Plant and Soil

, Volume 406, Issue 1–2, pp 409–423 | Cite as

Magnesium deficiency decreases biomass water-use efficiency and increases leaf water-use efficiency and oxidative stress in barley plants

  • Merle TränknerEmail author
  • Bálint Jákli
  • Ershad Tavakol
  • Christoph-Martin Geilfus
  • Ismail Cakmak
  • Klaus Dittert
  • Mehmet Senbayram
Open Access
Regular Article



In water-scarce agro-environments a clear understanding of how plant nutrients like magnesium (Mg) affect plant traits related to water-use efficiency (WUE) is of great importance. Magnesium plays a crucial role in photosynthesis and is thus a major determinant of biomass formation. This study investigated the effect of Mg deficiency on leaf and whole plant water-use efficiency, δ13C composition, hydrogen peroxide (H2O2) production and the activity of key enzymes involved in ROS scavenging in barley.


Barley (Hordeum vulgare) was grown in hydroponic culture under three different levels of Mg supply (0.01, 0.1, 0.4 mM Mg). WUE was determined on the leaf-level (leaf-WUE), the biomass-level (biomass-WUE) and via carbon isotope discrimination (δ13C). Additionally, concentrations of Mg, chlorophyll and H2O2, and the activities of three antioxidative enzymes (ascorbate peroxidase, glutathione reductase and superoxide dismutase) in youngest fully expanded leaves were analyzed.


Dry matter production was significantly decreased (by 34 % compared to control) in Mg deficient barley plants. Mg deficiency also markedly reduced leaf Mg concentrations and chlorophyll concentrations, but increased H2O2 concentrations (up to 55 % compared to control) and the activity of antioxidative enzymes. Severe Mg deficiency decreased biomass-WUE by 20 %, which was not reflected regarding leaf-WUE. In line with leaf-WUE data, discrimination against 13C (indicating time-integrated WUE) was significantly reduced under Mg deficiency.


Mg deficiency increased oxidative stress indicating impairment in carbon gain and decreased biomass-WUE. Our study suggests that biomass-WUE was not primarily affected by photosynthesis-related processes, but might be dependent on effects of Mg on night-time transpiration, respiration or root exudation.


Magnesium Water use efficiency Carbon discrimination Oxidative stress Barley 


The increasing world population and the concomitant decrease in water availability due to global climate change and land use change (Pachauri and Meyer 2014) raise an increasing challenge for higher agricultural crop productivity. A key factor limiting plant productivity under drought stress is water-use efficiency (WUE) of crop plants. In general, WUE describes the ratio of assimilated carbon to water used by the plant and is thus, a measure for the effciency in optimizing carbon assimilation while minimzing water use (Bramley et al. 2013). Usually, plants transpire much higher amounts of water compared to relatively small amounts of carbon that are fixed; between 200 and 1000 g of water are transpired per g of assimilated carbon (Bramley et al. 2013). Manipulating the ratio towards higher efficiency might contribute to the stabilization of yields under present and future conditions of diminishing fresh water supply and increasing food demand.

Water-use efficiency is a rather flexible term depending on the scale being considered, e.g. at the leaf or whole plant level. Biomass-WUE takes the whole plant into account and is defined as plant dry matter production per unit of water loss via transpiration during the vegetation period (Tallec et al. 2013). Biomass-WUE is affected by numerous factors related to biomass formation (e.g. photosynthesis, respiratory carbon loss) and whole plant transpiration (transpiration and unproductive water-loss by e.g. nocturnal transpiration) (Claussen 2002; Wang et al., 2013). Under field conditions, only aboveground biomass is considered for calculation of WUE (shoot-WUE), whereas in greenhouse experiments, where roots can be harvested, total biomass can be used for calculation of WUE (Bramley et al. 2013). Thus, biomass-WUE can be calculated as: Biomass-WUE = Total biomass (g) /Water use (L). Substantial variation in biomass-WUE under variable environmetal conditions (e.g. nutrient supply, water scarcity, elevated CO2 concentrations) have been reported (Cernusak et al. 2009; Lewis et al. 2011).

At the leaf level, intrinsic water-use efficiency (leaf-WUE) is defined as the instantaneous ratio between net CO2 assimilation rate (A) and stomatal conductance (gs): Leaf-WUE = A/gs, whereas gs is the ratio of transpiration to air-to-leaf vapor pressure deficit. The carbon stable isotope composition of the plant dry matter (δ13C) is used as time-integrated indicator for WUE as it represents a measure of plant C assimilation over the period during which dry matter is generated (Wang et al. 2013). Low carbon isotope discrimination (Δ13C) is seen as indicator of high leaf-WUE (Farquhar et al. 1982) and it has been commonly used as an indicator of leaf-WUE in wheat (Farquhar and Richards 1984), poplar (Rasheed et al. 2013) and tobacco (Brueck and Senbayram 2009). However, the relationship between Δ13C and leaf-WUE can be unbalanced by differences in the respective time of integration (Ripullone et al. 2004) or by variable mesophyll diffusion conductances (gm) (Warren and Adams 2006; Soolanayakanahally et al. 2009).

The different scales at which both types of WUE (at a leaf or whole plant level) are measured, might lead to discrepancies in upscaling leaf-WUE to biomass-WUE (Medrano et al. 2015) because components like canopy effects, night-time transpiration and respiration affect WUE differently. Night-time transpiration increases water use without concomitant carbon assimilation, thus lowering biomass-WUE. Medrano et al. (2015) reported that night-time transpiration accounted for 10 % of the daily transpiration in grapevine. In addition, respiratory processes lead to carbon loss, thus reducing net carbon gain and also lowering biomass-WUE. Plant respiration plays a crucial role in carbon balance and might be a main unknown factor when comparing leaf-WUE and biomass-WUE (Medrano et al. 2015).

Water-use efficiency can be improved by plant breeding or different agronomical practices regarding soil and plant nutrient management (Blum 2009). Positive effects of adequate nitrogen (Shangguan et al. 2000; Brueck and Senbayram 2009) and potassium supply (Fournier et al. 2005; Arquero et al. 2006) on biomass-WUE and leaf-WUE were reported, but knowledge on how Mg deficiency may affect WUE in crop plants is scarce.

Magnesium as one of the essential plant nutrients, is the most abundant divalent cation in cellular systems (Li et al. 2001). Free Mg2+ ions stabilize membranes and are involved in activation of numerous enzymes, among them ATPases and ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) (Li et al. 2001; Shaul 2002). Magnesium is essential for chlorophyll synthesis and up to 10 % of total Mg can be associated with chlorophyll (Wilkinson et al. 1990). Furthermore, Mg is important for grana stacking in chloroplasts (Hall et al. 1972; Ceppi et al. 2012) that may adversely affect photosynthetic performance of plants suffering from low Mg supply. It was reported that the degree of stacking increases with increasing Mg concentrations (Stys 1995) and Mg deficiency leads to disruption of grana stacks (Hall et al. 1972). Such involvements of Mg demonstrate its irreplaceable functions in photosynthesis. A reduction of dry matter production under Mg deficiency was observed in various plants, such as bean (Fischer and Bremer 1993; Cakmak et al. 1994a), sugar beet (Hermans et al. 2004), rice (Ding et al. 2006), Arabidopsis (Hermans and Verbruggen 2005). One of the earliest responses of plants to Mg deficiency is impaired phloem loading (Cakmak et al. 1994a). Photo-assimilates are not sufficiently transported from source leaves into sink tissues such as shoot tips, seeds and roots, leading to an accumulation of sucrose and starch in source leaves. This accumulation of carbohydrates commonly occurs before visible Mg deficiency symptoms develop, e.g. interveinal chlorosis and necrotic lesions on leaves and reduction in shoot biomass and also before any distinct change in photosynthetic performance of plants is detectable (Cakmak and Kirkby 2008; Verbruggen and Hermans 2013). The sucrose accumulation may trigger a downregulation of genes involved in photosynthesis (Jang and Sheen 1994) or a carbon-metabolite feedback inhibition of photosynthesis, whereas both processes inevitably decrease assimilation. A decrease in assimilation is also caused by reduced acitivities of enzymes involved in CO2 fixation (Cakmak and Kirkby 2008). Decreased capacity to fix CO2 leads to an overreduction of the photosynthetic electron transport chain, initiating the photoreduction of dioxygen (O2) to superoxide (O2 ) and subsequently the catalysis of superoxide to H2O2 by superoxide dismutase (Asada 1999). H2O2 is suggested to act as a signal messenger (Møller et al. 2007), being involved in abiotic and biotic stress response (Cheeseman 2007). However, increased levels of H2O2 cause oxidative damage to cell components (Foyer and Noctor 2011). Additionally, the presence of H2O2 inhibits the activity of enzymes involved in photosynthesis, in particular RubisCO (Asada 1999), further limiting the photon-utilization capacity and ultimately enhancing the production of reactive oxygen species. Enhanced H2O2 generation is also typical under low water supply, contributing to ABA-induced stomatal closure (Hu et al. 2006; Wang and Song 2008), lipid peroxidation and chlorophyll degradation (Farooq et al. 2009; Foyer and Shigeoka 2011). Under such conditions, detoxification of H2O2 is extremely important and it is achieved by ascorbate peroxidase (APX) in the chloroplast (Foyer and Noctor 2011). APX reduces H2O2 to H2O at the expense of ascorbate which is subsequently regenerated either by oxidation of monohydroascorbate or by reduction of dehydroascorbate. For the latter, glutathione serves as the reductant wich is generated from reducing glutathione sulfide by glutathione reductase (GR) (Polle 2001). Enhanced activities of the antioxidant enzymes indicate photooxidative stress as commonly observed under Mg deficiency (Cakmak and Kirkby 2008). An up-regulation of antioxidant enzyme activities and antioxidant metabolites was observed in common bean (Cakmak and Marschner 1992), citrus (Tang et al. 2012), wheat (Mengutay et al. 2013) and maize (Tewari et al. 2006).

