (p. 497) “Relation between porometer rate and apparent assimilation at ‘high’ light, December 1920.” Exp t LI and LII correspond to 2 days of continuous measurements to what Maskell called “diurnal march”

Scarth ( 1927 ) argued that there would be little advantage for a plant to have a high rate of transpiration, but stressed the “... advantage of maintaining the fullest diffusive capacity of the stomata and the highest possible pressure of CO 2 in the intercellular spaces”. He concluded that the principal function of stomata “... is to regulate that very factor which is presumed to regulate them, viz. the concentration of CO 2 in the leaf or, respectively, in the guard cells”. Maskell and Blackman ( 1928 ) tested this hypothesis experimentally and concluded that the rate of uptake of carbon dioxide was determined by variations in stomatal resistance and by resistances within the leaf, thereby introducing the importance of the CO 2 concentrations in the chloroplasts. The suggestion of a strong link between the leaf internal carbon dioxide concentration and leaf-level WUE represented a large advance in the theoretical understanding of WUE.

Penman and Schofield ( 1951 ) proposed, perhaps, the first theoretical link between the leaf-level transpiration ratio (leaf transpiration divided by assimilation) and the ratio of the coefficients of diffusion of water vapour and carbon dioxide in air, and the water vapour and carbon dioxide air-to-leaf pressure gradients. Gaastra ( 1959 ) suggested that the leaf internal conductance to carbon dioxide is a pivotal point of the ratio of assimilation to transpiration and of the water economy of crop plants. Bierhuizen and Slatyer ( 1965 ) showed that the transpiration ratio could be predicted from water vapour and carbon dioxide gradients over a range of light intensities, temperatures and relative humidities. These studies were the first to suggest that whole plant transpiration efficiency might be regulated directly by leaf functioning and would be therefore a trait in its own right and not only the ratio of two plant traits.

8 How can the transpiration ratio be improved?

Because water is increasingly scarce in a warming world, Rüsch ( 1959 ) queried whether the luxury of highly transpiring tree species could be justified. He argued for selective breeding of tree species varieties with low transpiration-to-assimilation ratio T/A by means of minimising transpiration while maximising assimilation. Also Polster et al. ( 1960 ) assessed the potential suitability of tree species to sites by their dry matter production and transpiration ratio. Troughton ( 1969 ) and Cowan and Troughton ( 1971 ) suggested that genetic selection of plant varieties could be used to improve the transpiration ratio by decreasing leaf internal resistance to carbon dioxide diffusion. Cowan and Farquhar ( 1977 ) built on this theme by proposing that stomata might optimise carbon gain to water lost by varying the conductances to diffusion and thereby maximising the ratio of the mean assimilation rate to mean rate of evaporation in a fluctuating environment. Approaches which target photosynthesis, stomatal opening, leaf internal resistance to carbon dioxide diffusion or stomatal optimisation in order to improve plants performance have since been followed in plant breeding and have largely been reviewed elsewhere (e.g. Condon et al. 2004 ; Cregg 2004 ; Vadez et al. 2014 ).

9 Intrinsic water use efficiency and carbon stable isotopes

Another milestone towards contemporary research on water use efficiency was the use of stomatal conductance to water vapour rather than transpiration by Farquhar and Rashke ( 1978 ) and to calculate water use efficiency as assimilation divided by stomatal conductance. This definition allowed an estimation of water use efficiency resulting only from plant functioning, without a direct impact from leaf-to-air vapour pressure difference and was named by Meinzer et al. ( 1991 ) “intrinsic water use efficiency” (W i ). Knowledge of W i facilitated the search for a genetic basis of within species variation, e.g. Brendel et al. ( 2002 ), Condon et al. ( 2002 ) and Chen et al. ( 2011 ).

Development of the stable carbon isotope method for estimating W i resulted in a widely applicable screening method, and a large increase of publications around plant water use efficiency. Based on the two-step fractionation model (atmospheric CO 2 – leaf internal CO 2 – plant carbon) proposed by Park and Epstein ( 1960 ), various models explaining the difference in carbon isotope composition between atmospheric CO 2 and plant carbon were developed in the late 1970s and early 1980s, e.g. Grinsted ( 1977 ), Schmidt and Winkler ( 1979 ) and Vogel ( 1980 ). Vogel’s model contained many theoretical aspects which, however, lacked experimental understanding. In parallel, Farquhar ( 1980 ) developed a similar model, but which resulted in a simple, elegant mathematical equation relating plant natural abundance carbon isotope discrimination, relative to atmosphere, to the ratio of leaf internal to atmospheric CO 2 concentration. This was, in turn, related to W i . Experimental evidence showed that carbon isotope measurements, in wheat, reflected long-term water use efficiency (Farquhar et al. 1982 ) as well as whole plant transpiration efficiency (Farquhar and Richards 1984 ). They concluded that carbon isotope discrimination may provide an effective means to assess and improve WUE of water-limited crops. Strong correlations between whole plant TE and stable carbon isotope measurements of plant organic material were shown in a host of papers to be. Some of these papers were (1) for crops and other annuals (Hubick et al. 1986 ; Ehleringer et al. 1990 ; Virgona et al. 1990 ) and (2) for trees (Zhang and Marshall 1994 ; Picon et al. 1996 ; Roupsard et al. 1998 ). The isotopic method has spread rapidly as a general estimator of WUE and continues to be used widely in screening programmes for plant improvement as well as in ecological research, e.g. Rundel et al. ( 1989 ) and notably used in tree rings (McCarroll and Loader 2004 ).

