Advertisement

Acta Physiologiae Plantarum

, Volume 35, Issue 2, pp 549–565 | Cite as

The relations between drought susceptibility index based on grain yield (DSIGY) and key physiological seedling traits in maize and triticale genotypes

  • Maciej T. GrzesiakEmail author
  • Piotr Waligórski
  • Franciszek Janowiak
  • Izabela Marcińska
  • Katarzyna Hura
  • Piotr Szczyrek
  • Tomasz Głąb
Open Access
Original Paper

Abstract

The physiological reasons for the differences in sensitivity of C3 and C4 plant species to environmental stresses have not been thoroughly explained. In this study the effects of drought stress on the growth and selected physiological traits were examined in the seedlings of 13 single cross maize (C4 plant) hybrids and 11 spring triticale (C3 plant) breeding lines and varieties differing in drought sensitivity. For plants in the seedling stage the results demonstrated a genetic variation in dry matter accumulation of shoots and roots (DWS, DWR), number (N) and length (L) of particular components (seminal, seminal adventitious, nodal) of the root system, membrane injury by soil drought (LID), osmotic and high temperature stress (LIOS, LIHT), water potential (ψ), water loss (WL), grain germination in osmotic stress (FG, PI), and seedling survival (SS). Seedlings grown under moderate soil drought showed a decrease in dry matter of the top parts and roots and a decrease in the length of seminal, seminal adventitious and nodal roots in comparison to seedlings grown in control conditions. The observed harmful effects of drought stress were more distinct in drought sensitive genotypes. Used in this paper drought susceptibility indexes (DSIGY) were calculated in other experiment by determining the changes in grain yield (GY) under two soil moisture levels (irrigated and drought). The variation of DSIGY for maize ranges from 0.381 to 0.650 and for triticale from 0.354 to 0.578. The correlations between DSIGY and laboratory tests (LI, FG, SS) confirmed that they are good indicators of drought tolerance in plants. The highest values of genetic variation were observed in LI, DWS, SS and WL and the lowest in the measurements of ψ FG, PI, LS, LSA and LN. The correlation coefficients between LIOS and LIHT tests were, in most of the considered cases, statistically significant, which indicates that in maize and triticale the mechanisms of membrane injury caused by simulated drought or high temperature are physiologically similar. It can be concluded that an approach to the breeding of maize and triticale for drought tolerance using these tests can be implemented on the basis of separate selection for each trait or for all of them simultaneously. In that case, it would be necessary to determine the importance of the trait in relation to growth phase, drought timing and level, as well as its associations with morphological traits contributing to drought tolerance. The obtained values of the correlation coefficient between laboratory tests suggest that the same physiological traits may be applied as selection criteria in drought tolerance of maize and triticale genotypes.

Keywords

Triticale-(x Triticosecale Witt) Maize-(Zea mays L.) Soil drought Osmotic stress Germination Membrane stability Roots Water potential Water loss 

Abbreviations

DSIGY

Drought susceptibility index

C

Control

D

Drought

SI

Stress index

FWC

Field water capacity

FG

Final germination

PI

Promptness index

SS

Seedling survival

LI

Membrane injury index

Ψ

Leaf water potential

WL

Water loss

DWR

Root dry matter

DWS

Shoot dry matter

FW

Leaf fresh weight

DW

Leaf dry matter

LS, LSA, LN

Length of seminal, seminal adventitious and nodal roots, respectively

NN

Number of nodal roots

Introduction

As drought is the most important environmental phenomenon affecting a plant’s growth, development and crop yield, considerable progress has been made in understanding the changes in physiological processes caused by drought stress. It has been shown that the physiological responses of plants to drought stress are extremely complex and vary with plant species as well as with the degree and time of the exposure to drought (Levitt 1980; Bennett 1990; Evans et al. 1990, 1991; Jones 1993; Reynolds 2002; King 2011). Plants develop different morphological, physiological and biochemical mechanisms which inhibit or remove the harmful effects of drought stresses (Sullivan and Ross 1979; Boyer 1982; Larsson and Górny 1988; Chaves et al. 2002; Reynolds et al. 1998; Asharaf 2010). Drought tolerance of a plant species is usually determined by the plant’s genes and also by morphological, phonological, physiological, and biochemical traits. The responses of plants to drought stress depend on the species, genotype, plant age, level and duration of drought, and physical parameters of the soil. Differences in tolerance to drought are known to exist within genotypes of plant species and were found in many studies, e.g. in maize (Martinielio and Lorenzoni 1985; Lorens et al. 1987; Grzesiak 1990; Grzesiak et al. 2012), wheat (Winter et al. 1988; Reynolds et al. 1998; Paknejad et al. 2007), rape seed (Richards and Thurling 1978), oat (Larsson and Górny 1988), coconut (Gomez et al. 2008) and triticale (Royo et al. 2000; Grzesiak et al. 2012).

Methods of evaluating the degree of drought tolerance allow for a direct or indirect estimation of the various physiological, biochemical or morphological traits of the examined genotypes. Measurements of different physiological processes of plant response to drought provide important information about the reactions of the plant intended to remove or to reduce the harmful effects of water deficit in the soil or plant tissues. Techniques of screening for drought tolerance were devised by selecting genotypes in a field or greenhouse study. Conducting field experiments is necessary for the verification of the drought tolerance estimated on the basis of physiological laboratory tests (Grzesiak 1990; Richards 1991; Kpoghomou et al. 1990). For proper field testing a number of methodological problems must be solved to enable water content in the soil to be controlled by irrigation or by limiting the inflow of water from rainfall. The relations between the plant yield obtained under conditions of drought and that obtained under conditions of optimal soil moistening were preferred among the field indices of drought tolerance. Such tests, however, are not accurate enough or too simplified to show important relations between the crop forming processes and soil–water-plant relationship. A more precise quantitative formulation of this relationship can be found in the studies by Fischer and Maurer (1978), Hanson and Nelson (1985), Winter et al. (1988), Stanley (1990) and in FAO reports by Doorenbos and Pruit (1977), Doorenbos and Kassam (1986).

Methods of screening for drought tolerance within a large number of genotypes should be easy, rapid and inexpensive (Hanson and Nelson 1985; Palta 1990; Zagdańska 1992). It is also necessary for the laboratory testing method to be characterized by a significant correlation with drought resistance observed under field conditions (Sullivan and Ross 1979; Blum et al. 1980; Bouslama and Schapauch 1984; Hanson and Nelson 1985; Kpoghomou et al. 1990; Chaves et al. 2002; Grzesiak et al. 2012). The most important laboratory methods suggested for screening for drought tolerance in crop plants were: germination in osmotic substances (mannitol, PEG), growth or survival of young seedlings subjected to soil or simulated water stress and high temperature stress (Sullivan and Ross 1979; Blum and Ebercon 1981; Martinielio and Lorenzoni 1985), leaf injury, leaf water content, leaf temperature and parameters of leaf gaseous exchange (Passioura et al. 1993; Farquhar et al. 1993; Dubey 1997). Other traits that may be promising as screening traits are different parameters of chlorophyll fluorescence (Reynolds 2002; Hura et al. 2007) and associations between dark respiration under drought conditions and heat tolerance of sorghum lines (Gerik and Eastin 1985) and of wheat (Reynolds et al. 1998).

The physiological reasons for the differences in sensitivity of C3 and C4 plant species to environmental stresses have not been thoroughly explained and understood (Edwards and Ku 1987; Medrano et al. 2002; Nayyar and Gupta 2006; Lopes et al. 2011). Maize and triticale have different types of photosynthesis (C3—in triticale, C4—in maize), different metabolic pathways and structure of bundle sheath chloroplasts (Kranz syndrome). According to Iijima and Kono (1991), cereal species develop two types of root system, depending on the angle of the growth of branches (lateral roots) and their distribution in the soil profile. For triticale, the root system structure is “concentrated” whereas it is “scattered” for maize. Maize and spring triticale appear to be sensitive to drought stress during grain germination, seedling emergence, early vegetative growth and pollination. Moreover, both maize and triticale are important crops widely cultivated throughout the world (Fageria et al. 2006).

The objectives of this study were to evaluate the variation of selected physiological characteristics of maize and triticale seedlings grown in drought conditions and to compare them to variations in drought susceptibility index based on grain yield (DSIGY) of plants grown in a stressed environment under field conditions and then determine which tests are most useful for screening drought resistance genotypes.

Materials and methods

Plant materials

Experiments were conducted on 13 single-cross maize hybrids and on 4 strains and 7 cultivars of spring triticale. Maize grains were obtained from Pioneer Overseas GmbH (Austria), Pioneer Saaten (Poland), Garst Seed Company (USA), Agriculture Canada and SAMPLO Holding (Slovakia). Triticale grains of breeding lines were obtained from DANKO in Choryn and cultivars from IHAR, Małyszyn (Poland). The choice of the maize hybrids and triticale lines and cultivars to be examined was done on the basis of the information on the effect of drought on plant yield received from breeders. Plant materials and drought susceptibility indexes (DSIGY) according to Grzesiak et al. (2012) are listed in Fig. 1.
Fig. 1

Maize and triticale genotypes ordered according to their Drought Susceptibility Index (DSIGY)

Experimental conditions

The experiment was carried out in a greenhouse under the conditions of 25/20 °C day/night temperature and 65 % relative humidity. Plants were grown in Mitscherlich pots and root-boxes filled with a mixture of garden soil, peat and sand (1:1:1). Air-dried soil substrate was sieved in a 0.25 cm mesh. Soil substrate pH was 7.1 and the percent of organic material was 0.7 %. For the determination of root length and number of root system components, seedlings were grown in root-boxes, which enabled non-destructive isolation of all compartments of the root system (Kono et al. 1987). A set for the “root-box and pin board method” consists of: a Plexiglas box (width—0.25 m, depth—0.40 m, thickness—0.02 m), a pin board for sampling the root system, and a polyethylene sheet (envelope) for handling and preserving the root system. In root-boxes soil compaction level was 1.30 g cm−3. Mechanical impedance in soil substrate was measured with penetrometer DIK-5520 (Daiki Rika Kogyo Co. Ltd, Japan).