In the future, incidences and duration of drought are predicted to increase, posing a risk to agricultural production and yield stability. Plants suffering Mg deficiency are assumed to be more sensitive to drought and adequate Mg supply is needed for optimal yield formation under drought situations (Senbayram et al. 2015a). Hence, a clear understanding of how Mg affects plant traits related to water-use efficiency (WUE) is of great importance. To our knowledge, there are no published reports on effects of Mg-deficiency on leaf- and biomass-WUE. For several reasons, WUE of plants can be adversely affected by the Mg nutritional status of plants. For example, it is well known that an adequate Mg nutrition is required for stomatal conductance of plants (Laing et al. 2000; Cakmak and Kirkby 2008). In addition, well-documented reductions in root growth due to impairments in carbon partitioning into roots (Cakmak et al. 1994a; Hermans et al. 2004) may greatly affect root water uptake from growth medium as suggested by Cakmak and Kirkby, (2008) and Senbayram et al. (2015a, b). This study has been conducted to increase knowledge regarding direct effect of varying Mg supply on biomass-WUE and related parameters, including carbon assimilation, carbon isotope discrimination and ascorbate peroxidase activity in combination with the capacity to form H2O2 under non-limiting water supply.

Materials and methods

Plant culture

Seeds of Hordeum vulgare L cv. Şahin-91 were germinated in wetted paper rolls in the greenhouse and seedlings were transferred to hydroponic plant culture using 5 l pots (2 plants per pot). In order to avoid osmotic shock, seedlings were grown in half-strength nutrient solution for the first 5 days, then transferred to 75 % of full-strength nutrient solution for another 6 days before supplying full-strength nutrient solution. Full-strength nutrient solution contained 2 mM K2SO4, 3 mM NH4NO3, 1 mM MgSO4*7 H2O, 1 mM CaCl2*2 H2O, 0.25 mM Ca(H2PO4)2*H2O, 0.1 mM Fe-EDTA, 25 μM H3BO3, 2 μM ZnSO4*7 H2O, 2 μM MnSO4*H2O, 0.5 μM CuSO4*5 H2O, 0.075 μM H24Mo7N6O24*4 H2O. 24 days after germination, three magnesium levels were introduced: 0.01 mM (Mglow), 0.1 mM (Mgmed) and 0.4 mM MgSO4*7 H2O (Mghigh). In order to avoid nutrient depletion, nutrient solutions were renewed every 5 days at the beginning of the experiment and every 2 days at later growth stages. Nutrient solutions were permanently aerated.

Determination of Mg concentrations, SPAD and δ13C

Plants were harvested 34 days after onset of treatments (DAO), separated into roots and shoots and dried at 60 °C for dry matter (DM) determination. In order to assess chlorophyll concentrations, SPAD readings (Konica Minolta, Japan) were taken on youngest fully expanded leaves. For determination of magnesium concentration, 100 mg of dried plant material was digested in 4 ml concentrated HNO3 and 2 ml 30 % H2O2 at 200 °C and 15 bar for 75 min. Magnesium concentrations were measured by ICP-OES (Vista RL, CCD simultaneous ICP-OES, Varian Inc., USA) and atomic absorption spectrometry (220 FS, Varian Inc., USA).

The carbon isotope discrimination (Δ13C) was analyzed as a measure of time-integrated leaf-WUE. The ratio of 13C to 12C in shoot dry matter and in young leaves was determined after Dumas combustion on a ThermoFinnigan Delta Plus IRMS (ThermoFinnigan, Bremen, Germany). δ13C was calculated by relating the measured isotopic ratio to Vienna PeeDee Belemnite lime stone formation (VPDB) (according to Smith and Epstein 1971). For analyzing the relationship between leaf-WUE and leaf δ13C values, mean leaf-WUE was calculated via temporal integration. For analyzing the relationship between total biomass-WUE and leaf δ13C values, two additional replications of Mglow and Mghigh were included in the dataset. These two additional replications were harvested on 20 DAO and total biomass-WUE was determined as described below.

Determination of hydrogen peroxide concentrations and ascorbate peroxidase activity

Determination of H2O2 concentration was conducted using ferrous ammonium sulfate xylenol orange (FOX) solution described by Wolff (1994) and modified by Cheeseman (2009). Briefly, 3 leaf discs were taken from young leaves using a cork borer (0.46 cm2) and were transferred to 1 ml acetone acidified by adding 25 mM H2SO4. Samples were frozen in liquid nitrogen. For measurements, FOX solution containing 250 μM ferrous ammonium sulfate, 100 mM sorbitol, 100 μM xylenol orange and 25 mM H2SO4 was prepared prior to thawing the samples at room temperature for 45 min. 1 ml of FOX solution was added to 50 μl of each sample which were previously dissolved in acidified acetone. Samples were incubated at room temperature for 30–45 min. H2O2 was quantified spectrometrically (EPOCH, BioTec, USA/8453 UV-VIS Spectroscopy System, Agilent, USA) at 550 nm and subtracting the background at 850 nm using a standard curve ranging from 0 to 100 μM.

For measurement of ROS scavenging enzyme activities, leaf samples were harvested and immediately frozen in liquid nitrogen. 0.5 g of samples were homogenized in 5 ml phosphate buffer (pH 7.6) including 1 % polyvinylpyrrolidone (PVP) and 0.1 mM EDTA and centrifuged for 20 min at 16,000 g at 4 °C. The supernatant was collected and used as crude extract in the reaction mixtures of the enzyme activity assays.

For ascorbate peroxidase (APX) assay, the 0.3 ml reaction mixture contained 0.5 mM ascorbic acid, 50 mM phosphate buffer, 1 mM EDTA, 0.5 mM H2O and 10–15 μl of the supernatant. The reaction was started by adding 10 μl of 15 mM of H2O2 and APX was assayed spectrometrically (EPOCH, BioTec, USA/8453 UV-VIS Spectroscopy System, Agilent, USA) following the decrease of absorbance at 290 nm (Nakano and Asada 1981).

Glutathione reductase (GR) was assayed according to Halliwell and Foyer (1978) with slight modifications. The 0.3 ml reaction mixture contained 0.2 mM nicotinamide adenine dinucleotide phosphate (NADPH), 1 mM GSSG (glutathione disulfide; oxidized form of glutathione), 50 mM K-P buffer (pH 7.6) with 0.1 mM EDTA and 10–15 μl of crude extract. GR was determined following the decrease in absorbance at 340 nm as NADPH was oxidized. The background was corrected by observing the non-enzymatic oxidation of NADPH in the absence of GSSG. Superoxide dismutase (SOD) activity was determined according to Giannopolitis and Ries (1977) with small modifications. The 0.3 ml reaction mixture contained 50 mM phosphate buffer, 0.1 mM EDTA, 50 mM Na2CO3, 12 mM L-methionine, 75 μM nitroblue tetrazolium (NBT), 2 μM riboflavin and 10–20 μl of the enzyme extract. Riboflavin was added at last and the samples were placed under fluorescent light (4000 lx) for 10 min. Following that, the inhibition of photoreduction of NBT by SOD was measured at 560 nm. Blank samples with no crude extract where considered having the highest reaction rate of super oxide with NBT. One unit of SOD activity is defined as the amount of enzyme required to cause 50 % inhibition of the rate of NBT reduction at 560 nm.