10 Closing remarks

Water use efficiency is probably one of the oldest of plant traits to stimulate across the centuries the interest of philosophers, theologians, Middle Age savants, natural philosophers and modern plant scientists across different disciplines (plant physiology, ecophysiology, ecology, genetics, agronomy). The interest began as a purely philosophical one, progressed to thought experiments, towards an interest in plant functioning and its relationship to the environment.

Already in the early Renaissance (mid-fifteenth century), an experimentation was proposed, in a time when botany consisted mainly of naming plants (Morton 1981 ). It is then also an early example of an actually performed experimentation, the famous willow experiment by Van Helmont ( 1662 ) as well as of early “in laboratory” experimentation on plants (hydroponics experiments by Woodward 1699 ). The question of what makes plants grow, between water and soil, kept natural philosophers busy up to the end of the eighteenth century, when the assimilation of CO 2 was discovered and the question finally solved.

Early in the nineteenth century, the interest and experimentation turned to the amount of water that plants would need to grow, in the context of a developing research on agricultural practices (Morton 1981 ). Biomass was used to standardise the water losses which allowed comparisons among species (crops as well as trees) and a beginning study of the impact of different environmental variables.

At the end of the nineteenth century, knowledge on the physiological aspects of CO 2 assimilation and the control of transpiration by stomata had sufficiently advanced, so that scientists started to reflect on their inter-dependency. Was transpiration only a physical process or was there a physiological control? Was transpiration regulated by the dry matter production? Or does the stomatal opening determine the rate of CO 2 assimilation?

At the turn of the twentieth century, the study of species differences led to questioning why these differences did exist. As the discipline of “physiological ecology” developed, “water requirement” was inverted into an “efficiency”, reflecting an evolution from standardising transpiration to a trait in its own right. This introduced ecological questions about the adaptation of plants to dry environments and the relation to transpiration efficiency. Counterintuitive results stimulated the discussion and linked differences in WUE to different ecological strategies.

Methodological and theoretical advances in the description of leaf gas exchange in the mid-twentieth century showed the central role of stomata in the control of transpiration and CO 2 assimilation, leading to the idea that stomata might optimise water losses versus carbon gain. The development of carbon stable isotopes as an estimator of leaf-level WUE was an important step not only to further develop these theoretical considerations, but also towards large-scale studies. In parallel, modelling approaches were developed to scale from leaf-level WUE to whole plant TE, e.g. Cernusak et al. ( 2007 ), and to the field or canopy, e.g. Tanner and Sinclair ( 1983 ).

At least from the beginning of the twentieth century onwards, also critical views on the relationship between water requirement and its relation to growth mostly in terms of yield were published (Dachnowski 1914 ). Viets ( 1962 ) asked “Is maximum water use efficiency desirable?”, especially in terms of crop production. Sinclair et al. ( 1984 ) considered different options for improving water use efficiency, however concluding that most of these have important limitations or drawbacks. This discussion is ongoing, as can be seen by the article published by Blum ( 2009 ): “Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress”.

Exploration and application of transpiration efficiency at the whole plant level, and its derivatives at other levels, are still a very active research field across nearly all levels of forest science: concerning very rapid processes at the leaf level (Vialet-Chabrand et al. 2016 ), up-to-date genetic and genomic approaches for breeding (Plomion et al. 2016 ; De La Torre et al. 2019 ; Vivas et al. 2019 ), studying local adaptation of trees to their environment in a population genetic context (Eckert et al. 2015 ) or an ecological context (Pellizzari et al. 2016 ), water use efficiency from the plant to the ecosystem (Medlyn et al. 2017 ), estimated at the population level (Rötzer et al. 2013 ; Dekker et al. 2016 ) or modelling up to the global earth level (Cernusak et al. 2019 ), just to name a few. Thus, the first curiosity of Greek philosophers has motivated scientists through history, with many exciting discoveries still to come.

Change history

17 june 2021.

A Correction to this paper has been published: https://doi.org/10.1007/s13595-021-01073-0

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Acknowledgements

Much of the historical background is based on A.G. Morton’s “History of Botanical Sciences” as well as to Frank N. Egerton’s “A History of the Ecological Sciences” series in the “Bulletin of the Ecological Society of America”. The author is also largely indebted to C. Schuchardt from the Library of the Staatsbetrieb Sachsenforst for help with the quest for rare German publications. The author would also like to thank E. Dreyer and J.M. Guehl (both from the SILVA Unit at INRAE Nancy, France) who commented extensively on an earlier version of the draft and J. Williams (University of Sussex), L. Handley and J. Raven (University of Dundee) who made many valuable suggestions and improved language.

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Brendel, O. The relationship between plant growth and water consumption: a history from the classical four elements to modern stable isotopes. Annals of Forest Science 78 , 47 (2021). https://doi.org/10.1007/s13595-021-01063-2

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• Science Fair Project Ideas for Kids, Middle & High School Students ⋅

Science Fair Project for Testing Different Soils With Plant Growth

Science Fair Projects About Growing Beans and the Life Cycle

Science fair projects use a student’s creativity to teach scientific methods. While the possible projects are almost limitless, a straightforward project, such as testing soil types’ impact on plant growth, will provide clear, observable results for the student to study.