Field soil water capacity (FWC) for soil mixture was determined according to Kopecky methods. Air-dried soil samples were placed inside metal cylinders, with a 1 mm hole at the bottom. The volume for the samples was 100 cm3. Cylinders with the samples were placed inside a container with water for 30 min. After 8 h, maximal soil water content in the samples was 0.43 (g cm−3) and after 48 h it decreased to 0.21 (g cm−3). Following Hillel and van Bavel (1976), those last values were assumed to be 100 % of soil field water capacity (FWC). The pots and root-boxes were weighed every day, and the amount of water loss through transpiration was added to maintain the original weight in each treatment. For control (C) treatment soil water content was maintained from sowing for 42 days at the level of 65–70 % FWC. Soil water content in the root-box experiment for drought (D) treatment was kept at the level of 30–35 % FWC from the 21st to 42nd day. Similarly, in the pot experiment from the 21st to 42nd day it was kept at the level of 30–35 % FWC for drought treatment D35 and at the level 15–20 % FWC for treatment D20. In order to limit water evaporation from pots and root-boxes, soil surface was covered with 1 cm layer of ground Styrofoam. A single pregerminated grain was planted at the depth of 3–4 cm. After 42 days of plants’ growth under C treatment Zadoks scale was about 17 or 18 for maize and about 16 or 17 for triticale.

On the 42th day seedlings grown in pots were used for measurements of leaf water potential, membrane injury and water loss in the excised leaf. For each species (2), genotypes (13 or 11) and treatments (3) 5 pots with 3 plants were used (n = 5). Similarly, seedlings grown in root-boxes were cut into top parts and roots for the determination of dry weigh (DWS, DWR) and number and length of particular components of the root system. The roots were sampled after the soil from the pot had been washed away by a gentle stream of water. After the measurements root samples were preserved in a FAA (formalin, acetic acid, and ethanol) solution. For each species (2), genotypes (13 or 11) and treatments (2) 4 root-boxes with 1 plants were used (n = 4).

Measurements

Germination and seedling survival (FG, PI, SS). Twenty grains of each genotype were germinated under 0.00, −0.47, −0.85 and −1.30 MPa of osmotic stress. Grains of the tested genotypes were surface sterilized in 70 % ethanol for 5 min and placed on petri dishes and incubated in an air-conditioned growth cabinet at the temperature of 25 °C. Osmotic stress was simulated using mannitol solutions (C6H1406, M.W. 182.17), Lobe Chemia. The concentration of solutions at the desired chemical water potential (ψ) was calculated according to Michel et al. (1983), the control was distilled water (ψ = 0.0 MPa). Germination was recorded when the radicle reached 5 mm in length. There were 4 replications for each treatments. Counts of germinated grains were made each day to compute the final germination percentage (FG) and promptness index (PI) and calculated as follows:
$$ {\text{FG = }}\left( {{\text{nd}}_{\text{l4}} \cdot 1 0 0} \right) \cdot 2 0^{ - 1} $$
$$ {\text{PI}} = \left( {{\text{nd}}_{2} \cdot 1.00} \right) + \left( {{\text{nd}}_{4} \cdot 0.75} \right) + \left( {{\text{nd}}_{6} \cdot 0.50} \right) + \left( {{\text{nd}}_{8} \cdot 0.25} \right) $$
where ndx = number of germinated grains by the xth day, Seedling survival (SS), 25 germinated grains were planted in 5 pots and after 14 days the number of growing seedling was recorded, and seedling survival index (SS) was calculated as follows:
$$ {\text{SS}} = {\text{number}}\,{\text{of}}\,{\text{living}}\,{\text{seedlings}} \cdot 25^{ - 1} $$

Leaf water potential (ψ) was measured with thermocouple psychrometer HR 33T (Wescor Inc., Logan, USA) in “dew point” mode, equipped with sample chamber C-52 SF and digital multimeter Metex M-3640 D. Measurements were taken on leaf discs—diameter of 0.3 cm for triticale and 0.5 cm for maize—and immediately placed inside the psychrometer chamber and left to balance temperature and water vapor equilibrium for 30 min before measurements. For each treatments there were 5 replications. Psychrometric readout and ψ determination were made as described by Johnson and Brown (1977).

Excised-leaves water loss (WL) was measured only for control treatment (C) of each maize and triticale genotype. Leaves were cut from the upper half of the plant, weighed (FW0) and left to desiccate at 25 °C in the dark. After 12 and 24 h samples were reweighed (FW12, FW24) and next oven dried at 70 °C and weighed again for the determination of dry weight of samples (DW). There were 5 replications. Water loss of excised leaves (WL) was calculated by the following formula:
$$ {\text{WL}} = \left( {{\text{FW}}_{0} - {\text{FW}}_{12} \,{\text{or}}\,{\text{FW}}_{24} } \right){\text{DW}}^{ - 1} $$

Dry weight of shoots (DWS) and roots (DWR) was sampled in each root-box and was determined on the 42nd day after sowing through drying at 65 °C for 72 h.

Number (N) and length (L) of seedling root components (seminal-LS, seminal adventitious-LSA, nodal-NN·LN) was measured with DELTA-T SCAN (England) analyzer.

Relative loss of intracellular electrolytes from leaf tissues (LI) was measured with the conductivity method using conductivity meter OK-102/1 (Radelkis, Hungary), according to the procedure and formula described by Sullivan and Ross (1979) and Blum et al. (1980).
$$ {\text{LI}} = 1 - \left[ {1 - \left( {{\text{T}}_{\text{I}} - {\text{T}}_{2} } \right)^{ - 1} } \right]\left[ {1 \, - \left( {{\text{C}}_{1} .{\text{C}}_{2} } \right)^{ - 1} } \right]^{ - 1} 100 $$
where C and T refer to the conductivity of control and treatment solutions, respectively, and subscript 1 and 2 refer to initial and final conductance, respectively.

Nine leaf discs (0.5 cm diameter for maize and 0.3 cm for triticale) were cut from leaves and immersed in test tubes containing 30 cm3 redistilled water. After 24 h initial conductance measurements were taken. Final conductance measurements were taken after autoclaving all tubes at 110 °C for 15 min and cooling them to room temperature.

In the experiment additional conductivity tests of leaf injury by simulated drought (LIOS) and by high temperature (LIHT) were made. For these tests leaf discs were cut only from control (C) plants. For LIOS leaf discs were immersed for 12 h in test tubes with 30 cm3 of redistilled water (control 0.0 MPa) or 30 cm3 of mannitol solution (treatment −0.47, −0.85 and −1.30 MPa). High temperature stress (LIHT) was imposed by immersing leaf discs for 1 h in test tube with 30 cm3 of redistilled water by placing them in well-stirred water bath at three temperatures: 25 °C (control), 35 and 45 °C (treatments). There were 5 replications for each treatments.

All measurements except germination tests (FG, PI, SS) were made after 42 days of seedlings’ growth. Samples for measurements of leaf water potential (ψ), relative loss of intracellular electrolytes (LI), and water loss of excised leaves (WL) were taken between 11:00 and 13:00 on most recent fully expanded leaf. Results of all measurements are presented as stress index (SI) which was calculated as indicated below:
$$ {\text{SI}} = {\text{treatment}}\,{\text{value}} \cdot {\text{control}}\,{\text{value}}^{ - 1}. $$

Statistical analysis

All data were analyzed by one-way ANOVA and LSD tests (p < 0.05) using Statistica 10.0 (StatSoft, Inc., USA). Linear correlation analyses were used to determine the relationship between drought susceptibility index (DSIGY) and stress indices (SI) obtained in laboratory tests. Also correlation coefficients were calculated for the determination of the relationship between and within laboratory tests.

Results and discussion

Drought susceptibility indexes (DSIGY) of maize and triticale genotypes

According to our earlier research (Grzesiak et al. 2012), drought susceptibility indexes (DSIGY) for maize and triticale genotypes were calculated by determining the changes in grain yield (GY) under two soil moisture levels (irrigated and drought). Variation of DSIGY for maize ranges from 0.381 to 0.650 and for triticale from 0.354 to 0.578. The values of DSIGY made it possible to rank the examined maize and triticale genotypes according to their drought tolerance. In the maize hybrids the drought resistant group (0.381 < DSIGY > 0.439) comprises the hybrids Tina, Garst 8344, Pioneer 3925 and Pioneer 3957, while the drought sensitive group (0.607 < DSIGY > 0.650) comprises the hybrids Ankora, Garst 8702, Garst 8388 and Garst R5515. For triticale the drought resistant group (0.354 < DSIGY > 0.419) comprises the lines CHD 247 and CHD 220, and cultivars Migo and Wanad, while the drought sensitive group (0.544 < DSIGY > 0.578) comprises the strains CHD 147 and CHD 12, and cultivars Mieszko and Maja (Fig. 1).