Gas exchange measurements and calculation of leaf water-use efficiency

Net assimilation and stomatal conductance were determined on youngest fully expanded leaves (GFS-3000, Heinz Walz GmbH, Germany). Cuvette conditions were set as follows: 22 °C, 55 % rel. humidity, 380 ppm CO2, photosynthetic photon flux density of 1000 μmol m−2 s−1 generated by blue and red LEDs. After reaching stable values due to leaf adjustment to cuvette conditions (after 30 to 45 min.), fluxes were averaged over 5 min. Leaf water-use efficiency (leaf-WUE) was determined by relating net assimilation to stomatal conductance.

Calculation of transpiration and biomass water-use efficiency

Daily whole plant transpiration was assessed by measuring daily weight differences of the pots. Each pot was placed on a balance (TQ30, ATP Messtechnik, Germany), automatically recording the weight in an interval of 30 min. in a one-gram-resolution. As pots were sealed, weight reduction of pots was solely caused by transpiration of plants. Cumulative transpiration was calculated by summing up daily weight differences. Shoot biomass water-use efficiency (shoot biomass-WUE) was determined by relating the shoot dry mass to the cumulative transpiration and total biomass water-use efficiency (total biomass-WUE) was determined by relating the total plant dry mass to the cumulative transpiration.

Modelling plant growth and daily shoot water-use efficiency

To allow comparison of daily shoot-WUE an empirical model procedure was developed to estimate daily biomass production from leaf area (LA) development. For this purpose, 30 pots of barley plants were grown in a preliminary trial under conditions similar to those of the main experiment. Plant images were taken from a fixed position (defined distance and angle) at least twice a week in front of a black background using a digital single-lens reflex camera (Canon EOS 600D, Canon Inc., Japan). The area of green pixels in each picture was calculated using ImageJ software (Rasband 1997). After the imaging procedure, plants of two pots were harvested and total leaf area per pot was determined using a desktop scanner together with ImageJ. Measured LA of the harvested pots was plotted against the respective area of green pixels and a second order polynomial was fitted (see Online Resource 1). The parameters of the polynomial were used for calculating LA per pot from plant images only. The linear regression between observed and predicted LA together with the mean absolute predictive discrepancy (MD) were used to indicate the goodness of fit (r2 = 0.98, p < 0.001, MD = 87.1 cm2). During the main experiment, LA was imaged at least once a week. To obtain daily values of LA development, three-parametric logistic growth curves of the shape \( f(x)=\frac{a}{1+b\ {e}^{\left(-kt\right)}} \) (where a, b, k are the estimated parameters of the function and t is temporal component) were calculated from iterative non-linear least-square regression using the nls-function implemented in R (R Core Team 2014). Daily shoot dry matter was estimated assuming that growth curves of shoot dry matter production follow the same curve progression as leaf area production.

The logistic curves obtained from the imaging of leaf area development were fitted to three discrete data points along the experimental period where DM per pot was known (start of experiment, second harvest) or could be estimated (first harvest). Parameters a and b were re-fitted, k was considered constant. For estimating dry matter at the first harvest date, the ratio of LA to shoot DM was calculated from plants harvested on that day. Mean ratio per treatment was then used to calculate shoot DM for each pot not harvested on that day with respect to treatments. The resulting curves of total daily dry matter per pot were differentiated to obtain values of dry matter production per day and pot (daily DM) (see Online Resource 2).

Daily shoot-WUE was calculated by relating daily DM increase to daily whole plant transpiration.

Statistical analyses

Statistical analyses were performed using R version 3.0.3 (R Core Team 2014). Analysis of variance (ANOVA) was performed to determine whether effects of treatments on the respective factor were significant, followed by Duncan’s post-hoc test (α = 0.05) where ANOVA indicated significance. Data were tested for normal distribution with Shapiro-Wilk-Test and, where necessary, transformed logarithmically. Data are displayed untransformed.


Plant dry matter formation, Mg leaf concentrations and chlorophyll content

At the final harvest, 34 days after onset of treatments (DAO), plant total dry matter (DM), shoot DM and root DM decreased significantly with decreasing rate of Mg supply (Table 1). Total DM was about 73 % and 34 % lower in plants supplied with 0.01 mM Mg (Mglow) and 0.1 mM Mg (Mgmed) as compared to plants treated with 0.4 mM Mg (Mghigh). The shoot/root ratio was unaffected by the rate of Mg supply (Table 1). Over all treatments, total DM production correlated well with shoot Mg uptake (r2 = 0.923) (Fig. 1) and leaf Mg concentrations were significantly lower in Mglow and Mgmed when compared to Mghigh treated plants. This effect was more severe in older leaves than in younger leaves (Table 1). Highest Mg concentrations were measured in youngest fully expanded leaves in Mghigh treatment (1.09 ± 0.07 mg g−1 DM ). Mg concentrations in leaves of medium and low Mg supplied plants were 37 % and 62 % lower than in plants treated with Mghigh 34 DAO, respectively. However, in older leaves, Mg concentrations in Mglow (0.25 ± 0.01 mg g−1 DM) and Mgmed (0.45 ± 0.08 mg g−1 DM) treated plants were 74 % and 53 % lower compared to plants supplied with Mghigh. Leaf SPAD values that were recorded to assess leaf chlorophyll content were similar in all treatments until 9 DAO. Then, until 33 DAO, SPAD readings decreased steadily to 64 % and 57 % in Mglow and Mgmed, but remained almost constant in Mghigh (Fig. 2).
Table 1

Effect of Mg supply on total DM (g per pot), shoot DM (g per pot), root DM (g per pot), shoot-to-root ratio, Mg concentration (young and old leaves; mg g−1 DM), δ13C, total biomass water-use efficiency (biomass-WUE, g L−1) and shoot-WUE (g L−1) in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treated barley plants. Values are means ±SE (n = 3). Means followed by the same small letter are not significantly different (α = 0.05)

Mg supply (mM)

Dry matter

Shoot/root ratio

Mg concentration


total (g)

shoot (g)

root (g)

young leaf (mg g−1 DM)

old leaf (mg g−1 DM)

total (g DM L-1)

shoot (g DM L-1)


17.9 ± 1.38 c

15.56 ± 1.22 c

2.38 ± 0.15 c

6.53 ± 0.11 a

0.41 ± 0.00 c

0.25 ± 0.01 b

3.77 ± 0.12 b

3.27 ± 0.10 b


38.8 ± 1.63 b

33.36 ± 1.21 b

5.42 ± 0.43 b

6.20 ± 0.29 a

0.69 ± 0.06 b

0.45 ± 0.08 b

4.49 ± 0.28 a

3.86 ± 0.22 a


53.2 ± 0.50 a

46.06 ± 0.59 a

7.08 ± 0.50

6.58 ± 0.54 a

1.09 ± 0.07 a

0.96 ± 0.14 a

4.55 ± 0.13 a

3.95 ± 0.14 a

Fig. 1

Relationship between total DM (g per pot) and shoot Mg uptake in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants

Fig. 2

SPAD values measured on old leaves in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants. Means ±SE (n = 3). DAO = days after onset of treatment

Hydrogen peroxide concentration and ROS scavenging enzyme activity

Hydrogen peroxide concentrations in youngest fully expanded leaves of Mglow plants were 40 % and 55 % higher than in Mghigh on 13 DAO and 33 DAO (Fig. 3a). Overall, the activities of ROS scavenging enzymes were higher in Mg deficient plants. 13 DAO, the activities of APX and GR were 4- and 3-fold higher in Mglow than in Mghigh, and 33 DAO, they were 5- and 3-fold higher (Fig. 3b, c). Superoxide dismutase, the enzyme being responsible for the reduction of superoxide to hydrogen peroxide, showed highest activity in Mg deficient plants. In contrast to APX and GR, the SOD activity of Mglow and Mgmed treatments did not differ from each other (Fig. 3d).
Fig. 3

Hydrogen peroxide (H2O2) concentrations (a), ascorbate peroxidase (APX) activity (b), glutathione reductase (GR) activity (c), and superoxide dismutase (SOD) activity (d) in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants at 13 and 33 days after onset of treatment (DAO). Error bars represent standard errors (n = 6, n = 12 for hydrogen peroxide). Means labelled with the same small letter are not significantly different (α = 0.05)