Choosing Your Soils

Soils are a mixture of three particles – sand, silt and clay – along with organic matter, water and air. Soil differences are the result of different mixtures of these particles. Since this project will test the growth in different soil types, choose at least two soils with distinct differences. For example, choose one soil with high sand content and another with high clay content. This will make the differences between the soils easier to spot.

The Experiment

Fill paper cups or other growing vessels with the soils, marking each vessel to keep the soil types separate. Plant the same type of seeds in each vessel. The seeds should be planted at the same depth and spaced the same in each vessel. Follow the seed packet’s instructions to properly water and care for the plants, ensuring you treat each vessel in the same way so that plant differences are not the result of differences in your care of each vessel.

What to Measure

The purpose of this experiment is to observe differences between the plants as they grow in different soils. This can be measured in any way appropriate for your plant. For example, you could measure the height, width, number of leaves, how fast the plants grow, number of flowers or yield of seeds or fruits. The results of your experiment will be clearer if you use objective measurements, rather than subjective descriptions of the plants.

Presenting Your Results

Science fairs may prescribe a required format for presentation of your results. For example, you may be required to present data tables or graphs to support your conclusion instead of simply reporting which soil type was best for your plants. Therefore, it is important for you to keep accurate records throughout your experiment for later inclusion in your presentation. Graphs, descriptions of your methods and pictures will help the judges and teachers understand your experiment and the level of work you put into your project.

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• University of Illinois Extension: Natural Resources, the Environment and Ecosystems: Soil and Ecosystems
• United States Department of Agriculture: Agricultural Ideas for Science Fair Projects: Environmental Sciences
• Selah School District: The Effect of Different Potting Soil Mixtures on Radish Growth

About the Author

Heather Frances has been writing professionally since 2005. Her work has been published in law reviews, local newspapers and online. Frances holds a Bachelor of Arts in social studies education from the University of Wyoming and a Juris Doctor from Baylor University Law School.

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Creatas Images/Creatas/Getty Images

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How does PH level affect the plant growth?

Introduction: (initial observation).

It is well known that chemical properties of soil has a major effect in the plant growth. Now we can optimize the plant’s growth by adding specific fertilizers and minerals. Another property of soil that we can control and may have some affects on plants growth is the PH of soil. PH shows acidity or alkalinity level of soil. As a mater of fact we may even explore that different plants require different soil PH for their best growth. In the United States, soil pH ranges from four to ten”‘. Each species of plant has different needs, different plants may prefer different pH levels. This information promoted the investigation of the question, “What is soil pH and what effect does it have on plant growth?”. In this project we will study the effect of pH in plant growth.

In this project you will study the soil PH, not the PH of water used to irrigate the plant. Acidic water such as acid rain have different effects. I have included the information about acid rain project at the end of this project for your reference.

This project guide contains information that you need in order to start your project. If you have any questions or need more support about this project, click on the “Ask Question” button on the top of this page to send me a message.

If you are new in doing science project, click on “How to Start” in the main page. There you will find helpful links that describe different types of science projects, scientific method, variables, hypothesis, graph, abstract and all other general basics that you need to know.

Project advisor

Information Gathering:

Before any experimentation, it was necessary to obtain more information on soil pH. This was accomplished with a pilgrimage to the local library and an excursion in the net. It was found that soil pH (potential of hydrogen), which measures the amounts of hydrogen and hydroxyl ions, is not important to the plant in itself but is important because it influences the supply of dissolved nutrients that plants need to absorb from the soil. The availability of different nutrients changes at different pH levels. Soil pH can also affect the growth of certain fungi and bacteria which, in turn, affect plant growth. Soil pH can be modified very easily. It can be raised by adding an alkaline solution (lime) and lowered by adding an acidic solution (acetic acid, sulfur). Following are samples of collected information.

Soil pH and Landscape Plants

Soil pH is the most commonly-used index of plant root-zone acidity or alkalinity. Soil pH is important to plants because (1) it influences the chemical form of many elements in the soil, and (2) it influences soil microbial processes. Some elements influenced by pH are essential nutrients for plants, so soil pH affects plant nutrition. Other elements are toxic when present in excessive amounts, and soil pH helps to determine how much is in solution at any one time.

While keeping in mind the importance of soil pH, it should be noted that concern about its impact in typical residential or commercial landscape situations is often exaggerated. The purpose of this publication is to help you put landscape soil pH into proper perspective and help you manage soil pH for better plant performance.

What Is the “Desirable pH Range” for My Plants?

There are plenty of charts and tables around that list the “desirable pH range” for just about any plant you might wish to grow. Sometimes the term “optimum pH range” is used. While these guides are helpful in a general sense, they present problems in many Florida situations. First, the ranges given are usually narrow. Landscape plants are more tolerant of pH than is implied in the “desirable pH range” commonly given. Second, “desirable pH ranges” are generally biased toward fine-textured mineral soils such as silt loams and clays. Because fine-textured soils have greater quantities of aluminum (Al) and micronutrients than coarse-textured soils, they have greater potential for Al toxicity and less potential for micronutrient deficiencies. There are far more coarse-textured soils (sands) in Florida than there are fine-textured ones.

See Table 1 for a listing of landscape plants which have documented pH tolerances or sensitivities. If the species you are interested in is not listed, you can probably presume that the species will do fine in Florida soils with pH in the 5.0 to 6.5 range.

Consider correcting soil pH only when it is appreciably higher or lower than the ideal for the kind of plants you are growing. You can determine this by having your soil tested by a responsible lab. If your soil pH is within 0.4 of a pH unit of the ideal range, adjusting the pH will probably not improve plant performance (Figure 1).