Germination under simulated drought conditions (FG, PI) and seedling survival (SS)

In the control treatment (0.0 MPa), maize and triticale genotypes did not differ in the final germination index (FG) and the correlation coefficients between FG and DSIGY were insignificant. The imposed drought from −0.43 to 1.30 MPa caused a decrease in the values of stress indexes of FG and the correlation coefficients between FG and DSIGY were statistically significant for maize in −0.43 MPa treatment and for triticale in −0.85 and −1.30 MPa treatments (Table 1). Differences between maize and triticale genotypes in promptness index (PI) values in the control were statistically significant only for maize, whilst the correlation coefficient with DSIGY was statistically significant for both species. As with drought FG, osmotic stress caused a decrease in the values of PI and the correlation coefficients between PI and DSIGY were statistically significant for maize in −0.43 and −0.85 MPa treatments, but for triticale only in −1.30 MPa treatment (Table 2). The results of seedling survival (SS) after grain germination in different water potential of mannitol solutions are presented in Table 3. Germination under osmotic stress conditions (−0.43, −0.85. −1.30 MPa) caused a decrease in the number of live seedlings in maize to 86, 79 and 76 %, respectively, and for triticale to 97, 86 and 84 %, respectively. Correlation coefficients between DSIGY and the values of stress index of seedling survival were statistically significant in −0.43, −0.85 and −1.30 MPa treatments of maize genotypes, but for triticale only −0.85 and −1.30 MPa treatments were affected.
Table 1

Final germination index (FG) of maize and triticale genotypes after 14 days germination in mannitol solutions differing in water potential

Genotype

Control (C)

Stress index (SI) of osmotic stress treatments

0.0 MPa (%)

−0.43 MPa

−0.85 MPa

−1.30 MPa

Maize

Ankora

98.5

0.818

0.698

0.627

Garst 8702

100.0

0.862

0.632

0.480

Garst 8388

98.1

0.826

0.654

0.502

Garst R5515

99.2

0.808

0.701

0.557

Pioneer-38-F-70

97.5

0.852

0.739

0.668

Nova

99.2

0.863

0.757

0.686

Pioneer-39-G-12

99.5

0.847

0.702

0.631

Pioneer-39-R-10

98.9

0.860

0.691

0.620

Funk`s G4083

99.5

0.876

0.726

0.655

Pioneer 3957

98.9

0.841

0.740

0.669

Pioneer 3925

100.0

0.890

0.721

0.651

Garst 8344

98.0

0.879

0.668

0.597

Tina

100.0

0.872

0.777

0.652

    Mean

99.0

0.854

0.708

0.615

    Range

97.5–100.0

0.808–0.890

0.632–0.777

0.488–0.686

   LSD0,05

1.8

0.025

0.028

0.067

     CV

0.8

2.9

5.8

10.5

  r versus DSIGY

−0.259NS

−0.728**

−0.419NS

−0.473NS

Triticale

Maja

100.0

0.989

0.809

0.679

CHD-147

100.0

0.956

0.713

0.583

Mieszko

98.5

0.984

0.801

0.620

CHD-12

99.9

0.943

0.723

0.593

Kargo

98.7

0.984

0.801

0.619

MAH

98.5

0.996

0.813

0.681

Gabo

99.2

0.990

0.808

0.677

Wanad

99.6

0.985

0.804

0.744

Migo

99.0

0.985

0.803

0.672

CHD-220

100.0

0.989

0.842

0.712

CHD-247

98.5

0.995

0.863

0.713

     Mean

99.3

0.981

0.798

0.663

     Range

98.5–100.0

0.943–0.996

0.713–0.863

0.583–0.744

    LSD0,05

1.31

0.016

0.047

0.077

      CV

0.7

1.7

5.5

7.9

   r versus DSIGY

0.203NS

−0.463NS

−0.616*

−0.719*

Results for osmotic stress treatments are shown as a stress index (SI). Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) between measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 4)

Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS, r-Correlation coefficient not significant

*, ** r-Correlation coefficient significant at the 0.05 or 0.01 level, respectively

Table 2

Promptness index (PI) of maize and triticale genotypes after 8 days germination in mannitol solutions differing in water potential

Genotype

Control (C)

Stress index (SI) of osmotic stress treatments

0.0 MPa (%)

−0.43 MPa

−0.85 MPa

−1.30 MPa

Maize

Ankora

17.0

0.682

0.659

0.635

Garst 8702

16.4

0.744

0.659

0.634

Garst 8388

17.1

0.713

0.614

0.608

Garst R5515

17.6

0.722

0.642

0.619

Pioneer-38-F-70

18.0

0.783

0.611

0.572

Nova

16.9

0.769

0.686

0.604

Pioneer-39-G-12

18.1

0.823

0.707

0.575

Pioneer-39-R-10

17.6

0.778

0.699

0.619

Funk`s G4083

17.9

0.726

0.704

0.626

Pioneer 3957

17.9

0.810

0.670

0.570

Pioneer 3925

18.1

0.818

0.652

0.575

Garst 8344

17.8

0.781

0.702

0.624

Tina

17.8

0.781

0.702

0.624

    Mean

17.6

0.764

0.670

0.606

    Range

16.0–17.9

0.682–0.823

0.611–0.707

0.570–0.635

   LSD0,05

1.11

0.130

0.078

0.020

     CV

3.0

5.7

5.0

4.1

  r versus DSIGY

−0.632*

−0.592*

−0.574*

0.163NS

Triticale

Maja

14.8

0.946

0.905

0.757

CHD-147

17.8

0.837

0.618

0.506

Mieszko

15.0

0.933

0.847

0.627

CHD-12

18.1

0.773

0.718

0.497

Kargo

16.1

0.981

0.832

0.714

MAH

14.9

0.980

0.866

0.725

Gabo

15.9

0.987

0.818

0.711

Wanad

19.0

0.80

0.711

0.605

Migo

16.0

0.931

0.831

0.781

CHD-220

15.9

0.994

0.937

0.755

CHD-247

16.5

0.958

0.909

0.788

    Mean

16.3

0.920

0.817

0.679

    Range

14.8–19.0

0.773–0.987

0.618–0.937

0.497–0.788

   LSD0.05

1.80

0.122

0.131

0.107

     CV

8.4

8.6

11.9

15.4

  r versus DSIGY

−0.121NS

−0.229NS

−0.333NS

−0.557NS

Results for osmotic stress treatments are shown as a stress index (SI). Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) among measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 4)

Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS, r-Correlation coefficient not significant

r-Correlation coefficient significant at the 0.05 level

Table 3

Seedlings survival (SS) of maize and triticale genotypes germinated in mannitol solutions differing in water potential and after 14 days of grown in well watered soil

Genotype

Control (C)

Stress index (SI) of osmotic stress treatments

0.0 MPa (%)

−0.43 MPa

−0.85 MPa

−1.30 MPa

Maize

Ankora

25.0

0.80

0.72

0.52

Garst 8702

25.0

0.72

0.64

0.72

Garst 8388

25.0

0.96

0.80

0.84

Garst R5515

25.0

0.72

0.68

0.52

Pioneer-38-F-70

25.0

0.80

0.76

0.76

Nova

25.0

0.92

0.84

0.92

Pioneer-39-G-12

25.0

0.72

0.64

0.72

Pioneer-39-R-10

25.0

0.84

0.80

0.72

Funk`s G4083

25.0

0.88

0.80

0.84

Pioneer 3957

25.0

0.96

0.92

0.72

Pioneer 3925

25.0

0.92

0.88

0.72

Garst 8344

25.0

0.92

0.84

0.88

Tina

25.0

1.00

0.96

1.00

    Mean

25.0

0.86

0.79

0.76

    Range

 

0.72–1.00

0.64–0.96

0.52–1.00

   LSD0.05

0.088

0.107

0.099

     CV

11.51

12.76

18.36

  r versus DSIGY

−0.638*

−0.763**

−0.544NS

Triticale

CHD-147

25.0

0.96

0.68

0.64

Maja

25.0

1.00

0.96

0.92

CHD-12

25.0

0.96

0.72

0.64

Mieszko

25.0

1.00

0.88

0.88

Kargo

25.0

0.96

0.8

0.76

MAH

25.0

0.92

0.84

0.80

Gabo

25.0

0.96

0.84

0.80

Wanad

25.0

1.00

0.96

0.96

Migo

25.0

0.96

0.96

0.96

CHD-220

25.0

1.00

0.96

0.96

CHD-247

25.0

1.00

0.92

0.92

    Mean

25.0

0.97

0.86

0.84

    Range

 

0.92–1.00

0.72–0.96

0.64–0.96

   LSD0.05

0.067

0.049

0.039

     CV

2.77

11.56

14.44

  r versus DSIGY

−0.161NS

−0.827**

−0.634*

Results for osmotic stress treatments are shown as a stress index (SI). Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) between measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 5)

Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS, r-Correlation coefficient not significant

*, ** r-Correlation coefficient significant at the 0.05 and 0.01 level, respectively

Germination is strongly influenced by plant species, grain age and storage conditions and is also highly sensitive to soil water quality and temperature (Ashraf and Mehmood 1990; Ahmad et al. 2009). Grain germination indexes were used with different results in selecting procedures for identifying drought resistant genotypes (Jajarmini 2009). Opinions on the efficiency of plant drought tolerance assessments on the basis of different parameters of germination are divergent and indicate their limited usefulness. It is believed that germination traits are affected by drought stress less than other physiological and biochemical traits (Winter et al.1988; Morgan 1992). Genotype tolerance to drought stress is process-specific with regard to grain water imbibition, endosperm utilization, activation of the dormant enzyme system and seedling emergence, growth and survival after stress. The tolerance to drought in any of those stages will affect the results and thus screening for drought tolerance reaction should also be process-specific, and the prediction of genotype performance from one process to another would not necessarily be possible (Blum et al. 1980; Ahmad et al. 2009). In this study the tolerance to simulated drought stress proved a relatively good indicator in the screening of maize and triticale genotypes. Our results showed that the efficiency of FG, PI and SS is related to how strongly a trait is expressed and the measurement must be performed at the right moment in order to maximize the expression of genetic variations of the particular trait.