Leaf water-use efficiency

Temporal courses of net assimilation rate (AN) and stomatal conductance (gs) measured on youngest fully expanded leaves are presented in Fig. 4. In treatments Mghigh and Mgmed, mean AN were 26.8 ± 0.64 and 21.2 ± 0.27 μmol CO2 m−2 s−1, and remained almost constant throughout the experimental period. However in Mglow, AN decreased rapidly and already starting 8 DAO, it was significantly lower than in the other Mg treatments. Here, AN was 11.7 ± 1.14 μmol m−2 s−1 in Mglow, being 59 % lower than in Mghigh. Stomatal conductance (gs) decreased in all three Mg-supply treatments during the experimental period. However, the decrease in gs was more pronounced in Mglow. From 6 to 13 DAO in Mglow, the gs values decreased from 267.5 ± 19.6 mmol m−2 s−1 to 125.8 ± 24.9 mmol m−2 s−1 and remained almost constant thereafter. Interestingly, the decrease in gs was more pronounced than the decrease in AN and thus, affected the leaf-WUE (AN/gs) significantly. In Mglow, leaf-WUE was 79.29 ± 3.87 μmol CO2 mol−1 H2O at 6 DAO, being already slightly higher than in Mghigh, and over time, it increased to 92.51 ± 3.94 μmol CO2 mol−1 H2O on 33 DAO. In Mghigh, leaf-WUE was 56.38 ± 2.67 μmol CO2 mol−1 H2O on 8 DAO and increased gradually to 88.8 ± 10.44 μmol CO2 mol−1 H2O until 33 DAO (Fig. 4c). In Mgmed, leaf-WUE remained almost constant during the experiment, being similar to leaf-WUE in Mghigh, except for the measurement on 13 DAO.
Fig. 4

Net assimilation (a), stomatal conductance (b) and leaf water-use efficiency (leaf-WUE) (c) in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants. Symbols represent means ±SE (n = 3). DAO = days after onset of treatment

Biomass water-use efficiency

Overall, total biomass-WUE of barley plants ranged from 3.77 to 4.55 g DM L−1 H2O on 34 DAO (Table 1). Here was no significant difference when comparing Mghigh and Mgmed, however, total biomass-WUE in Mglow was significantly lower (17 % lower total biomass-WUE) than in both Mghigh and Mgmed (Table 1). The effect of Mg supply on shoot biomass-WUE followed the same trend as with total biomass-WUE (Table 1).

Daily shoot water-use efficiency

Daily shoot-WUE (DM production day−1/transpiration day−1 pot−1) was calculated by estimating the biomass production and relating it to the measured daily transpiration. The daily shoot-WUE of all three treatments increased from treatment start and showed quite simultaneous maxima at day 18 and 19 (Fig. 5). These maximum daily shoot-WUE of Mghigh, Mgmed and Mglow were 4.9, 5.8 and 5.6 g DM L−1 H2O. Subsequently, the daily shoot-WUE decreased until 32 DAO in all three treatments, however, the decline was much more pronounced in Mglow. Here, daily shoot-WUE declined by 75 % to 1.4 g DM L−1 H2O at 32 DAO, thus having a daily shoot-WUE of only 44 % compared to the control.
Fig. 5

Daily shoot water-use efficiency in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants. Represented are means ±SE (n = 3). Error bars represent standard errors. DAO = days after onset of treatment

Carbon isotope composition

The δ13C values of the shoot biomass decreased with increasing Mg supply (Fig. 6a). Highest discrimination was observed in control plants and plants with medium Mg supply (−30.47 ± 0.37 ‰ and −29.26 ± 0.37 ‰). In Mg deficient plants, δ13C values were significantly higher (−27.66 ± 0.14 ‰) compared to the control. δ13C values of young leaves showed significant positive correlation with integrated leaf-WUE (Fig. 6b). However, there was no common relationship between shoot δ13C values and total biomass-WUE (Fig. 6c), but individual linear relationships were found between shoot δ13C and total biomass-WUE within plants of the same Mg treatment.
Fig. 6

δ13C values of shoot (a), relationship between δ13C of young leaves and integrated leaf water-use efficiency (leaf-WUE) (b), relationship between δ13C of shoot and total biomass water-use efficiency (total biomass-WUE) (c) in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants. DAO = days after onset of treatment. Error bars represent standard errors (n = 3). Means labelled with the same small letter are not significantly different (α = 0.05)


Effect of Mg supply on chlorophyll content and dry matter formation

Being involved in many physiological and biochemical processes, Mg is an essential element for plant growth and development (Cakmak and Kirkby 2008; Cakmak and Yazici 2010; Cakmak 2013). As expected, plant total dry matter (DM) formation responded significantly to the various Mg supply regimes (Table 1). In a similar study on wheat, authors reported a decrease of 21 % in total DM within 23 DAO when concentration of supplied Mg decreased from 0.45 to 0.015 mM (Mengutay et al. 2013). Magnesium leaf concentrations were lower in older leaves than in younger leaves, an effect that is well known to be attributable to the phloem-mobile nature of Mg within the plant: Mg is thought to be relocated from the older to the younger leaves enabling the plant to start the generative phase in order to finish its life cycle. One of the major functions of Mg in plants is its role as the central atom of the chlorophyll molecule in the light absorbing complex of chloroplasts and its contribution to the photosynthetic carbon dioxide fixation (Cakmak and Kirkby 2008). In the present study, chlorophyll content of Mg-deficient plants was remarkably lower than in adequate Mg-supplied plants. Visual leaf symptoms of Mg deficiency appeared as interveinal leaf chlorosis being apparent first on older leaves. The latter progressed to the young leaves as the deficiency became more severe over time, which might be attributable to the phloem-mobile behavior of Mg (Hermans et al. 2013). Chlorosis and following necrosis might be, among other reasons, a consequence of increased ROS generation, such as hydrogen peroxide (H2O2). Increased levels of H2O2 under Mg deficiency might be attributed to a lower demand for reductants in the Calvin cycle and concomitant fully reduced photosynthetic electron carriers which results in an increased electron flow to O2. The reduced oxygen is then dismutated to H2O2. In line with our data, higher H2O2 concentrations in Mg deficient maize leaves (Tewari et al. 2004) and mulberry plants (Tewari et al. 2006) were reported. An increased activity of key ROS scavenging enzymes (APX, SOD and GR) indicates an activation of the antioxidant machinery and an increased effort to reduce, viz. detoxify H2O2 in the chloroplasts. In line with the present study, enhanced activities of ROS scavenging enzymes under Mg deficiency were observed in maize (Tewari et al. 2004), wheat (Mengutay et al. 2013), citrus (Tang et al. 2012; Yang et al. 2012) and bean (Cakmak 1994). The fact that H2O2 concentrations in Mglow were higher than in Mgmed and Mghigh despite highest APX and GR activities indicates that the capacity of H2O2 scavenging was insufficient and thus, the generation of H2O2 might be one of the stress factors that contribute to growth reductions under severe Mg deficiency.

In the present experiment, the shoot/root ratio was similar in all treatments indicating no significant effect of Mg supply on DM allocation (Table 1). A number of reports showed an increased shoot/root ratio under Mg deficiency, for example in wheat and maize (Mengutay et al. 2013), bean (Cakmak et al. 1994b) and coffee (Meireles da Silva et al. 2014). Such increase in shoot/root ratio is commonly attributed to the negative effect of Mg deficiency on phloem loading and assimilate translocation. In contrast, in studies on Arabidopsis (Hermans and Verbruggen 2005) and sugar beet (Hermans et al. 2005) the shoot/root ratio did not change. So far, we do not have a clear explanation for this discrepancy; however, it may be related to the duration of the Mg deficiency and the vegetation period in our experiment. Verbruggen and Hermans (2013) suggested a more severe impact on root growth than on shoot growth, hence an increase in shoot/root ratio can only be observed when young plants are exposed to Mg deficiency. We may speculate that when plants are grown with sufficient Mg concentrations before exposing them to Mg deficiency, like in our study, no or less pronounced changes may occur. Currently, knowledge on the effect of Mg-deficiency on root developement is still scarce, thus further research in this area is needed.