How Do I Raise My Soil pH?

First, have a lime requirement test run on your soil. Such a test measures your soil’s buffering capacity and tells how much lime you need to apply. Next, apply the prescribed amount of agricultural limestone. Always test before liming. Don’t just assume that lime is needed. Many soils already contain excess lime. Such soils will typically have pHs between 7.0 and 8.2.

How Do I Lower My Soil pH If It’s Too High for the Plants I Want to Grow?

When soil pH is high because of naturally- occurring lime (like limestone, marl, or sea shells), there is no practical way of permanently lowering the soil pH. There simply is too much lime present to neutralize. The same is often true near new masonry buildings where excessive waste concrete and mortar fell on the soil during construction. Under those circumstances, you should select plants which are tolerant of high pH conditions to avoid continuing plant nutritional problems.

Can’t I do Anything to Help My Acid-Loving Plants Grow in High-pH soil?

Sure, but be prepared for a never-ending, up-hill battle. Elemental sulfur added to soil will result in a lower soil pH. That’s because soil bacteria transform elemental sulfur to sulfuric acid. The acid in turn neutralizes any alkalinity with which it comes in contact. However, as soon as the sulfur is “used up”, soil pH will return to its original value. The cycle of pH dropping and then rising again to its original high level can be as short as a couple of weeks, depending on the rate and method of sulfur application. If you try to get around this cycle by putting on high rates of sulfur, or if you make the applications too frequently, you run the risk of damaging your plants.

Never apply more than 5 to 10 pounds of sulfur per 1,000 square feet per application. For specimen trees or shrubs, it is sometimes successful to acidify only a small zone of soil near the dripline of the plant. To do this, dig a small hole about a foot deep and 8 to 10 inches in diameter, mix 2 to 3 tablespoons of sulfur into the soil taken from the hole, and return the amended soil to the hole. Repeated annually, that volume of acidified soil is frequently sufficient to prevent micronutrient deficiencies commonly associated with high soil pH. Observe your plants’ performance carefully if you embark on any program of sulfur additions.

Please note that sulfate sulfur does not affect soil pH. There is much misunderstanding on this point because some sulfate compounds (e.g., ammonium sulfate, aluminum sulfate, iron sulfate) have soil- acidifying properties. However, there are many other sulfate compounds which do not acidify soil. Examples include calcium sulfate (gypsum), magnesium sulfate (Epsom salt), and potassium sulfate.

Heavy applications (e.g., 200 pounds per 100 square feet) of organic matter such as manure, composted leaves, and peat help some landscape plants overcome the adverse effects of alkaline soil pH. Since these materials decompose with time, annual or semi-annual applications are usually required.

Table 1. Landscape plants of notable soil pH preference and tolerance.

Soil pH is referred to as the “acidity” of the soil and is measured by the number of Hydrogen ions present in the soil solution.

When the soil pH is too “acid” (low pH) or “alkaline” (high pH), nutrients present in the soil become locked-up or unavailable. Correcting the pH has the same effect as applying fertilizer since it “unlocks” plant nutrients already present.

Most plants grow best within a pH of 6.5 to 7.2 (7 is neutral).

Question/ Purpose:

The purpose of this project is to see how does the pH level of soil affects the plant growth.

Identify Variables:

Our independent variable is soil PH. Dependent variable is the plant growth.

Hypothesis:

My hypothesis is that slightly acidic soil for example PH 5 must result the best plant growth. My hypothesis is based on my gathered information that more minerals will be water soluble in this pH and micro organisms will grow best. Micro organisms can decompose organic maters to simplest form useable by plants.

Experiment Design:

(You can modify this experiment and use other seeds or different number of test samples) This experiment is designed to test the effect of pH on plant growth. The results of this experiment may provide useful information on growing plants. When soil pH levels at which a plant grows best are determined, plants can be grown much more effectively and efficiently.

Materials and Method: In this experiment, sixty Kentucky Wonder bean seeds are planted in starter cups. They are arranged in six rows and ten columns. Each cup is labeled with a letter for its column and a number for its row (use PH as the row number). Each cup is filled with one forth cup of soil. A bean seed is planted in each.

Prepare 6 empty/ clean 2 liter soda bottles and fill them up with water. Keep one as a control and just test and record it’s PH. To other bottles add material that can increase or decrease PH and make bottles with PH of 5, 6, 7, 8 and 9. You can increase the PH by adding hydrated lime or ammonia. You can also decrease PH by adding acetic acid or sublimed sulfur.

Rows one through five are watered with solutions that produce soil pH 5 through pH 9 respectively. Row six is left as a control. It is watered with water only. The plants are watered with one eighth cup of solution or water every day.

Continue watering until all bottles are empty. This experiment will take two to 4 weeks to complete. Record the final results in a table like this:

Results table: Plant heights on final day.

Then calculate the average plant height in each row (each PH) and record the results in a table like this:

Average final plant heights:

Materials and Equipment:

Can be extracted from the experiment.

You will need an electronic pH meter or pH indicator papers to test and adjust pH.

Results of Experiment (Observation):

In addition to the completed tables from previous section, write which pH created the tallest plant? In which pH plant did not grow at all?

Calculations:

You will need to calculate the average of height for plants in each row.