Seedling dry matter (DWS, DWR), root length (LS, LSA, LN) and the number of nodal roots (NN)

Soil drought decreased the dry matter of the above ground parts (DWS) in 13 genotypes of maize from 12.0 to 67.0 % and in 11 genotypes of triticale from 5.0 to 17.0 %. Similarly, the dry matter of roots (DWR) decreased in maize genotypes from 3.0 to 30.0 % and in triticale genotypes from 1.0 to 25.0 %. Drought also strongly influenced the ratio of DWS to DWR within maize genotypes from 0.0 to 60.0 % and in triticale genotypes from 0.0 to 25.0 % (Table 4). The variation coefficients (CV) in control (C) seedlings of DWS, DWR and the ratio of DWS to DWR in maize were about 29, 18 and 37 %, respectively, and in triticale 23, 29 and 22 %, respectively. CV calculated for treatment D as a stress index (SI) for those traits in maize were about 34, 13 and 31 %, respectively, and in triticale 15, 9 and 11 %, respectively (Table 4). In the control treatment (C), the statistically significant correlation between DSIGY and DWS, DWR and the ratio of DWS to DWR were found only in triticale for the ratio of DWS to DWR. In the drought treatment, the statistically significant correlation between DSIGY and DWS, DWR and the ratio of DWS to DWR were found in maize for DWS and the ratio of DWS to DWR, and in triticale, only for DWS (Table 4).
Table 4

Effect of moderate soil drought (35 % FWC) on dry matter of shoots (DWS)

Genotype

Shoots (S)

Roots (R)

Ratio of S to R

C

D35

C

D35

C

D35

Maize

Ankora

7.08

0.412

3.29

0.761

2.15

0.542

Garst 8702

9.39

0.339

3.21

0.779

2.92

0.435

Garst 8388

10.41

0.410

2.94

0.850

3.54

0.482

Garst R5515

8.18

0.463

3.17

0.583

2.58

0.795

Pioneer-38-F-70

6.07

0.450

3.32

0.901

1.83

0.499

Nova

6.11

0.750

5.50

0.891

1.11

0.842

Pioneer-39-G-12

9.13

0.329

3.44

0.807

2.65

0.407

Pioneer-39-R-10

11.18

0.359

3.45

0.704

3.24

0.510

Funk`s G4083

6.13

0.713

4.00

0.768

1.53

0.929

Pioneer 3957

8.11

0.755

3.65

0.840

2.22

0.898

Pioneer 3925

8.31

0.721

3.45

0.722

2.41

0.998

Garst 8344

4.50

0.644

4.00

0.950

1.13

0.678

Tina

4.00

0.875

3.50

0.971

1.14

0.901

    Mean

7.58

0.555

3.61

0.810

2.19

0.686

    Range

4.00–11.18

0.329–0.875

2.94–5.50

0.704–0.971

1.11–3.54

0.407–0.998

   LSD0.05

0.39

0.111

0.18

0.077

0.65

0.208

     CV

28.7

34.4

17.81

13.3

36.91

31.3

  r versus DSIGY

0.413NS

−0.732**

−0.228NS

−0.337NS

0.464NS

−0.642*

Triticale

Maja

3.99

0.779

3.59

0.864

1.11

0.903

CHD-147

3.06

0.654

1.68

0.832

1.82

0.786

Mieszko

4.02

0.746

2.41

0.974

1.67

0.766

CHD-12

3.45

0.725

1.90

0.791

1.82

0.917

Kargo

4.18

0.749

3.34

0.897

1.25

0.835

MAH

5.89

0.540

4.71

0.744

1.25

0.726

Gabo

4.11

0.852

3.50

0.857

1.17

0.994

Wanad

3.08

0.860

2.50

0.880

1.23

0.978

Migo

3.02

0.841

2.58

0.992

1.17

0.848

CHD-220

3.08

0.945

3.15

0.947

0.98

0.998

CHD-247

3.15

0.911

2.72

0.921

1.16

0.990

    Mean

3.73

0.782

2.92

0.882

1.33

0.885

    Range

3.02–5.89

0.725–0.945

1.68–4.71

0.744–0.992

0.98–1.82

0.726–0.998

   LSD0.05

0.25

0.139

0.11

0.223

0.38

0.141

     CV

23.0

15.0

29.4

8.6

22.1

11.2

  r versus DSIGY

0.370NS

−0.670*

−0.064NS

−0.451NS

0.627*

−0.554NS

Roots (DWR) and ratio of DWS to DWR of maize and triticale seedlings after 42 days of growth. Results for drought treatments (D35) are shown as a stress index (SI). Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) between measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 4)

Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS, r-correlation coefficient not significant

*, ** r-Correlation coefficient significant at the 0.05 and 0.01 level, respectively

The results presented in Table 5 show that in the control (C) treatment, differences within drought resistant and drought sensitive genotypes of maize and triticale in the length of seminal (LS), seminal adventitious (LSA), nodal roots (LN), and number of nodal roots (NN) were in most cases statistically insignificant. Also under control conditions the correlation coefficient between DSIGY and the measured traits was statistically insignificant. LS in drought treatment decreased in the drought resistant genotypes of maize (Tina, Garst 8344) about 10 % and in drought sensitive genotypes (Ancora, Garst 8702) about 23 %. In drought resistant genotypes of triticale (CHD-220, CHD-247) the decrease was about 6 % and in drought sensitive genotypes (CHD-12, CHD-147) about 13 %. In maize the decrease in the total length of two seminal adventitious roots (LSA) in comparison to the control was about 7 % in drought resistant hybrids and in drought sensitive about 25 %. For triticale the decrease in the total length of three seminal adventitious (LSA) roots in comparison to the control was statistically insignificant in drought resistant and sensitive lines. In seedlings exposed to drought, significant differences between drought resistant and sensitive genotypes were observed in the measurements of the total length of nodal roots (LN). In maize the decrease in LN in drought sensitive genotypes was about 22 % and in the drought resistant ones about 10 %, and in triticale about 30 and 13 %, respectively. In C treatments no statistically significant correlation between DSIGY and LS, LSA, NN and LN was found in either maize or triticale genotypes. In seedlings exposed to drought, a statistically significant correlation between DSIGY and SI calculated for the measurements of root traits was found in maize, but in triticale only for LN (Table 5).
Table 5

Effects of moderate soil drought (35 % FWC) on length and number of particular components of root system in maize and triticale seedlings after 42 days of growth in root-boxes

Genotype

Length of seminal root—LS (cm)

Length of seminal adventitious roots—LSA (cm)

Number of nodal roots—NN

Total length of nodal roots—LN (cm)