Leaf gas exchange and leaf water-use efficiency affected by Mg supply

In the present study, low Mg supply decreased both mean AN and gs significantly. Similarly, Terry and Ulrich (1974) reported a rapid decline in AN due to Mg deficiency in sugar beet already 7 DAO. Similar results were obtained in studies on Pinus (Laing et al. 2000; Sun et al. 2001), citrus (Tang et al. 2012; Yang et al. 2012), sugar beet (Terry and Ulrich 1974) and maize (Jezek et al. 2015). Tang et al. (2012) proposed non-stomatal reasons for a decrease in AN in citrus, as the internal CO2 concentration did not differ from that of plants supplied with adequate amounts of Mg. Significant decreases of AN in Mglow indicate that Mg deprivation did not only decrease the rate of leaf area expansion but also photosynthetic carbon gain per unit leaf area. This decrease in photosynthetic efficiency, which was most prominent in the Mglow-treated plants, might also explain why excessive H2O2 formation was highest in the Mglow treatment: it is very likely that an incomplete or blocked photosynthesis causally triggered the formation of oxygen radicals due to higher light intensities than sufficient to saturate photosynthetic processes under the experimental conditions. Mg deficiency induced decreases in AN are commonly attributed to i) a decrease in chlorophyll content (Peaslee and Moss 1966), and ii) an accumulation of carbohydrates in leaves suffering Mg deficiency causing feedback inhibition of RubisCo activity, and higher mesophyll resistance towards CO2 diffusion (Terry and Ulrich 1974). Furthermore, Mg deficiency leads to changes in the ultrastructure of chloroplasts as thylakoid stacks are disrupted (Hall et al. 1972). Ceppi et al. (2012) observed a decrease in maximum fluorescence intensity (FM) during measurement of fast chlorophyll flourescence induction kinetics on sugar beet. The authors attributed the decrease of FM to the disruption of grana stacks and an increasing spillover of energy from photosystem (PS) II to PS I (Ceppi et al. 2012). Grana disruption diminishes the segregation of PSI and PSII (Stys 1995) and leads to quenching of PSII fluorescence (Murata 1969). This and the above mentioned reasons might explain the detrimental effects of Mg deficiency on photosynthesis.

Tang et al. (2012) and Yang et al. (2012) reported lower gs in Mg deficient Citrus plants. A decrease of gs was also reported by Kobayashi et al. (2013). In their study on rice plants, Mg deficiency had a more pronounced effect on transpiration rates, as the latter declined earlier than assimilation rates. Similarly in our study, the stomatal conductance was distinctly affected, as the decrease in gs in Mglow compared to Mghigh was more pronounced than the decrease in AN, causing higher leaf-WUE. To our knowledge, there are no studies that analyze effects of varying Mg supply on leaf-WUE. For the first time, the presented data clearly indicate that severe Mg deficiency enhances leaf-WUE. Certainly, more details on the genetic nature of this relationship are needed to understand whether this is a common relationship or whether it is specific for barley.

Effect of Mg supply on biomass water-use efficiency

At the whole-plant level, WUE is defined as plant dry matter production per unit of water loss by transpiration (biomass-WUE). As in the calculation of shoot biomass-WUE root biomass production is not considered, shoot biomass-WUE is lower than total biomass-WUE in all three treatments, but significant differences remain comparing Mglow and Mghigh. This trend is as expected since the shoot/root ratio did not change significantly with differing Mg levels. In the present study, there were no significant differences in either shoot- or total biomass-WUE measured on 34 DAO when comparing Mgmed and Mghigh, although biomass was about 34 % lower (34 DAO), but severe Mg deficiency decreased shoot- and total biomass-WUE by about 20 % on 34 DAO. It is commonly accepted that biomass-WUE increases under elevated nitrogen (Ripullone et al. 2004; Brueck and Senbayram 2009) and potassium supply (Römheld and Kirkby 2010; Grzebisz et al. 2013), but to our knowledge until today, there are no studies reporting on effects of Mg nutrition on biomass-WUE. Yield depression of barley in Mgmed was significant (34 %) when compared to Mghigh; however, biomass-WUE was similar in both treatments. This clearly suggests that only severe Mg deficiency affects biomass-WUE.

Surprisingly, shoot- and total biomass-WUE and leaf-WUE were showing opposite trends: shoot- and total biomass-WUE decreased under Mg deficiency whereas leaf-WUE showed an increase under Mg deficiency. Discrepancies between whole plant-WUE and leaf-WUE were also reported by Tomás et al. (2012) and Senbayram et al. (2015b). In their studies on grapevine cultivars and tobacco, the large variability in whole plant-WUE was not reflected in leaf-WUE as determined by gas exchange and δ13C. This discrepancy might be due to the complexity of factors which are involved in regulating biomass-WUE, but are not directly addressed when measuring instantaneous leaf gas exchange. Compared to leaf-WUE, biomass-WUE takes into account carbon loss (e.g., respiration, root exudates) and unproductive water loss (from non-photosynthesizing parts, and/or night-time transpiration from the leaves), which are two additional parameters that might substantially contribute to the variation in biomass-WUE. One may speculate that higher night-time respiration specifically from the leaves (due to high soluble sugar content) or excessive rates of root exudation in severely Mg deficient plants may contribute to the decline in biomass-WUE. Root exudation may release remarkable amounts of fixed carbon into the rhizosphere; meaning a pronounced loss of reduced carbon assuming that most of the exudates are not re-absorbed by retrieval mechanisms (Kuzyakov and Xu 2013). Usually, retrieval of exuded carbonic compounds such as sugars is partly driven by an H+ electrochemical gradient which is established by H+-ATPase activity. The generated proton gradient drives the uptake of sugars by means of H+/sugar-cotransporter (Jones et al. 2009). Enhanced net release of sugars was found when plasma membrane ATPase was inhibited and consequently, the proton gradient degraded (Mühling et al. 1993). As the activity of H+-ATPase is strongly dependent on the presence of Mg2+ ions (Palmgren 2001), the retrieval mechanism might be disturbed under Mg deficiency. Enhanced root exudation due to mineral deficiency such as potassium deficiency (Kraffczyk et al. 1984), iron-, phosphorous- or nitrogen deficiency was observed in maize (Carvalhais et al. 2011).

Increased dark respiration under progressing Mg deficiency was reported in sugar beet (Terry and Ulrich 1974), and in Phaseolus vulgaris (Fischer and Bremer 1993). In the latter study, dark respiration rates of Mg deficient plants were 50 % higher than in control plants 6 days after onset of Mg deficiency. In conclusion, the decrease in biomass-WUE under severe Mg deficiency may be attributed to possible excessive carbon loss from the root (as exudates) and/or from leaves (night-time respiration).

Shoot- and total biomass-WUE can only give insight into the time-integrated responses at one specific time-point, but not into any dynamics. Thus, in order to understand the dynamics of progressing Mg deficiency and biomass-WUE, we studied variation in daily shoot-WUE throughout the vegetation period via empirical modeling. The strong decrease of daily shoot-WUE in Mg deficient plants after 18 DAO might be partly attributable to the decline in biomass production as the latter occurred as well after 18 DAO (see Online Resource 2). Furthermore, unproductive water loss at night was significantly higher in Mg deficient plants (data not shown), which is another factor that may cause lower biomass-WUE under Mg deficiency. In our study, night-time water loss in Mg deficient plants reached 35 % of daytime transpiration. Night-time transpiration rates may vary between 5 and 15 % of daytime transpiration and, in some cases, rates of up to 30 % were reported (Benyon 1999; Snyder et al. 2003). Night-time transpiration without simultaneous carbon gain imposes carbon costs to the plant; however, it might be beneficial by increasing transpiration-driven mass flow to the root rhizosphere, thus enhancing nutrient availability during night, which positively affects plant productivity and growth (Caird et al. 2007). Here, higher night-time transpiration without concomitant biomass production might partly contribute to reduced biomass-WUE.

Effect of Mg supply on carbon isotope composition

Carbon isotope discrimination reflects the time-integrated CO2 partial pressure at the carboxylation site (cc) and therefore can be used to calculate time integrated cc/ca ratios which are sensitive to A, gs and mesophyll conductance (Seibt et al. 2008; Buckley and Warren 2014). Overall, shoot δ13C values decreased with increasing Mg supply indicating a change in the cc/ca ratio possibly due to the restriction in CO2 diffusion by either stomatal (gs) or mesophyll (gm) conductance. The significant positive relationship between δ13C values of young leaves and leaf-WUE (Fig. 6b) is in accordance with the linear model introduced by Farquhar et al. (1982), where δ13C represents a temporal integration of leaf-WUE and where variability in CO2-diffusion is determined by ci/ca, the ratio of leaf internal to atmospheric concentration, alone. As seen in Fig. 6b, about 96 % of the variation in δ13C values is explained by the change in leaf-WUE. Thus, we may speculate that in our study gm was not significantly affected by varying Mg supply, although changes of leaf density by e.g. increased concentrations of non-structural carbohydrates, which are commonly associated with Mg deficiency (Cakmak et al. 1994a), can cause a reduction of gm (Flexas et al. 2012). Surprisingly, a common relationship could not be established between average shoot δ13C and total biomass-WUE. However, significant individual linear relationships were found between shoot δ13C and total biomass-WUE when plotting each Mg treatment separately. The linear response of average shoot δ13C to total biomass-WUE differed in slope and intercept with respect to Mg supply.