Summary of Results:

(This is only a sample, don’t count on it!, do your own experiment) In this experiment, the effects of soil pH on the growth and properties of Kentucky Wonder plants, a species of pole bean was investigated. Sixty Kentucky Wonder seeds were planted in sterilized starting mix. They were watered, (ten each), with solutions with pH of five, six, seven, eight, nine, and plain water for a control. It was observed that as plants were watered with solutions that produced increasingly higher pH levels, they grew taller in the same amount of time. None, however, growing as much as the control, watered with plain water to produce a soil pH level of 6. 11. It was concluded that the variety of bean plant tested, Kentucky Wonder, grows best in soil with a pH level around six.

Conclusion:

Using the trends in your experimental data and your experimental observations, try to answer your original questions. Is your hypothesis correct? Now is the time to pull together what happened, and assess the experiments you did.

Related Questions & Answers:

What you have learned may allow you to answer other questions. Many questions are related. Several new questions may have occurred to you while doing experiments. You may now be able to understand or verify things that you discovered when gathering information for the project. Questions lead to more questions, which lead to additional hypothesis that need to be tested.

Possible Errors:

In our experiment we used water with certain pH and assumed that soil will not modify this PH. So we used the pH of water as the pH of soil. To be more accurate we need to make soil with certain PH before starting our experiment. We also need to monitor and readjust the soil PH during our experiment. This is a potential error and we may get different results if we use soil with adjusted PH.

References:

Visit your local library and see some books about plants and conditions for plan growth. Use them as your references. Search the internet for keywords such “plants”, “PH”, “Growth” to find more information.

Following are some web based articles about the soil pH:

• Soil pH: What it means?
• More about soil pH
• Soil pH modification
• Soil pH and landscape plants

Why are acids low numbers on the pH scale if pH means potential of hydrogen and acids have more hydrogen ions than bases?

Since the concentration of Hydrogen ion (H+) is usually a very small number such as 0.00001, we usually write it as power of 10. For example 0.00001=10-5. The number -5 here is also known as the logarithm or log of 0.00001. The definition of pH is: pH = – Log (H+) = -Log (0.00001) = – (-5) = 5

In other words the pH is 5 when the concentration of H+ is 0.00001.

In other words if the concentration of (H+) is 1/1000000, then the pH is 6 and if the concentration of (H+) is 1/100 then the pH is 2. As you see 1/100 (or 0.01) is much larger value than 1/1000000 (or 0.000001). So less pH means more hydrogen ion.

Rain and wet weather don’t always mean good news for plants, especially in an area hit by acid rain. Acid rain is caused by the burning of fuels such as oil and coal. This burning releases sulfur dioxide and nitrogen oxide gases, which react with ozone in the atmosphere to form two destructive substances: sulfuric acid and nitric acid. Rain then washes these acids out of the atmosphere and down onto Earth, harming forests and lakes.

How exactly does acid rain affect plant growth? Be your own weather-person and find out! (A note of caution: Don’t try this at home! Do this experiment in school under the supervision of a science teacher.)

Material and Equipment:

• seeds (bean seeds work well)
• two plastic pots
• potting soil
• light source
• plastic wrap
• distilled water
• 2-liter plastic soda bottle
• medicine dropper
• nitric or sulfuric acid (ask your science teacher to help you get this)
• pH paper (again, ask your science teacher to help you get this)
• two spray bottles
• Plant the seeds in pots with moist potting soil, water them and place them in bright light.
• When the bean seedlings have emerged with their first pair of full-grown leaves, label one plant container “acid” and the other “control.”
• Use a separate piece of plastic wrap to cover each half of the soil surface in each pot. The stem should poke through between the two pieces of plastic.
• Pour one liter of distilled water into a clean 2-liter soda bottle.
• Ask your science teacher to help you add a drop of nitric acid or sulfuric acid to the distilled water. Swirl the water in the bottle to mix.
• Test the water pH using pH paper. If the pH is above 3, add more acid. If it is below 3, add more distilled water. Test the water pH until it is about 3. Then pour it into a spray bottle.
• Place the control plant into a sink and mist the leaves with a spray bottle full of pure distilled water. Let the leaves dry, then bring the control plant back to its growing location.
• Place the acid plant in a sink, and mist the leaves with the spray bottle filled with pH 3 solution. Let the leaves dry, then bring the acid plant back to its growing location.
• Observe any differences in growth and leaf color between the acid and control plant.

Experiment Results: The plant sprayed with the pH3 solution will be badly damaged. Its leaves will turn brown or yellow.

Conclusion: Rain is normally somewhat acidic because carbon dioxide gas will dissolve in it to make carbonic acid. As a result, normal rainwater has a pH of 5.6. Fossil fuels like coal or gasoline change the pH, however. When these fuels are burned, they release sulfur dioxide and nitrogen oxide gases into the air. These gases react with sunlight, ozone, and water vapor to form nitric and sulfuric acids. Rain that is tainted by these acids has a pH that is much less than 5.6. When the pH is below 5.6, it is called acid rain, and this low pH can harm plants.

For a further investigation, find out if you have acid rain in your area. Place plastic containers outside to collect rainwater, then measure the pH of this water with your pH paper.

It is always important for students, parents and teachers to know a good source for science related equipment and supplies they need for their science activities. Please note that many online stores for science supplies are managed by MiniScience.

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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)

Science Project Ideas

Does Music Affect Plant Growth

Though it is still a debatable topic, experiments conducted all over the world indicate that music can affect plant growth.  While soothing classical music, Beethoven, Brahms have been seen to help in stimulating growth, certain other music hindered their growth rate. Here is an experiment that can help you in the research and arrive at a conclusion.