C

D35

C

D35

C

D35

C

D35

Maize

Ankora

37.7

0.775

68.1

0.730

11.5

0.974

345.9

0.740

Garst 8702

36.4

0.769

71.1

0.755

12.5

0.968

333.2

0.739

Garst 8388

35.2

0.793

67.1

0.757

11.8

1.017

361.0

0.740

Garst R5515

35.4

0.850

70.6

0.776

12.9

1.008

374.1

0.738

Pioneer-38-F-70

36.4

0.810

65.4

0.751

10.7

1.047

300.2

0.741

Nova

37.1

0.803

78.1

0.939

11.5

1.043

355.5

0.889

Pioneer-39-G-12

37.2

0.892

68.4

0.939

11.0

0.982

321.1

0.739

Pioneer-39-R-10

38.2

0.809

65.0

0.749

11.3

1.044

325.4

0.738

Funk`s G4083

35.3

0.918

72.5

1.014

13.7

1.058

407.8

0.887

Pioneer 3957

31.0

0.926

69.0

0.930

11.2

1.232

324.5

0.900

Pioneer 3925

35.0

0.903

70.2

1.014

13.0

1.092

377.7

0.890

Garst 8344

39.4

0.916

64.2

0.922

11.7

0.983

340.2

0.890

Tina

34.2

0.907

75.0

0.949

13.5

1.015

411.7

0.887

    Mean

36.0

0.852

69.6

0.863

12.0

1.036

352.2

0.809

    Range

31.0–39.4

0.769–0.926

64.2–78.1

0.730–1.014

10.7–13.7

0.968–1.232

300.2–411.7

0.738–0.900

   LSD0.05

3.49

0.039

3.91

0.074

1.18

0.113

39.7

0.013

     CV

5.8

7.1

5.7

12.8

8.2

6.7

9.5

9.7

  r versus DSIGY

−0.144NS

−0.838**

−0.050NS

−0.746**

−0.347NS

−0.447NS

−0.363NS

−0.798**

Triticale

Maja

34.1

0.818

90

0.903

15.4

0.773

199.3

0.688

CHD-147

31.5

0.867

89.5

0.911

16.4

0.930

213.5

0.797

Mieszko

28.7

0.951

95.4

0.820

17.9

0.765

232.4

0.612

CHD-12

32.3

0.870

100.2

0.831

18.2

0.813

200.3

0.741

Kargo

28.4

0.891

98.6

0.722

14.1

0.816

222.2

0.653

MAH

30.5

0.866

88.2

0.783

15.0

0.820

194.9

0.796

Gabo

29.5

0.953

107.3

0.702

14.2

0.894

175.6

0.74

Wanad

30

0.900

108.7

0.786

19.9

0.905

298.3

0.889

Migo

33.3

0.937

95.1

0.918

17.2

0.884

257.0

0.817

CHD-220

30.5

0.938

95.4

0.894

17.2

0.797

228.6

0.854

CHD-247

34.2

0.939

90.2

0.945

16.2

0.938

211.3

0.953

    Mean

31.2

0.903

96.2

0.838

16.5

0.849

221.2

0.776

    Range

28.4–34.2

0.818–0.953

88.2–108.7

0.702–0.945

14.1–19.9

0.765–0.938

175.6–298.3

0.612–0.953

   LSD0.05

2.18

0.027

5.39

0.083

2.11

0.101

13.0

0.088

     CV

6.6

4.9

7.3

9.9

10.8

7.4

15.2

13.2

  r versus DSIGY

−0.234NS

−0.547NS

−0.210NS

−0.222NS

−0.198NS

−0.489NS

−0.392NS

−0.760**

Results of drought treatment (D35) are shown as stress index (SI). Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) between measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 4)

Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS, r-Correlation coefficient not significant

*, ** r-Correlation coefficient significant at the 0.05 and 0.01 level, respectively

As one might expect, a root system characteristic, such as the number and length of particular components of root system structure, their depth and abundance, are known to be associated with performance under drought conditions in many studies of cereal species (Richards 1996; Reynolds 2002). Nevertheless, decreased allocation in roots in the top layer of the soil has been shown to be an effective drought stress adaptive mechanism (Richards 1991). In the studies by Kono et al. (1987) the specific response of cereal species to drought stress was clearly noticeable in root distribution, nodal root number, leaf number and grain yield. Those authors reported that the responses in root growth for maize and rice were different in the downward penetration of the main axis and in the higher order laterals. Species with a “concentrated” type of root system showed less restriction of root and shoot growth compared to species with a “scattered” type. A decrease in size of the root system and an increasing irregularity of root distribution resulted in water and nutrients being transported greater distances to the nearest roots (Tardieu 1991; Lipiec et al. 1996). Drought also modifies root system components, such as the main root axis and lateral roots of different orders in rice and maize (Iijima and Kono 1991). Changes in the morphological structure of the root system in triticale and maize were also observed in our earlier studies in response to waterlogging and soil compaction (Grzesiak et al. 2012).

Leaf water potential (ψ) and water loss of excised leaves (WL)

Moderate (D35) and severe (D20) soil drought decreased ψ in maize and triticale (Table 6). Differences between resistant genotypes of maize (Garst 8344, Tina) and triticale (CHD-247, CHD-220) in terms of a decrease of ψ were statistically significant in comparison with sensitive genotypes of maize (Ankora, Garst 8702, Garst 8388) and triticale (Maja, CHD-247).
Table 6

Leaf water potential (ψ) of maize and triticale genotypes grown in moderate (D35) and severe (D20) soil drought and index of excised-leaf water loss (WL) from leaf seedlings grown in control condition after 12 and 24 h

Genotype

Leaf water potential (ψ)

Water loss (WL—g H2O/g DW)

D35

D20

12 h

24 h

Maize

Ankora

2.69

2.40

5.07

7.11

Garst 8702

2.39

2.39

5.65

6.99

Garst 8388

2.31

2.41

5.69

6.07

Garst R5515

2.49

2.49

4.18

7.13

Pioneer-38-F-70

2.71

2.35

5.55

7.25

Nova

2.58

2.10

5.55

8.39

Pioneer-39-G-12

2.55

2.41

5.64

8.14

Pioneer-39-R-10

2.11

2.39

5.84

7.1

Funk`s G4083

2.39

2.18

3.41

6.07

Pioneer 3957

2.41

2.27

3.18

5.23

Pioneer 3925

2.31

2.25

2.97

6.21

Garst 8344

2.19

2.18

3.39

6.21

Tina

2.07

2.21

3.13

6.55

    Mean

2.40

2.31

4.56

6.80

    Range

2.07–2.71

2.10–2.41

2.97–5.84

5.23–8.39

   LSD0.05

0.05

0.07

0.58

0.39

     CV

8.5

5.1

26.0

12.8

  r versus DSIGY

0.682*

0.645*

0.773**

0.511NS

Triticale

Maja

2.39

2.39

2.42

3.50

CHD-147

2.31

2.49

3.58

4.07

Mieszko

2.25

2.18

2.54

5.07

CHD-12

2.45

2.35

3.57

4.04

Kargo

2.33

2.25

3.39

5.11

MAH

2.21

2.00

2.41

4.41

Gabo

2.37

2.25

3.18

4.39

Wanad

2.41

2.11

2.07

3.52

Migo

2.18

2.18

1.27

3.07

CHD-220

2.13

2.07

2.07

3.50

CHD-247

2.11

2.11

2.13

3.00

    Mean

2.28

2.22

2.60

4.00

    Range

2.11–2.45

2.00–2.49

1.27–3.58

3.00–5.11

   LSD0.05

0.04

0.08

0.18

0.25

     CV

5.1

6.7

27.9

18.7

  r versus DSIGY

0.579NS

0.637*

0.714*

0.655*

The results of ψ are shown as a stress index (SI). Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) between measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 5)

Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS, r-Correlation coefficient not significant

*, ** r-Correlation coefficient significant at the 0.05 and 0.01 level, respectively

In seedlings exposed to drought, a statistically significant correlation between DSIGY and SI calculated for the measurement of ψ in maize was found for both treatments but in triticale only for treatment D20. The differences in WL between drought resistant and sensitive genotypes of maize and triticale genotypes were statistically significant. A statistically significant correlation between DSIGY and WL in maize was found only in the measurements taken after 12 h of leaf desiccation but in triticale in the measurements taken after 12 and 24 h (Table 6).

Decreases in leaf water potential initially induced stomatal closure, resulting in a decrease in the supply of CO2 to the mesophyll cells and subsequently in a decrease in the rate of leaf photosynthesis (Williams et al. 1999; Lawlor and Cornic 2002). According to Hura et al. (2007), a statistically significant correlation between water potential and photosynthetic rate and stomatal conductance was found in maize and triticale during various stages of plant development. Dehydration in C3 and C4 plants impairs various physiological processes, especially the changes in leaf water content, water potential and photosynthesis. There is a controversy as to whether drought limits photosynthesis due to leaf water status, stomatal closure, metabolic impairment or injuries to photosynthetic apparatus (Flexas et al. 2006). The first response to leaf water deficit is stomata closure, which limits CO2 diffusion to chloroplasts (Berkowitz et al. 1983; Cornic and Masacci 1996; Muller and Whitsitt 1996). Non-stomatal mechanisms under prolonged or severe soil drought include changes in chlorophyll synthesis, functional and structural changes in chloroplasts and also disturbances in accumulation and distribution of assimilation products (Medrano et al. 2002). However, it is known that during drought stress, plants with C4 photosynthesis increase water use efficiency and suppress photorespiration. Thus, C4 plants are often more competitive than C3 plants in drought-prone areas (Edwards and Ku 1987; King 2011).

Relative loss of intracellular electrolytes from leaf tissues (LI)

In drought sensitive genotypes of maize and triticale, the values of indexes of leaf injury by soil drought (D35, D20), osmotic stress (−0.47, −0.85, −1.30 MPa), and heat temperature (25, 35, 45 °C) were in general higher than in drought resistant genotypes and in most cases the differences between resistant and sensitive genotypes were statistically significant (Table 7). In treatment D35, the values of coefficients of variation (CV) in maize and triticale were higher than in D20 treatment. Under osmotic stress, higher values of CV were found in −0.85 MPa treatment and under high temperature stress in 35 °C treatment.
Table 7

Leaf injury index (LI) of maize and triticale seedlings grown in moderate (D35) and severe (D20) soil drought conditions and data of conductivity tests of leaf injury by osmotic drought stress (−0.47, −0.85, −1.30 MPa) and by high temperature stress (25, 35, 45 °C)

Genotype

Soil drought

Osmotic stress (MPa)

High temperature stress (°C)