Both δ13C and leaf-WUE data clearly showed that the decrease in biomass-WUE in Mglow treatment was not solely due to the variation in photosynthesis or photosynthesis-related gs. Therefore, we conclude that lower biomass-WUE in Mglow treatment might be attributable to the increase in nocturnal stomatal conductance, respiration or excessive root exudation as discussed above. Thus, this study is the first to establish a direct positive effect of Mg supply on biomass-WUE.


The aim of the current study was to improve our understanding on the direct effect of Mg supply on biomass-WUE and related parameters, e.g. leaf-WUE, and carbon isotope discrimination under non-limiting water supply. In this context, we draw the following conclusions:
  • H2O2 concentrations were increased in Mglow plants, although the activities of the ROS scavenging enzymes APX, GR and SOD were highest in in this experimental treatment. Plants that suffered from moderate Mg-deficiency maintained H2O2 on the level of plants treated with adequate amounts of Mg. Thus we conclude that the capacity of the antioxidative machinery to detoxify ROS is exhausted only under conditions of severe Mg-deficiency.

  • Leaf-WUE and δ13C values (showing time integrated leaf-WUE) were higher under Mg deficiency.

  • In contrast to leaf-WUE, our experiment clearly showed that shoot- and total biomass-WUE decreased under severe Mg deficiency. Mild Mg deficiency caused a significant decrease in DM production (34 %), but did not affect biomass-WUE. We speculate that a possible variation in nocturnal stomatal conductance, night respiration and/or excessive root exudates may be responsible for the decrease in biomass-WUE under severe Mg deficiency.

Supplementary material

11104_2016_2886_Fig7_ESM.gif (49 kb)
Online Resource 1

Correlation of green pixel area and measured leaf area of barley plants. Green pixel area was obtained by green pixel count of images of barley plants taken from a defined distance and angle. Measured leaf area was obtained by measuring the leaf area of individual barley plants used for determination of green pixel area (GIF 48 kb)

11104_2016_2886_MOESM1_ESM.tif (345 kb)
High Resolution Image (TIFF 345 kb)
11104_2016_2886_Fig8_ESM.gif (119 kb)
Online Resource 2 Estimated daily dry matter production over the experimental period in low Mg (0.01 mM Mg), medium Mg (0.1 mM Mg), and high Mg (0.4 mM Mg) treatments of barley plants. Error bars represent standard errors. DAO = days after onset of treatment (GIF 130 kb)
11104_2016_2886_MOESM2_ESM.tif (676 kb)
High Resolution Image (TIFF 676 kb)