How Does Music Affect Plant Growth: An Experiment

The pot having mustard seeds exposed to music germinates and grows faster than those without music.

• Packet of radish seeds
• 2 plastic pots
• Classical music CD
• 1-meter ruler
• The 2 pots are filled with the same amount of soil and labeled A and B.
• Maintaining a distance of 20 mm in between them, 10 radish seeds are placed in the soil of each pot.
• The pots are kept in such a place that they receive the same amount of sunlight every day.
• They are also watered equally twice every day.
• Pot A is placed beside the CD player playing classical music for 3 hours every day.
• Pot B is kept away from any source of sound.
• Their height is recorded every day for 15 days and tabulated.

It is seen that the plants under the effect of music record a greater increase in average height than the ones placed away from music. The relation between music and plant growth be studied better by plotting the no. of days as the independent variable on a graph paper and the average plant height as the dependent variable. You should have 2 different graphs for the data pertaining to plants growing with and without music on the same graph paper for a good comparative study. In fact, the absence of music does nothing to the normal growth rate.

You Can Also Try

• Check out the influence of rap, rock and heavy metal music on the growing plants.

Music Affecting Plant Growth Video

Possible explanation.

Music has been observed to improve the germination process and enhance growth in plants albeit without a proper scientific explanation. Plants, as such cannot hear sound, but they can feel the vibration of the sound waves in air. The living matter within plants, protoplasm, is in a state of perpetual motion. The sound vibrations add to it, speeding up the transfer of nutrients and resulting in faster growth. However, loud music like rock can be detrimental for development as they increase the vibrations to intolerable levels.

Get all the requisite background information before demonstrating the scope of music for an accelerated growth of plants at science fairs. Serve an eco-friendly purpose by using music therapy to promote healthy greenery in nurseries, gardens, etc.

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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?

23 Plant Experiment Ideas

ThoughtCo / Hilary Allison

• Cell Biology
• Weather & Climate
• B.A., Biology, Emory University
• A.S., Nursing, Chattahoochee Technical College

Plants are tremendously crucial to life on Earth. They are the foundation of food chains in almost every ecosystem. Plants also play a significant role in the environment by influencing climate and producing life-giving oxygen.

Plant experiments and studies allow us to learn about plant biology and its potential usage for plants in other fields such as medicine , agriculture , and biotechnology . The following plant experiment ideas provide suggestions for topics to be explored.

Plant Experiment Ideas

• Do magnetic fields affect plant growth?
• Do different colors of light affect the direction of plant growth?
• Do sounds (music, noise, etc.) affect plant growth?
• Do different colors of light affect the rate of photosynthesis ?
• What are the effects of acid rain on plant growth?
• Do household detergents affect plant growth?
• Can plants conduct electricity ?
• Does cigarette smoke affect plant growth?
• Does soil temperature affect root growth?
• Does caffeine affect plant growth?
• Does water salinity affect plant growth?
• Does artificial gravity affect seed germination?
• Does freezing affect seed germination?
• Does burned soil affect seed germination?
• Does seed size affect plant height?
• Does fruit size affect the number of seeds in the fruit?
• Do vitamins or fertilizers promote plant growth?
• Do fertilizers extend plant life during a drought ?
• Does leaf size affect plant transpiration rates?
• Can plant spices inhibit bacterial growth ?
• Do different types of artificial light affect plant growth?
• Does soil pH affect plant growth?
• Do carnivorous plants prefer certain insects?
• Guide to the 6 Kingdoms of Life
• Phases of the Bacterial Growth Curve
• Gram Positive vs. Gram Negative Bacteria
• Animal Studies and School Project Ideas
• Angiosperms
• 10 Facts About Pollen
• Nematoda: Roundworms
• Is Spontaneous Generation Real?
• Parts of a Flowering Plant
• 5 Tricks Plants Use to Lure Pollinators
• Carnivorous Plants
• Mutualism: Symbiotic Relationships
• The Photosynthesis Formula: Turning Sunlight into Energy
• All About Photosynthetic Organisms
• Protista Kingdom of Life
• Common Animal Questions and Answers

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Science project, how light affects plant growth.

Purpose : The purpose of this project is to show that different colors of light affect the development of plants.

Hypothesis : I predict that plants will grow better under blue, red and yellow lights than they will under white and green lights.

Background : The relationship between light and plant growth can be demonstrated by exposing leaves to various colors of light. Light supplies the power to carry on photosynthesis, the food-making process in leaves. But the spectrum of light most utilized by a leaf is limited to three distinct colors, red, blue and yellow. For example, leaves appear green because green is the color most leaves reflect rather than absorb and use.

Independent Variable : Color of light

Dependent Variable : Plant height

Control Variables : Same size soybean plants, fertilizer, soil, water, potting soil, colored filters, 10 gallon aquarium tank.

Procedures : Plant four soybean plants of the same size in an aquarium containing 5" of well moistened potting soil. Apply the recommended dosage of fertilizer. Place a colored filter tent over each plant. One filter should be clear. Use blue, yellow, and red film for the other filters. Place the aquarium in direct sunlight. Keep in the same location during the experiment and water daily. Measure each plant every day and record your findings in a notebook. Be sure to measure from the bottom of the aquarium and not the surface of the potting soil.