D35

D20

−0.47

−0.85

−1.30

25

35

45

Maize

Ankora

41.1

50.9

22.2

42.0

48.7

20.9

32.2

35.0

Garst 8702

39.5

45.3

25.6

45.0

27.0

24.3

30.0

58.0

Garst 8388

39.5

45.0

25.5

34.5

45.1

24.2

40.7

47.5

Garst R5515

39.5

42.3

24.3

31.8

42.4

23.0

34.3

44.8

Pioneer-38-F-70

31.2

36.8

19.5

26.3

26.5

18.2

29.5

40.4

Nova

31.2

40.8

20

30.3

40.9

18.7

30.0

43.3

Pioneer-39-G-12

31.2

36.8

18.6

18.0

22.2

17.3

28.6

39.0

Pioneer-39-R-10

30.6

35.3

18.2

21.8

22.5

16.9

28.2

37.8

Funk`s G4083

13.1

36.6

20

17.5

35.0

18.7

25.0

40

Pioneer 3957

18.4

36.3

22.2

25.8

26.0

20.9

24.2

38.8

Pioneer 3925

13.2

35.8

20.1

25.3

26.0

17.8

20.0

40.3

Garst 8344

18.1

33.9

22.2

23.4

34.0

20.9

32.2

36.4

Tina

11.1

30

20.9

17.5

23.6

19.6

21.2

35.5

    Mean

27.5

38.9

21.5

27.6

32.3

20.1

28.9

41.3

    Range

11.1–41.1

30.0–50.9

18.2–25.6

17.5–45.0

22.2–48.7

16.9–24.3

20.0–40.7

35.0–58.0

   LSD0.05

1.51

1.88

1.25

1.35

1.13

1.18

1.08

1.13

     CV

40.9

14.6

11.4

32.0

28.7

12.5

19.2

15.0

  r versus DSIGY

0.953**

0.868**

0.412NS

0.751**

0.540NS

0.438NS

0.707**

0.511NS

Triticale

Maja

24.5

30.6

16.5

37.5

39.4

12.7

36.4

52.4

CHD-147

26.4

35.9

18.7

33.3

38.1

14.4

34.2

48.5

Mieszko

25.3

31.5

20

34.5

37.9

18.2

34.8

55.9

CHD-12

25.4

39.7

18.5

28.8

45.7

17.8

42.8

49.5

Kargo

25.4

32.8

16.2

39.1

37.4

12.0

35.4

50.2

MAH

23.4

30.6

14.5

27.6

28.8

12.7

25.8

41.6

Gabo

21.4

27.5

22.2

25.4

26.4

15.7

22.5

44.4

Wanad

19.8

28.6

17.5

18.8

21.5

10.2

18.6

44.5

Migo

19.9

25.5

15.1

18.8

25.8

13.3

22.7

48.2

CHD-220

13.5

23.6

15.5

19.5

22.7

13.7

20.8

45.2

CHD-247

11.5

23.3

14.5

20.3

21.1

11.8

18.2

44.6

    Mean

21.5

30

17.2

27.6

31.3

13.9

28.4

47.7

    Range

11.5–25.4

23.3–35.9

14.5–22.2

18.8–39.1

21.1–45.7

10.2–17.8

18.2–42.8

41.6–55.9

   LSD0.05

1.18

1.39

1.41

1.07

1.08

0.59

1.11

0.99

     CV

23.3

16.8

14.3

27.8

27.3

18.0

30.0

8.7

  r versus DSIGY

0.857**

0.820**

0.404NS

0.911**

0.886**

0.436NS

0.876**

0.603*

Mean values, range of means, coefficient of variation (CV) and correlation coefficient (r) between measured traits and drought susceptibility index (DSIGY). Genotypes are ordered according to the DSIGY value (n = 5)

Number of degree of freedom (df) for critical values of (r) were for maize 11 and for triticale 9

NS, r-Correlation coefficient not significant

*, ** r″-Correlation coefficient significant at the 0.05 or 0.01 level, respectively

The ability to maintain the structure and function of cytoplasmatic membranes under water deficit is one of the most important physiological traits. Conductometric measurements of LI are applied as a screening test for the estimation of tolerance to various stresses (Vietor et al. 1977; Richards 1978; Blum and Ebercon 1981; Poljakoff-Mayber 1981; Martinielio and Lorenzoni 1985; Palta 1990). Differences between sensitive and resistant genotypes might stem from the fact that drought resistant genotypes possess more efficient mechanisms protecting membrane functions and structure. Drought stress causes a loosening of lamellar membranes in chloroplasts, loss of a certain amount of grana, and increase in the level of coarse-grain matrix (Haupt-Harting and Fock 2002; Lawlor and Cornic 2002; Tang et al. 2002). Some authors suggest that drought resistant plant species show stronger binding of chlorophyll molecules to the lipid-protein complex of chloroplast membranes (Smirnoff and Colombe 1988; Bukhov et al. 1990). Our earlier results indicate that leaf age is very important because differences in LI between drought resistant and sensitive genotypes were smallest in the oldest and youngest leaves, though the greatest differences were observed in the leaves where cellular divisions had taken place and which had reached the maximal area (Grzesiak et al. 2006).

Correlations among stress parameters (DWS, DWR, DWS/DWR) were significant, except for the relationship between DWS and DWR in maize. In this experiment the correlations between DWR and other traits were not significant except for seedling survival (SS). For both species, high and significant correlations were found between measurements of membrane injures due to drought (LID), osmotic stress (LIOS), and high temperature (LIHT). Also for both species, high and significant correlations were found between leaf water potential (ψ) and water loss (WL) and between FG and PI, except for maize (Table 8).
Table 8

Correlation coefficients (r) among physiological stress traits in maize and triticale genotypes

Traits

Species

Traits

DWR

DWS/DWR

LS, LSA, LN

LID

LIOS

LIHT

ψ

WL

FG

PI

SS

DWS

Maize

0.427NS

0.900***

0.845***

−0.581**

−0.381NS

−0.537*

−0.634**

−0.672**

0.712***

0.362NS

0.793***

Triticale

0.689**

0.854***

0.433NS

−0.670**

−0.560NS

−0.408NS

−0.275NS

−0.586*

0.555NS

0.343NS

0.657**

DWR

Maize

 

−0.002NS

0.315NS

−0.216NS

−0.233NS

−0.189NS

−0.390NS

−0.092NS

0.349NS

0.267NS

0.706***

Triticale

0.216NS

0.288NS

−0.423NS

−0.303NS

−0.084NS

−0.349NS

−0.518NS

0.340NS

0.380NS

0.641**

DWS/DWR

Maize

  

0.793***

−0.533*

−0.301NS

−0.489*

−0.489*

−0.714***

0.616**

0.232NS

0.533*

Triticale

0.342NS

−0.555*

−0.490NS

−0.437NS

−0.065NS

−0.376NS

0.461NS

0.149NS

0.406NS

LS, LSA, LN

Maize

   

−0.760***

−0.406NS

−0.618**

−0.619**

−0.670**

0.664**

0.597**

0.633**

Triticale

−0.678**

−0.714**

−0.573NS

−0.386NS

−0.722**

0.324NS

0.042NS

0.325NS

LID

Maize

    

0.589**

0.857***

0.539*

0.423NS

−0.778***

−0.695***

−0.495*

Triticale

0.973***

0.875***

0.770***

0.902***

−0.831***

−0.530NS

−0.691**

LIOS

Maize

     

0.837***

0.216NS

0.299NS

−0.660**

−0.350NS

−0.317NS

Triticale

0.922***

0.733**

0.880***

−0.752***

−0.364NS

−0.594*

LIHT

Maize

      

0.353NS

0.356NS

−0.868***

−0.576**

−0.381NS

Triticale

0.635**

0.715**

−0.770***

−0.373NS

−0.466NS

ψ

Maize

       

0.532*

−0.286NS

−0.652**

−0.799***

Triticale

0.673**

−0.743***

−0.672**

−0.532NS

WL

Maize

        

−0.253NS

−0.155NS

−0.472NS

Triticale

−0.733**

−0.459NS

−0.806***

FG

Maize

         

0.434NS

0.523*

Triticale

0.748***

0.819***

PI

Maize

          

0.357NS

Triticale

0.567NS

Data for calculation of correlation coefficient (r) for traits DWS, DWR and DWS/DWR were SI for treatment D35, for LS, LSA and LN were SI for total length for seminal, seminal adventitious and nodal roots, for LID were mean values of treatment D35 and D20, for LI OS and LIHT mean values of treatments −0.47, −0.85 and −1.30 MPa and 25, 35 and 45 °C, respectively, for ψ were mean values of SI in treatment D35 and D20, for WL were mean values of treatments 12 and 24 h and for FG, PI and SS were mean values of treatments −0.47, −0.85 and −1.30 MPa. Number of degree of freedom (df) for critical values of (r) was for maize 11 and for triticale 9

NS r-Correlation coefficient not significant

*, **, *** r-Correlation coefficient significant at the 0.10, 0.05 and 0.01 level, respectively

Most of physiological processes are affected by the stresses of soil drought, osmotic and high temperatures (Levitt 1980). The usefulness of methods for studying plant drought tolerance has been discussed in many papers and reviews (Blum et al. 1980; Kpoghomou et al. 1990; Zagdańska 1992; Reynolds 2002). According to some authors, besides the evaluation criteria used in the present research, positive results were also obtained by utilizing the measurements of canopy infra-red temperature, changes of leaf color and responses of root architecture (Clarke and McCaig 1982; Stanley 1990). Partly positive results were obtained in tests of the plant’s ability to reduce leaf area, the development of wax bloom on leaves, leaf rolling and the ability of the leaf to hold water (Passioura et al. 1993). On the other hand, the effectiveness of the evaluation of tolerance based on the measurement of different parameters of leaf gaseous exchange and the content of various metabolites, including proline, has not been definitely confirmed. (Hanson and Nelson 1985; Farquhar et al. 1993; Dubey 1997; Bandurska and Stroiński 2003).