  1. Arquero O, Barranco D, Benlloch M (2006) Potassium starvation increases stomatal conductance in olive trees. Hortscience 41:433–436Google Scholar
  2. Asada K (1999) The water-water cycle in chloroplasts: scavenging of active oxygens and dissipation of excess photons. Annu Rev Plant Physiol Plant Mol Biol 50:601–639. doi: 10.1146/annurev.arplant.50.1.601 CrossRefPubMedGoogle Scholar
  3. Benyon R (1999) Nighttime water use in an irrigated Eucalyptus grandis plantation. Tree Physiol 19:853–859. doi: 10.1093/treephys/19.13.853 CrossRefPubMedGoogle Scholar
  4. Blum A (2009) Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress. F Crop Res 112:119–123. doi: 10.1016/j.fcr.2009.03.009 CrossRefGoogle Scholar
  5. Bramley H, Turner NC, Siddique KHM (2013) Water use efficiency. In: Kole C (ed) Genomics and breeding for climate resilient crops, vol 2. Springer Science & Business Media, New York, pp. 225–269CrossRefGoogle Scholar
  6. Brueck H, Senbayram M (2009) Low nitrogen supply decreases water-use efficiency of oriental tobacco. J Plant Nutr Soil Sci 172:216–223. doi: 10.1002/jpln.200800097 CrossRefGoogle Scholar
  7. Buckley TN, Warren CR (2014) The role of mesophyll conductance in the economics of nitrogen and water use in photosynthesis. Photosynth Res 119:77–88. doi: 10.1007/s11120-013-9825-2 CrossRefPubMedGoogle Scholar
  8. Caird MA, Richards JH, Donovan LA (2007) Nighttime stomatal conductance and transpiration in C3 and C4 plants. Plant Physiol 143:4–10. doi: 10.1104/pp.106.092940 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Cakmak I (2013) Magnesium in crop production, food quality and human health. Plant Soil 368:1–4. doi: 10.1007/s11104-013-1781-2 CrossRefGoogle Scholar
  10. Cakmak I (1994) Activity of ascorbate-dependent H2O2-scavenging enzymes and leaf chlorosis are enhanced in magnesium- and potassium-deficient leaves, but not in phosphorus-deficient leaves. J Exp Bot 45:1259–1266CrossRefGoogle Scholar
  11. Cakmak I, Hengeler C, Marschner H (1994a) Changes in phloem export of sucrose in leaves in response to phosphorus, potassium and magnesium deficiency in bean plants. J Exp Bot 45:1251–1257. doi: 10.1093/jxb/45.9.1251 CrossRefGoogle Scholar
  12. Cakmak I, Hengeler C, Marschner H (1994b) Partitioning of shoot and root dry matter and carbohydrates in bean plants suffering from phosphorus, potassium and magnesium deficiency. J Exp Bot 45:1245–1250. doi: 10.1093/jxb/45.9.1245 CrossRefGoogle Scholar
  13. Cakmak I, Kirkby EA (2008) Role of magnesium in carbon partitioning and alleviating photooxidative damage. Physiol Plant 133:692–704. doi: 10.1111/j.1399-3054.2007.01042.x CrossRefPubMedGoogle Scholar
  14. Cakmak I, Marschner H (1992) Magnesium deficiency and high light intensity enhance activities of superoxide dismutase, ascorbate peroxidase, and glutathione reductase in bean leaves. Plant Physiol 98:1222–1227. doi: 10.1104/pp.98.4.1222 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Cakmak I, Yazici AM (2010) Magnesium: a forgotten element in crop production. Better Crop 94:23–25Google Scholar
  16. Carvalhais LC, Dennis PG, Fedoseyenko D, et al. (2011) Root exudation of sugars, amino acids, and organic acids by maize as affected by nitrogen, phosphorus, potassium, and iron deficiency. J Plant Nutr Soil Sci 174:3–11. doi: 10.1002/jpln.201000085 CrossRefGoogle Scholar
  17. Ceppi MG, Oukarroum A, Çiçek N, et al. (2012) The IP amplitude of the fluorescence rise OJIP is sensitive to changes in the photosystem I content of leaves: a study on plants exposed to magnesium and sulfate deficiencies, drought stress and salt stress. Physiol Plant 144:277–288. doi: 10.1111/j.1399-3054.2011.01549.x CrossRefPubMedGoogle Scholar
  18. Cernusak LA, Tcherkez G, Keitel C, et al. (2009) Viewpoint: why are non-photosynthetic tissues generally 13C enriched compared with leaves in C3 plants? Review and synthesis of current hypotheses. Funct Plant Biol 36:199–213CrossRefGoogle Scholar
  19. Cheeseman JM (2007) Hydrogen peroxide and plant stress : a challenging relationship. Plant Stress 1:4–15Google Scholar
  20. Cheeseman JM (2009) Seasonal patterns of leaf H2O2 content: reflections of leaf phenology, or environmental stress? Funct Plant Biol 36:721–731. doi: 10.1071/FP09014 CrossRefGoogle Scholar
  21. Claussen W (2002) Growth, water use efficiency, and proline content of hydroponically grown tomato plants as affected by nitrogen source and nutrient concentration. Plant Soil 247:199–209Google Scholar
  22. Ding Y, Luo W, Xu G (2006) Characterisation of magnesium nutrition and interaction of magnesium and potassium in rice. Ann Appl Biol 149:111–123. doi: 10.1111/j.1744-7348.2006.00080.x CrossRefGoogle Scholar
  23. Farooq M, Wahid A, Basra NKDFSMA (2009) Plant drought stress: effects, mechanisms and management. Agronomy for Sustainable Development. Springer Verlag, In, pp. 185–212Google Scholar
  24. Farquhar GD, MH O′L, JA B (1982) On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Aust J Plant Physiol 9:121–137CrossRefGoogle Scholar
  25. Farquhar GD, Richards RA (1984) Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Aust J Plant Physiol 11:539–552CrossRefGoogle Scholar
  26. Fischer ES, Bremer E (1993) Influence of magnesium deficiency on rates of leaf expansion, starch and sucrose accumulation, and net assimilation in Phaseolus vulgaris. Physiol Plant 89:271–276CrossRefGoogle Scholar
  27. Flexas J, Barbour MM, Brendel O, et al. (2012) Mesophyll diffusion conductance to CO2: an unappreciated central player in photosynthesis. Plant Sci 193-194:70–84. doi: 10.1016/j.plantsci.2012.05.009 CrossRefPubMedGoogle Scholar
  28. Fournier JM, Roldán ÁM, Sánchez C, et al. (2005) K+ starvation increases water uptake in whole sunflower plants. Plant Sci 168:823–829. doi: 10.1016/j.plantsci.2004.10.015 CrossRefGoogle Scholar
  29. Foyer CH, Noctor G (2011) Ascorbate and glutathione: the heart of the redox hub. Plant Physiol 155:2–18. doi: 10.1104/pp.110.167569 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Foyer CH, Shigeoka S (2011) Understanding oxidative stress and antioxidant functions to enhance photosynthesis. Plant Physiol 155:93–100. doi: 10.1104/pp.110.166181 CrossRefPubMedGoogle Scholar
  31. Giannopolitis CN, Ries SK (1977) Superoxide dismutases: occurrence in higher plants. Plant Physiol 59:309–314. doi: 10.1146/ CrossRefPubMedPubMedCentralGoogle Scholar
  32. Grzebisz W, Gransee A, Szczepaniak W, Diatta J (2013) The effects of potassium fertilization on water-use efficiency in crop plants. J Plant Nutr Soil Sci 176:355–374. doi: 10.1002/jpln.201200287 CrossRefGoogle Scholar
  33. Hall JD, Barr R, Al-Abbas AH, Crane FL (1972) The ultrastructure of chloroplasts in mineral-deficient maize leaves. Plant Physiol 50:404–409. doi: 10.1104/pp.50.3.404 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Halliwell B, Foyer CH (1978) Properties and physiological function of a glutathione reductase purified from spinach leaves by affinity chromatography. Planta 139:9–17. doi: 10.1007/BF00390803 CrossRefPubMedGoogle Scholar
  35. Hermans C, Bourgis F, Faucher M, et al. (2005) Magnesium deficiency in sugar beets alters sugar partitioning and phloem loading in young mature leaves. Planta 220:541–549. doi: 10.1007/s00425-004-1376-5 CrossRefPubMedGoogle Scholar
  36. Hermans C, Conn SJ, Chen J, et al. (2013) An update on magnesium homeostasis mechanisms in plants. Met Integr biometal Sci 5:1170–1183. doi: 10.1039/c3mt20223b CrossRefGoogle Scholar
  37. Hermans C, Johnson GN, Strasser RJ, Verbruggen N (2004) Physiological characterisation of magnesium deficiency in sugar beet: acclimation to low magnesium differentially affects photosystems I and II. Planta 220:344–355. doi: 10.1007/s00425-004-1340-4 CrossRefPubMedGoogle Scholar
  38. Hermans C, Verbruggen N (2005) Physiological characterization of Mg deficiency in Arabidopsis thaliana. J Exp Bot 56:2153–2161. doi: 10.1093/jxb/eri215 CrossRefPubMedGoogle Scholar
  39. Hu X, Zhang A, Zhang J, Jiang M (2006) Abscisic acid is a key inducer of hydrogen peroxide production in leaves of maize plants exposed to water stress. Plant Cell Physiol 47:1484–1495. doi: 10.1093/pcp/pcl014 CrossRefPubMedGoogle Scholar
  40. Jang JC, Sheen J (1994) Sugar sensing in higher-plants. Plant Cell 6:1665–1679. doi: 10.1105/tpc.6.11.1665 CrossRefPubMedPubMedCentralGoogle Scholar
  41. Jezek M, Geilfus C-M, Bayer A, Mühling K-H (2015) Photosynthetic capacity, nutrient status, and growth of maize (Zea mays L.) upon MgSO4 leaf-application. Front Plant Sci 5:1–10. doi: 10.3389/fpls.2014.00781 CrossRefGoogle Scholar
  42. Jones DL, Nguyen C, Finlay RD (2009) Carbon flow in the rhizosphere: carbon trading at the soil–root interface. Plant Soil 321:5–33. doi: 10.1007/s11104-009-9925-0 CrossRefGoogle Scholar
  43. Kobayashi NI, Saito T, Iwata N, et al. (2013) Leaf senescence in rice due to magnesium deficiency mediated defect in transpiration rate before sugar accumulation and chlorosis. Physiol Plant 148:490–501. doi: 10.1111/ppl.12003 CrossRefPubMedGoogle Scholar
  44. Kraffczyk I, Trolldenier G, Beringer H (1984) Soluble root exudates of maize: influence of potassium supply and rhizosphere microorganisms. Soil Biol Biochem 16:315–322CrossRefGoogle Scholar
  45. Kuzyakov Y, Xu X (2013) Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol 198:656–669. doi: 10.1111/nph.12235 CrossRefPubMedGoogle Scholar
  46. Laing W, Greer D, Sun O, et al. (2000) Physiological impacts of Mg deficiency in Pinus radiata: growth and photosynthesis. New Phytol 146:47–57. doi: 10.1046/j.1469-8137.2000.00616.x CrossRefGoogle Scholar