Materials : All the materials for this project are available locally. You can obtain a 10 gallon aquarium from a pet shop. Office stores sell colored transparency sheets. Most garden supply shops sell soybean seeds, potting soil and plant feretilizer. Be sure to germinate your soybean plants to a height of 4" before beginning your experiment.

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August 29, 2022

How light and temperature work together to affect plant growth

The findings may help scientists develop more resilient plants to help withstand climate change

Home - Salk News - How light and temperature work together to affect plant growth

LA JOLLA—Plants lengthen and bend to secure access to sunlight. Despite observing this phenomenon for centuries, scientists do not fully understand it. Now, Salk scientists have discovered that two plant factors—the protein PIF7 and the growth hormone auxin—are the triggers that accelerate growth when plants are shaded by canopy and exposed to warm temperatures at the same time.

The findings, published in Nature Communications on August 29, 2022, will help scientists predict how plants will respond to climate change—and increase crop productivity despite the yield-harming global temperature rise.

“Right now, we grow crops in certain densities, but our findings indicate that we will need to lower these densities to optimize growth as our climate changes,” says senior author Professor Joanne Chory , director of Salk’s Plant Molecular and Cellular Biology Laboratory and Howard Hughes Medical Institute investigator. “Understanding the molecular basis of how plants respond to light and temperature will allow us to fine-tune crop density in a specific way that leads to the best yields.”

During sprouting, seedlings rapidly elongate their stems to break through the covering soil to capture sunlight as fast as possible. Normally, the stem slows down its growth after exposure to sunlight. But the stem can lengthen rapidly again if the plant is competing with surrounding plants for sunlight, or in response to warm temperatures to increase distance between the hot ground and the plant’s leaves. While both environmental conditions—canopy shade and warm temperatures—induce stem growth, they also reduce yield.

In this study, the scientists compared plants growing in canopy shade and warm temperatures at the same time—a condition that mimics high crop density and climate change. The scientists used the model plant Arabidopsis thaliana, as well as tomato and a close relative of tobacco, because they were interested to see if all three plant species were affected similarly by this environmental condition.

Across all three species, the team found that the plants grew extremely tall when simultaneously trying to avoid the shade created by neighboring plants and being exposed to warmer temperatures. On a molecular level, the researchers discovered that transcription factor PIF7, a protein that helps turn genes “on” and “off,” was the dominant player driving the increased rapid growth. They also found that the growth hormone auxin increased when the crops detected neighboring plants, which fostered growth in response to simultaneous warmer temperatures. This synergistic PIF7-auxin pathway allowed the plants to respond to their environments and adapt to seek the best growing conditions.

A related transcription factor, PIF4, also stimulated stem elongation during warm temperatures. However, when shade and increased temperatures were combined, this factor no longer played an important role.

“We were surprised to find that PIF4 did not play a major role because prior studies have shown the importance of this factor in related growth situations,” says first author Yogev Burko, a Salk staff researcher and assistant professor at the Agriculture Research Organization at the Volcani Institute in Israel. “The fact that PIF7 is the dominant driving force behind this plant growth was a real surprise. With this new knowledge, we hope to fine-tune this growth response in different crop plants to help them adapt to climate change.”

The researchers believe that there is another player, yet to be discovered, that is boosting the effect of PIF7 and auxin. They hope to explore this unknown factor in future studies. Burko’s lab will also be studying how this pathway can be optimized in crop plants.

“Global temperatures are increasing, so we need food crops that can thrive in these new conditions,” says Chory, who co-directs Salk’s Harnessing Plants Initiative and holds the Howard H. and Maryam R. Newman Chair in Plant Biology. “We’ve identified key factors that regulate plant growth during warm temperatures, which will help us to develop better-performing crops to feed future generations.”

Other authors included Björn Christopher Willige and Adam Seluzicki of Salk; Ondřej Novák of Palacký University and Institute of Experimental Botany at The Czech Academy of Sciences; and Karin Ljung of the Swedish University of Agricultural Sciences.

The work was funded by the National Institutes of Health (5R35GM122604-05_05), Howard Hughes Medical Institute, Knut and Alice Wallenberg Foundation (KAW 2016.0341 and KAW 2016.0352), Swedish Governmental Agency for Innovation Systems (VINNOVA 2016-00504), EMBO Fellowships (ALTF 785-2013 and ALTF 1514-2012), BARD (FI-488-13), Human Frontier Science Program (LT000222/2013-L) and Salk’s Pioneer Postdoctoral Endowment Fund.

DOI: 10.1038/s41467-022-32585-6

PUBLICATION INFORMATION

Nature Communications

PIF7 is a master regulator of thermomorphogenesis in shade

Yogev Burko, Björn Christopher Willige, Adam Seluzicki, Ondřej Novák, Karin Ljung and Joanne Chory

Plant Biology

For more information.

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JSmol Viewer

How do plant growth-promoting bacteria use plant hormones to regulate stress reactions.