The maize and triticale genotypes used in these experiments show a relatively wide range of drought tolerance. This study contributes to the understanding of responses of different genotypes to drought, though in this research, the method of estimating drought susceptibility index was relatively simple and did not take into account other important factors of soil–water-plant relationship. The correlations between DSIGY and laboratory tests (LI, FG, SS) showed that they are good indicators of plant drought tolerance. The correlation coefficients between LIOS and LIHT tests were, in most of the considered cases, statistically significant, thus indicating that in maize and triticale the mechanisms of membrane injury caused by simulated drought or high temperature were physiologically similar. It can be concluded that an approach to the breeding of maize and triticale for drought tolerance using these tests can be implemented on the basis of separate selection for each trait or for all of them simultaneously. In that case, it would be necessary to determine the importance of the trait in relation to the growth phase, drought timing and level as well as associations with morphological traits contributing to drought tolerance (Kono et al. 1987; Kpoghomou et al. 1990).

The results presented in this paper, our earlier research (Grzesiak 1990; Grzesiak et al. 2012) and the results of other authors (Lorens et al. 1987; Martinielio and Lorenzoni 1985; Kono et al. 1987) confirm the existence of a wide range of genotypic variability of response to drought in cereal plants. In maize and triticale, similar to other crop plants, the physiological reasons for this variability have not as yet been fully recognized and explained. In the literature one can find many contradictory conclusions, as the reduction of crop yield of the tested genotypes depends not only on the drought level and its duration, but also on the phase of plant growth and development and interaction with other environmental factors (Hanson and Nelson 1985; Blum 1988; Naylor and Su 1998).

The grounds for genotypic variation of drought tolerance in cereals has not yet been entirely elucidated and future research is necessary (Zagdańska 1992; Royo et al. 2000; Reynolds 2002). The frequent occurrence of drought stress in many regions of the world and the deteriorating water conditions for plant growth and productivity have raised interest in research into the responses of crops to periodic water deficiency. Progress in the breeding of drought resistant cereal plants requires future study of the physiological mechanisms underlying the responses of plants to water stress. It is a widely held opinion that the breeding of drought resistant crop plants will not be an easy task (Hanson and Nelson 1985; Zagdańska 1992; Reynolds 2002). The complexity of the property of drought tolerance will make it necessary to take into consideration various tolerance evaluation criteria in the breeding programs (Levitt 1980; Turner 1986; Blum 1988; Richards 1991; Jones 1993; King 2011).

Author contribution

MTG, FJ, PW, KH designed the research; MTG, PS, IM and TG conducted the research; MTG, FJ, PS and KH analyzed the data; MTG, FJ, IM and KH wrote the paper; MTG had primary responsibility for the final content. All authors have read and approved the final manuscript.

Notes

Acknowledgments

The authors are grateful to the National Science Centre (NCN) for financial support (Project No. N N310 782540). We are thankful to SEMPOL–Holding, Trnava, Slovakia and Choryn Breeding Station for providing maize and triticale genotypes.