  47. Lewis JD, Phillips NG, Logan BA, et al. (2011) Leaf photosynthesis, respiration and stomatal conductance in six Eucalyptus species native to mesic and xeric environments growing in a common garden. Tree Physiol 31:997–1006. doi: 10.1093/treephys/tpr087 CrossRefPubMedGoogle Scholar
  48. Li L, Tutone AF, Drummond RSM, et al. (2001) A novel family of magnesium transport genes in Arabidopsis. Plant Cell 13:2761–2775. doi: 10.1105/tpc.010352 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Medrano H, Tomás M, Martorell S, et al. (2015) From leaf to whole-plant water use efficiency (WUE) in complex canopies: limitations of leaf WUE as a selection target. Crop J 3:220–228. doi: 10.1016/j.cj.2015.04.002 CrossRefGoogle Scholar
  50. Meireles da Silva D, Brandão IR, Alves JD, et al. (2014) Physiological and biochemical impacts of magnesium-deficiency in two cultivars of coffee. Plant Soil 382:133–150. doi: 10.1007/s11104-014-2150-5 CrossRefGoogle Scholar
  51. Mengutay M, Ceylan Y, Kutman UB, Cakmak I (2013) Adequate magnesium nutrition mitigates adverse effects of heat stress on maize and wheat. Plant Soil 368:57–72. doi: 10.1007/s11104-013-1761-6 CrossRefGoogle Scholar
  52. Møller IM, Jensen PE, Hansson A (2007) Oxidative modifications to cellular components in plants. Annu Rev Plant Biol 58:459–481. doi: 10.1146/annurev.arplant.58.032806.103946 CrossRefPubMedGoogle Scholar
  53. Mühling KH, Schubert S, Mengel K (1993) Role of plasmalemma H+ ATPase in sugar retention by roots of intact maize and field bean plants. Z Pflanzenernähr Bodenkd 156:155–161CrossRefGoogle Scholar
  54. Murata N (1969) Control of excitation transfer in photosynthesis. II. Magnesium ion dependent distribution of excitation energy between two pigment systems in spinach chloroplasts. Biochim Biophys Acta 189:171–181CrossRefPubMedGoogle Scholar
  55. Nakano Y, Asada K (1981) Hydrogen peroxide is scavenged by ascorbat-specific peroxidase in spinach chloroplasts. Plant Cell Physiol 22:867–880Google Scholar
  56. Palmgren MG (2001) Plant plasma membrane H+ − ATPases: powerhouses for nutrient uptake. Annu Rev Plant Physiol Plant Mol Biol 52:817–845. doi: 10.1146/annurev.arplant.52.1.817 CrossRefPubMedGoogle Scholar
  57. Peaslee D, Moss N (1966) Photosynthesis in K- and Mg-deficient maize leaves. Soil Sci Soc Am Proc 30:220–223CrossRefGoogle Scholar
  58. Polle A (2001) Dissecting the superoxide dismutase-ascorbate-glutathione-pathway in chloroplasts by metabolic modeling. Computer simulations as a step towards flux analysis. Plant Physiol 126:445–462. doi: 10.1104/pp.126.1.445 CrossRefPubMedPubMedCentralGoogle Scholar
  59. Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  60. Pachauri RK, Meyer LA (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate ChangeGoogle Scholar
  61. Rasband WS (1997) ImageJ. U. S, National Institutes of Health, Bethesda, Maryland, USAGoogle Scholar
  62. Rasheed F, Dreyer E, Richard B, et al. (2013) Genotype differences in 13C discrimination between atmosphere and leaf matter match differences in transpiration efficiency at leaf and whole-plant levels in hybrid Populus deltoides ×nigra. Plant Cell Environ 36:87–102CrossRefPubMedGoogle Scholar
  63. Ripullone F, Lauteri M, Grassi G, et al. (2004) Variation in nitrogen supply changes water-use efficiency of Pseudotsuga menziesii and Populus x euroamericana; a comparison of three approaches to determine water-use efficiency. Tree Physiol 24:671–679. doi: 10.1093/treephys/24.6.671 CrossRefPubMedGoogle Scholar
  64. Römheld V, Kirkby EA (2010) Research on potassium in agriculture: needs and prospects. Plant Soil 335:155–180. doi: 10.1007/s11104-010-0520-1 CrossRefGoogle Scholar
  65. Seibt U, Rajabi A, Griffiths H, Berry JA (2008) Carbon isotopes and water use efficiency: sense and sensitivity. Oecologia 155:441–454. doi: 10.1007/s00442-007-0932-7 CrossRefPubMedGoogle Scholar
  66. Senbayram M, Gransee A, Wahle V, Thiel H (2015a) Role of magnesium fertilisers in agriculture: plant – soil continuum. Crop Pasture Sci 66:1219–1229CrossRefGoogle Scholar
  67. Senbayram M, Tränkner M, Dittert K (2015b) Brück H. Daytime leaf water use efficiency does not explain the relationship between plant N status and biomass water-use efficiency of tobacco under non-limiting water supply 178:682–692Google Scholar
  68. Shangguan ZP, Shao MA, Dyckmans J (2000) Nitrogen nutrition and water stress effects on leaf photosynthetic gas exchange and water use efficiency in winter wheat. Environ Exp Bot 44:141–149. doi: 10.1016/S0098-8472(00)00064-2 CrossRefPubMedGoogle Scholar
  69. Shaul O (2002) Magnesium transport and function in plants: the tip of the iceberg. Biometals 15:309–323. doi: 10.1023/a:1016091118585 CrossRefPubMedGoogle Scholar
  70. Smith BN, Epstein S (1971) Two categories of 13C/12C ratios for higher plants. Plant Physiol 47:380–384CrossRefPubMedPubMedCentralGoogle Scholar
  71. Snyder KA, Richards JH, Donovan LA (2003) Night-time conductance in C3 and C4 species: do plants lose water at night? J Exp Bot 54:861–865. doi: 10.1093/jxb/erg082 CrossRefPubMedGoogle Scholar
  72. Soolanayakanahally RY, Guy RD, Silim SN, et al. (2009) Enhanced assimilation rate and water use efficiency with latitude through increased photosynthetic capacity and internal conductance in balsam poplar (Populus balsamifera L.). Plant Cell Environ 32:1821–1832CrossRefPubMedGoogle Scholar
  73. Stys D (1995) Stacking and separation of photosystem I and photosystem II in plant thylakoid membranes: a physico-chemical view. Physiol Plant 95:651–657. doi: 10.1034/j.1399-3054.1995.950421.x CrossRefGoogle Scholar
  74. Sun OJ, Gielen GJ, Sands R, et al. (2001) Growth, Mg nutrition and photosynthetic activity in Pinus radiata: evidence that NaCl addition counteracts the impact of low Mg supply. Trees - Struct Funct 15:335–340. doi: 10.1007/s004680100111 CrossRefGoogle Scholar
  75. Tallec T, Béziat P, Jarosz N, et al. (2013) Crops’ water use efficiencies in temperate climate: comparison of stand, ecosystem and agronomical approaches. Agric For Meteorol 168:69–81. doi: 10.1016/j.agrformet.2012.07.008 CrossRefGoogle Scholar
  76. Tang N, Li Y, Chen LS (2012) Magnesium deficiency-induced impairment of photosynthesis in leaves of fruiting Citrus reticulata trees accompanied by up-regulation of antioxidant metabolism to avoid photo-oxidative damage. J Plant Nutr Soil Sci 175:784–793. doi: 10.1002/jpln.201100329 CrossRefGoogle Scholar
  77. Terry N, Ulrich A (1974) Effects of magnesium deficiency on the photosynthesis and respiration of leaves of sugar beet. Plant Physiol 54:379–381. doi: 10.1104/pp.54.3.379 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Tewari RK, Kumar P, Nand Sharma P (2006) Magnesium deficiency induced oxidative stress and antioxidant responses in mulberry plants. Sci Hortic (Amsterdam) 108:7–14. doi: 10.1016/j.scienta.2005.12.006 CrossRefGoogle Scholar
  79. Tewari RK, Kumar P, Tewari N, et al. (2004) Macronutrient deficiencies and differential antioxidant responses - influence on the activity and expression of superoxide dismutase in maize. Plant Sci 166:687–694. doi: 10.1016/j.plantsci.2003.11.004 CrossRefGoogle Scholar
  80. Tomás M, Medrano H, Pou a., et al (2012) Water-use efficiency in grapevine cultivars grown under controlled conditions: effects of water stress at the leaf and whole-plant level. Aust J Grape Wine Res 18:164–172. doi: 10.1111/j.1755-0238.2012.00184.x
  81. Verbruggen N, Hermans C (2013) Physiological and molecular responses to magnesium nutritional imbalance in plants. Plant Soil 368:87–99. doi: 10.1007/s11104-013-1589-0 CrossRefGoogle Scholar
  82. Wang P, Song CP (2008) Guard-cell signalling for hydrogen peroxide and abscisic acid. New Phytol 178:703–718. doi: 10.1111/j.1469-8137.2008.02431.x CrossRefPubMedGoogle Scholar
  83. Wang Y, Zhang X, Liu X, et al. (2013) The effects of nitrogen supply and water regime on instantaneous WUE, time-integrated WUE and carbon isotope discrimination in winter wheat. F Crop Res 144:236–244CrossRefGoogle Scholar
  84. Warren CR, Adams MA (2006) Internal conductance does not scale with photosynthetic capacity: implications for carbon isotope discrimination and the economics of water and nitrogen use in photosynthesis. Plant Cell Environ 29:192–201CrossRefPubMedGoogle Scholar
  85. Wilkinson SR, Welch RM, Mayland HF, Grunes DL (1990) Magnesium in plants: uptake, distribution, function, and utilization by man and animals. Met Ions Biol Syst 26:33–56Google Scholar
  86. Wolff SP (1994) Ferrous ion oxidation in presence of ferric ion indicator xylenol orange for measurement of hydroperoxides. Methods Enzymol 233:182–189CrossRefGoogle Scholar
  87. Yang GH, Yang LT, Jiang HX, et al. (2012) Physiological impacts of magnesium-deficiency in Citrus seedlings: photosynthesis, antioxidant system and carbohydrates. Trees - Struct Funct 26:1237–1250. doi: 10.1007/s00468-012-0699-2 CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Merle Tränkner
    • 1
    • 4
    Email author
  • Bálint Jákli
    • 1
    • 4
  • Ershad Tavakol
    • 1
    • 4
  • Christoph-Martin Geilfus
    • 2
  • Ismail Cakmak
    • 3
  • Klaus Dittert
    • 1
    • 4
  • Mehmet Senbayram
    • 1
    • 5
  1. 1.Institute of Applied Plant NutritionGoettingenGermany
  2. 2.Facility for Systems Biology based Mass Spectrometry, Division of Crop BiotechnicsKU LeuvenLeuvenBelgium
  3. 3.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey
  4. 4.Department of Crop Science, Section of Plant Nutrition & Crop PhysiologyUniversity of GoettingenGoettingenGermany
  5. 5.Institute of Plant Nutrition and Soil Science, Faculty of AgricultureHarran UniversitySanliUrfaTurkey

Personalised recommendations