1. Introduction

2. pgpb and phytohormones in the rhizosphere, 2.1. auxins, 2.2. cytokinins, 2.3. gibberellins, 2.4. salicylic acid, 2.5. abscisic acid, 2.6. volatile organic compounds, 2.7. ethylene and acc deaminase, 3. synergistic effects of pgpb on plant growth through the interaction of multiple pathways, 3.1. effect of iaa on acc deaminase and ethylene synthesis, 3.2. interactions among phytohormones, 4. strategies for assessing the ability of pgpb to synthesize phytohormones, 4.1. determination of the potential for iaa synthesis, 4.2. detection of acc deaminase activity, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Timofeeva, A.M.; Galyamova, M.R.; Sedykh, S.E. How Do Plant Growth-Promoting Bacteria Use Plant Hormones to Regulate Stress Reactions? Plants 2024 , 13 , 2371. https://doi.org/10.3390/plants13172371

Timofeeva AM, Galyamova MR, Sedykh SE. How Do Plant Growth-Promoting Bacteria Use Plant Hormones to Regulate Stress Reactions? Plants . 2024; 13(17):2371. https://doi.org/10.3390/plants13172371

Timofeeva, Anna M., Maria R. Galyamova, and Sergey E. Sedykh. 2024. "How Do Plant Growth-Promoting Bacteria Use Plant Hormones to Regulate Stress Reactions?" Plants 13, no. 17: 2371. https://doi.org/10.3390/plants13172371

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Experiments for Kids | Effecting Plant Growth

As always, I am excited to be back for another  Saturday Science . We love experiments for kids ! Science is such a staple in our house and guides the rest of our lessons for the week. This week, I thought it would be fun to share some old science fun we had before we ever started homeschooling . This experiment is one we did when Legoman was in second grade for his science fair project.

{THIS POST MAY CONTAIN AFFILIATE LINKS TO MATERIALS I RECOMMEND. ANYTHING YOU PURCHASE THROUGH THESE LINKS HELPS SUPPORT LEMON LIME ADVENTURES. THANK YOU IN ADVANCE FOR CHOOSING TO SUPPORT US.}

Since this science experiment was for science fair, we needed to follow the scientific method. If you are a regular here, you know how much we love science and how we try to teach the correct procedures and techniques involved in science explorations. This science experiment would be great for any age, with some modifications and adult help for the younger ages.

Question/ Hypothesis

Question: How do various liquids {tap water, river water, salt water, carbonated water, and soda} effect plant growth?

Hypothesis: Legoman predicted that the plant that was given the river water would grow the most.

Materials and Procedure

What we needed:

6 Plants (all the same variety, roughly the same size) (We chose to use established plants to see the effects of the liquids on the plant growth)

6 Different Liquids {We used tap water, river water, salt water, carbonated water, and soda but you could use any liquids your child wants to investigate}

Planters Ruler Measuring Cup (to ensure you are using the same amounts of liquid with each plant) Journal and pencil (for recording data)

We planted each plant in individual pots and used our handy label maker to label each pot with the liquid we would be giving it over the next two weeks. We also labeled each liquid container so that they would match the plants.

Something important about a science experiment is to teach children about constants (unchanging elements) and the variables (what you are manipulating).

For this project, our contants are the type of plant used, the container, and the amount of liquid for each plant.

We measured the same amount of liquid and “watered” each plant. We notated the amount we used (this will vary depending on the size of your pot) We used 1/4 cup at the beginning. You will see in observations, that we later had to change this.

It is important to note: We also measured each plant at the beginning of the project to get the starting size for each plant. We wanted to know how much the plants grew over time and having a baseline measurement was very important.

Each day we measured each plant, “watered” it with the appropriate liquids, and collected the data in our science notebooks. We repeated this for 2 weeks.

Observations/Data

Every day Legoman would grab his tray of plants, his ruler and his liquids. He was excited to wake up each day and “get to work”. It was immediately obvious that the plant with the salt water was starting to wilt. For day one, most of the plants had not grown any, but the salt plant had began to shrivel.

If we were reporting this as a science fair (and if you repeat this) we would report what happened every day, with the measurements and the changes. However, I need to leave something for you to find out! Don’t you want to see what happens?

We couldn’t believe what happened to the plants! Seriously, you will want to try this one and this is the perfect season! I wanted to have a printable available for you but couldn’t find it. I’d love to know if you are interested or have a need for a printable science journal and science project packet.

Legoman really had fun putting all his data into the computer and making graphs for his science board.

ARE YOU READY FOR MORE SCIENCE FUN?

Time for saturday science blog hop, visit these great bloggers for more fun saturday science experiments too.

Jelly Bean Science from P is for Preschooler

25 Classic Science Experiments For Kids from Little Bins For Little Hands

Follow Science Experiments for Kids on Pinterest.

What is your favorite science activity? I would love to hear! connect with me on  Facebook ,  Twitter ,  Google+ ,  Pinterest ,  Instagram  or  subscribe by email . I can’t wait to hear your ideas.

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16 thoughts on “Experiments for Kids | Effecting Plant Growth”

Aww, you’re not going to tell us the results?! 😉 This sounds like an interesting experiment and I love how into itLegoman was!

I absolutely love this!!! Thank you so much for posting such a thorough post about it. On my list of things to do.

Oh your poor salt plant! It looks like most of mine in the garden, haha. Great experiment.

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This is an interesting science experiment. Let’s not forget the proper spelling though. In most of the times when the word “effecting” was used here it actually meant “affecting” instead. Since wet are teaching children, spelling is important. This scientific project could be called: ” The effect of different solutions in plant growth: how various solutions affect plant growth. ” Tricky words!

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Dear Legoman Mum,

I am a science teacher from Hong Kong and I find your experiement bery useful and interesting. I would like to have a printable science journal and science project packet. Please kindly send to me. Thank you very much!!!!

What would you like in your science journal. This is definitely something I could work on.

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Hi, i am a primary school teacher. I really love this experiment that you have conducted, by any chance are you able to send me the results of this experiment? I would love to show my Year 2 class your observations, and your results.

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