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

References

  1. Ahmad S, Ahmad R, Ashraf MY, Ashraf M, Waraich EA (2009) Sunflower (Helianthus annuus L.) response to drought stress at germination and seedling growth stages. Pak J Bot 41:647–654Google Scholar
  2. Asharaf M (2010) Inducing drought tolerance in plants: recent advances. Biotech Adv 28:199–238CrossRefGoogle Scholar
  3. Ashraf M, Mehmood S (1990) Response of four Brassica species to drought stress. Environ Exp Bot 30:93–100CrossRefGoogle Scholar
  4. Bandurska H, Stroiński A (2003) ABA and proline accumulation in leaves and roots of wild (Hordeum spontaneum) and cultivated (Hordeum vulgare Maresi) barley genotypes under water deficyt conditions. Acta Physiol Plant 25:55–61CrossRefGoogle Scholar
  5. Bennett JM (1990) Problems associated with the measuring plant water status. HortScience 25:1551–1554Google Scholar
  6. Berkowitz GA, Chen C, Gibbs M (1983) Stromal acidification mediates in vivo water stress inhibition of nonstomatal controlled photosynthesis. Plant Physiol 72:1123–1126PubMedCrossRefGoogle Scholar
  7. Blum A (1988) Plant breeding for stress environments. CRC Press, Boca RatonGoogle Scholar
  8. Blum A, Ebercon A (1981) Cell membrane stability as a measure of drought and heat tolerance in wheat. Crop Sci 21:43–47CrossRefGoogle Scholar
  9. Blum A, Sinmena B, Ziv O (1980) An evaluation of seed and seedling drought tolerance screening tests in wheat. Euphytica 29:727–736CrossRefGoogle Scholar
  10. Bouslama M, Schapauch WT (1984) Stress tolerance in soybean. I. Evaluation of three screening techniques for heat and drought tolerance. Crop Sci 24:933–937CrossRefGoogle Scholar
  11. Boyer JS (1982) Plant productivity and environment. Science 218:443–448PubMedCrossRefGoogle Scholar
  12. Bukhov NG, Sabat SC, Mohanty P (1990) Analysis of chorophyll a fluorescence changes in weak light in heat-treated Amaranthus chloroplasts. Photosyn Res 23:81–87CrossRefGoogle Scholar
  13. Chaves MM, Pereira JS, Maroco J, Rodrigues ML, Ricardo CPP, Osorio LM, Carvalho I, Faria T, Pinheiro C (2002) How plants cope with water stress in the field. Photosynthesis and growth. Ann Bot 89:907–916PubMedCrossRefGoogle Scholar
  14. Clarke JM, McCaig TN (1982) Evaluation of techniques for screening for drought resistance in wheat. Crop Sci 22:503–506CrossRefGoogle Scholar
  15. Cornic G, Masacci A (1996) Leaf photosynthesis under drought stress. In: Baker NR (ed) Photosynthesis and the environment. Kluwer Academic Publication, Dordrecht, pp 347–366Google Scholar
  16. Doorenbos J, Kassam AH (1986) Yield response to water. FAO Irrigation and drainage paper. Food and Agriculture Organization of the United Nations. RomeGoogle Scholar
  17. Doorenbos J, Pruit WO (1977) Guidelines for predicting crop water requirements. FAO Irrigation and drainage paper. Food and Agriculture Organization of the United Nations. RomeGoogle Scholar
  18. Dubey RS (1997) Photosynthesis in plants under stressful conditions. In: Pessarakli M (ed) Handbook of Photosynthesis, University of Arizona, Tuscon. Arizona. Marcel Dekker, Inc., New York, pp 859–875Google Scholar
  19. Edwards GE, Ku MSB (1987) Biochemistry of C3–C4 intermediates. In: Hatch MD, Boardman NK (eds) The Biochemistry of plants, a comprehensive treatise, vol 10. Photosynthesis. Academic Press, New York, pp 275–324Google Scholar
  20. Evans RO, Skagss RW, Sneed RE (1990) Normalized crop susceptibility factors for corn and soybean to excess water stress. Trans ASAE 33:1153–1161Google Scholar
  21. Evans RO, Skagss RW, Sneed RE (1991) Stress day index models to predict corn and soybean relative yield under high water table conditions. Trans ASAE 5:1997–2005Google Scholar
  22. Fageria NK, Balingar VC, Clark RB (2006) Physiology of crop production. The Haworth Press Inc., New York, pp 23–60Google Scholar
  23. Farquhar GD, Wong SC, Evans JR, Hubiek KT (1993) Photosynthesis and gas exchange. In: Jones HG, Flowers TJ, Jones MB (eds) Plants under stress. Cambridge University Press, New York, pp 47–70Google Scholar
  24. Fischer RA, Maurer R (1978) Drought resistance in spring wheat cultivars. I Grain yield responses. Aus J Agric Res 29:897–912CrossRefGoogle Scholar
  25. Flexas J, Bota J, Galme′s J, Medrano H, Ribas-Carbo′ M (2006) Keeping a positive carbon balance under adverse conditions: responses of photosynthesis and respiration to water stress. Physiol Plant 127:343–352CrossRefGoogle Scholar
  26. Gerik TJ, Eastin JD (1985) Temperature effects on dark respiration among diverse sorghum genotypes. Crop Sci 25:957–961CrossRefGoogle Scholar
  27. Gomez F, Oliva MA, Mielke MS, de Almeida AAF, Leita HG, Aquino LA (2008) Photosynthetic limitations in leaves of young Brazilian Green Dwarf coconut (Cocos macifera L.) ‘‘nana’’ palm under well-watered conditions or recovering from drought stress. Environ Exp Bot 62:195–204CrossRefGoogle Scholar
  28. Grzesiak S (1990) Reaction to drought of inbreds and hybrids of maize (Zea mays L.) as evaluated in field and greenhouse experiments. Maydica 35:303–331Google Scholar
  29. Grzesiak MT, Grzesiak S, Skoczowski A (2006) Changes of leaf water potential and gas exchange during and after drought in triticale and maize genotypes differing in drought tolerance. Photosyntetica 44:561–568CrossRefGoogle Scholar
  30. Grzesiak MT, Marcińska I, Janowiak F, Rzepka A, Hura T (2012) The relationship between seedling growth and grain yield under drought conditions in maize and triticale genotypes. Acta Physiol Plantarum. doi: 10.1007/s11738-012-0973-3
  31. Hanson AD, Nelson ChE (1985) Water adaptation of crop to drought. In: Carlson PS (ed) The biology of crop productivity. Academic Press, New York, pp 79–149Google Scholar
  32. Haupt-Herting S, Fock HP (2002) Oxygen exchange in relation to carbon assimilation in water-stressed leaves during photosynthesis. Ann Bot 89:851–859PubMedCrossRefGoogle Scholar
  33. Hillel D, Van Bavel CHM (1976) Simulation of profile water storage as related to soil hydraulic properties. Soil Sci Soc Am J 40:s807–s815CrossRefGoogle Scholar
  34. Hura T, Hura K, Grzesiak MT, Rzepka A (2007) Effect of long-term drought stress on leaf gas exchange and fluorescence parameters in C3 and C4 plants. Acta Physiol Plantarum 29:103–113CrossRefGoogle Scholar
  35. Iijima M, Kono Y (1991) Interspecific differences of the root system structures of four cereal species as affected by soil compaction. Jpn J Crop Sci 60:130–138CrossRefGoogle Scholar
  36. Jajarmini V (2009) Effects of water stresson germination indices in seven wheat cultivar. World Acad Sci Eng Technol 45:105–106Google Scholar
  37. Johnson DA, Brown RW (1977) Psychrometric analysis of turgor pressure response; a possible technique for evaluating plant water stress resistance. Crop Sci 17:507–510CrossRefGoogle Scholar
  38. Jones HG (1993) Drought tolerance and water-use efficiency. In: Smith JAC, Griffiths H (eds) Water deficits plant responses from cell to community. Bios Scientific Publishers Limited, Oxford, pp 193–204Google Scholar
  39. King J (2011) Reaching for the sun. How plant work. Part IV Stress, defense, and decline, 2nd edn. Cambridge University Press, Cambridge, p 185–244Google Scholar
  40. Kono Y, Yamauchi A, Nonoyama T, Tatsumi T, Kawamura N (1987) A revised system of root-soil interaction for laboratory work. Environ Conltrol Biol 25:141–151CrossRefGoogle Scholar
  41. Kpoghomou BK, Sapra VT, Beyl CA (1990) Screening for drought tolerance: soybean germination and its relationship to seedling response. J Agron Crop Sci 164:153–159CrossRefGoogle Scholar
  42. Larsson S, Górny AG (1988) Grain yield and drought resistance indices of oat cultivars in field rain shelter and laboratory experiments. J Agron Crop Sci 161:277–286CrossRefGoogle Scholar
  43. Lawlor DW, Cornic G (2002) Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants. Plant Cell Environ 25:275–294PubMedCrossRefGoogle Scholar
  44. Levitt J (1980) Response of plants to environmental stresses. Academic Press, New YorkGoogle Scholar
  45. Lipiec J, Ishioka T, Szustak A, Pietrusiewicz J, Stępniewski W (1996) Effects of soil compaction and transient oxygen deficiency on growth, water use and stomatal resistance in maize. Acta Agric Scand Sect Soil Plant Sci 46:186–191Google Scholar
  46. Lopes MS, Araus JL, van Heerden PDR, Foyer CH (2011) Enhancing drought tolerance in C4 crops. J Exp Bot 62:3135–3153PubMedCrossRefGoogle Scholar
  47. Lorens GF, Bennett JM, Loggale LB (1987) Differences in drought resistance between two corn hybrids. I. Water relations and root length density. Agron J 79:802–807CrossRefGoogle Scholar
  48. Martinielio P, Lorenzoni C (1985) Response of maize genotypes to drought tolerance tests. Maydica 30:361–370Google Scholar
  49. Medrano H, Escalona JM, Bota J, Gulias J, Flexas J (2002) Regulation of photosynthesis of C3 plants in response to progressive drought: stomatal conductance as a reference parameter. Ann Bot 89:895–905PubMedCrossRefGoogle Scholar
  50. Michel BE, Wiggins KO, Outlow WHJ (1983) A guide to establishing water potential for aqueous two-phase solutions (Polyethylene glycol plus dextran) by amendment with mannitol. Plant Physiol 72:60–65PubMedCrossRefGoogle Scholar
  51. Morgan JM (1992) Osmotic components and properties associated with genotypic differences in osmoregulation in wheat. Aust J Plant Physiol 19:67–76CrossRefGoogle Scholar
  52. Muller JE, Whitsitt MS (1996) Plant cellular response to water deficit. Plant Growth Regul 20:41–46Google Scholar
  53. Naylor RES, Su J (1998) Plant development of triticale cv. Lasko at different sowing date. J Agric Sci 130:297–306CrossRefGoogle Scholar
  54. Nayyar H, Gupta D (2006) Differential sensitivity of C3 and C4 plants to water deficit stress: association with oxidative stress and antioxidants. Environ Exp Bot 58:106–113CrossRefGoogle Scholar
  55. Paknejad F, Nasri M, Moghadam HRT, Zahedi H, Alahmadi MF (2007) Effects of drought stress on chlorophyll fluorescence parameters, chlorophyll content and grain yield of wheat cultivars. J Biol Sci 7:841–847CrossRefGoogle Scholar
  56. Palta JP (1990) Stress interactions at the cellular and membrane level. Hort. Sci. 25:1337–1381Google Scholar
  57. Passioura JB, Condon AG, Richards RA (1993) Water deficits, the development of leaf area and crop productivity. In: Smith JAC, Griffiths H (eds) Water deficits plant responses from cell to community. BIOS Scientific Publishers Ltd, Oxford, pp 253–264Google Scholar
  58. Poljakoff-Mayber A (1981) Ultrastructural consequences of drought. In: Paleg LG, Aspinall D (eds) The physiology and biochemistry of drought resistance in plants. Academic Press, New York, pp 389–403Google Scholar
  59. Reynolds MP (2002) Physiological approaches to wheat breeding. In: Curtis BC, Rajaram S, Gomez Macpherson H (Eds) Bread wheat: improvement and production. Food and Agriculture Organization, Rome. pp118–140Google Scholar
  60. Reynolds MP, Singh RP, Ibrahim A, Agech OAA, Larque-Saavedra A, Quick JS (1998) Evaluation physiological traits to complement empirical selection for wheat in warm environments. Euphytica 100:84–95CrossRefGoogle Scholar
  61. Richards RA (1978) Variation between and within species of rapeseed (Brassica campestris and B. napus) in response to drought stress. III. Physiological and physicochemical characters. Aust J Agric Res 29:495–501Google Scholar
  62. Richards RA (1991) Crop improvement for temperate Australia: future opportunities. Field Crop Res 39:141–169CrossRefGoogle Scholar
  63. Richards RA (1996) Defining selection criteria to improve yield under drought. Plant Grow Regul 20:157–166CrossRefGoogle Scholar
  64. Richards RA, Thurling N (1978) Variation between and within species of rapeseed (Brasica campestris and B. napus) in response to drought stress. I. Sensitivity at different stages of development. Aust J Agric Res 29:469–477CrossRefGoogle Scholar
  65. Royo C, Abaza M, Bianco R, del Moral LFG (2000) Triticale grain growth and morphometry as affected by drought stress, late sowing and simulated drought stress. Aust J Physiol 27:1051–1059Google Scholar
  66. Smirnoff N, Colombe SV (1988) Drought influences the activity of enzymes of the chloroplast hydrogen peroxide scavenging system. J Exp Bot 39:1097–1108CrossRefGoogle Scholar
  67. Stanley CD (1990) Proper use and data interpretation for plant- and soil water status measuring instrumentation: introductory remarks. HortScience 25:1534Google Scholar
  68. Sullivan ChY, Ross WN (1979) Selecting for drought and heat resistance in grain sorghum. In: Mussel H, Staples R (eds) Stress physiology in crop plant. Wiley, New York, pp 263–281Google Scholar
  69. Tang AC, Kawamitsu Y, Kanechi M, Boyer JS (2002) Photosynthetic oxygen evolution at low water potential in leaf discs lacking an epidermis. Ann Bot 89:861–870PubMedCrossRefGoogle Scholar
  70. Tardieu F (1991) Spatial arrangement of maize roots in the field. In: McMichael BL, Person H (eds) Plant roots and their environment. Elsevier, Amsterdam, pp 506–514CrossRefGoogle Scholar
  71. Turner NC (1986) Adaptation to water deficits: a changing perspective. Aust J Plant Physiol 13:175–190CrossRefGoogle Scholar
  72. Vietor DM, Ariyanayagam RP, Musgrave RB (1977) Photosynthetic selection of Zea mays L. I. Plant age and leaf position effects and relationship between leaf and canopy rates. Crop Sci 17:567–573CrossRefGoogle Scholar
  73. Williams MH, Rosenqvist E, Buchhave M (1999) Response of potted miniature roses (Rosaxhybrida) to reduced water availability during production. J Hortic Sci Biotechnol 74:301–308Google Scholar
  74. Winter SR, Musick JT, Porter KB (1988) Evaluation of screening techniques for breeding drought resistant winter wheat. Crop Sci 28:512–516CrossRefGoogle Scholar
  75. Zagdańska B (1992) Physiological criteria for estimation of plant resistance to drought. Biul Inst Hod i Aklimatyzacji Roślin 183:11–19 (In Polish)Google Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Maciej T. Grzesiak
    • 1
    Email author
  • Piotr Waligórski
    • 1
  • Franciszek Janowiak
    • 1
  • Izabela Marcińska
    • 1
  • Katarzyna Hura
    • 2
  • Piotr Szczyrek
    • 3
  • Tomasz Głąb
    • 4
  1. 1.The F. Górski Institute of Plant PhysiologyPolish Academy of SciencesKrakówPoland
  2. 2.Department of Plant PhysiologyAgricultural UniversityKrakówPoland
  3. 3.Institute of BiologyPedagogical UniversityKrakówPoland
  4. 4.Institute of Machinery ExploitationAgricultural UniversityKrakówPoland

Personalised recommendations