Root-Zone Salinity: II. Indices for Tolerance in Agricultural Crops
H. Steppuhn,* M. Th. van Genuchten, and C. M. Grieve

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

ABSTRACT

Maas, 1990). Averaged spatially and temporally, thesalinity (C) of the subsurface interstitial solutions canbe measured in solute concentration, osmotic potential,
or electrical conductivity. As detailed in the companion
paper, Steppuhn et al. (2005) showed that the modified
compound-discount function,

This paper provides the tools for distinguishing levels of tolerance
to root-zone salinity in agricultural crops. Such distinction rests on the
response of a crop’s product yield following the declining, sigmoid-shaped,
modified compound-discount function {Yr ⫽ 1/[1 ⫹ (C/C50)exp(sC50)]}for plants grown as crops exposed to increasing root-zone salinity.
This nonlinear function relates relative yield (Yr) to root-zone salinity(C ) measured in equivalent saturated soil-paste extract electrical conductivity with two nonlinear parameters, the salinity level producing
50% of the nonsaline crop yield (C50) and a response curve steepness
constant (s ) equal to the absolute value of the mean dYr/dC fromYr ⫽ 0.3 to 0.7. These discount parameters suggest the existence ofa single-value salinity tolerance index (ST-Index) equal to the 50%
reduction in crop yield from that of the nonsaline yield plus a tendency
to maintain some product yield as the crop is subjected to salinity
levels approaching C50, i.e., ST-Index ⫽ C50 ⫹ s(C50). The explicit
purpose of this study is to determine if the discount function using
biophysically relevant parameters can be applied to historical data
sets. Approximations for C50 and s were identified in the thresholdsalinity (Ct) and declining slope (b ) parameters of the well-known
threshold-slope linear response function. Several procedures for converting Ct to C50 and b to s offer the linkage between these linear andnonlinear response functions. From these procedures, two regression
equations, C50 ⫽ 0.988[(0.5/b ) ⫹ Ct] ⫺ 0.252 and s ⫽ 1.52b, proved
the most appropriate for the eight representative field, forage, andvegetable crops tested. The selected conversion procedures were applied to previously published Ct and b values to obtain a list of therelative root-zone salinity tolerance in agricultural crops. In addition
to C50 and s, values for exp(sC50) and the ST-Index were computedfor each crop. The revised list provides extension personnel and plant
growth modelers the parameter values from a nonlinear analog ofcrop yield response to root-zone salinity.

Yr ⫽ 1/[1 ⫹ (C/C50)exp(sC )]50

resulted in the lowest root mean square error among
the six functions tested. Equation [2] describes a function with two biophysically based parameters: C50, thesalinity (C) at Yr ⫽ 0.5, and s (a steepness parameter)
identified as an approximate estimate of the absolutevalue of the mean dYr/dC for the equation from Yr ⫽0.3 to 0.7.
If the term p is substituted for [exp(sC50)] in Eq. [2],a form of the modified discount function results, which
was introduced by van Genuchten (1983) and van Genuchten and Hoffman (1984) and used by van Genuchten
and Gupta (1993) and Steppuhn et al. (1996):
Yr ⫽ 1/[1 ⫹ (C/C50)p][3]

where p is shape parameter with no biophysical characteristic.
If C50 and s are combined such that the salinity levelassociated with a 50% yield reduction (C50) plus a measure of the tendency to maintain some product yield as
the crop is subjected to increasing salinity levels approaching C50, a comparative, single-value, salinity tolerance index (ST-Index) is defined:
ST-Index ⫽ C50 ⫹ sC50

T

he relative yield of an agricultural crop grown inincreasingly saline rooting media has become theprimary criterion with which to indicate the crop’s inherent tolerance or resistance to salinity (U.S. Salinity Laboratory Staff, 1954; Ayers and Westcot, 1985; Katerji etal., 1992). If Y represents the absolute yield and Yr
the relative yield of a test crop rooted in a series ofincrementally increasing saline environments,
Yr ⫽ Y/Ym

[2]

[4]

The ST-Index is proposed as an indicator of the inherent
salinity tolerance or resistance of agricultural crops toroot-zone salinity.
Since 1978, almost all crop salt-tolerance lists in theliterature follow the first and second line segments ofthe three-piece linear response function. This function
was proposed by Maas and Hoffman (1977) as thethreshold-slope model and functionalized by van Genuchten (1983):[1]

where Ym designates the yield of the crop when grownin a root zone free of salinity (Maas and Hoffman, 1977;
H. Steppuhn, Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Box 1030, Swift Current, Saskatchewan,
Canada S9H 3X2; M.Th. van Genuchten, Soil Physics/Pesticide Unit,
George E. Brown, Jr. Salinity Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Riverside, CA; C.M. Grieve,
Plant Sciences Group, George E. Brown, Jr. Salinity Laboratory,
Agricultural Research Service, U.S. Department of Agriculture, Riverside, CA. Received 8 Sep. 2003. *Corresponding author (SteppuhnH@
agr.gc.ca).

Yr ⫽ 10 ⬍ C ⬍ Ct

Yr ⫽ 1 ⫺ b(C ⫺ Ct)

Ct ⬍ C ⬍ C 0

Yr ⫽ 0C ⬎ C0[5]where b is the absolute value of the declining slope in
Yr with C, Ct is the maximum value of salinity withouta yield reduction (the threshold C), and C0 is the lowest
value of C where Yr ⫽ 0. The two-piece, threshold-slope
response function (the first and second linear segments)
Abbreviations: ECe, electrical conductivity of saturated soil paste extract; ECi, electrical conductivity of the irrigated water; ECs, electricalconductivity of test solution; FAO, Food and Agriculture Organization, United Nations; ST-Index, salinity tolerance index.

Published in Crop Sci. 45:221–232 (2005).
© Crop Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA

221

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CROP SCIENCE, VOL. 45, JANUARY–FEBRUARY 2005

Table 1. Selected-line-segments procedure for converting the linear parameters of Ct and b to the discount parameters of C50 and s by
selecting points from the horizontal and declining straight lines of the threshold-slope function, where C ⫽ ECe in dS m⫺1.
Step
1
2

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3
4
5
6
7
8

Procedure
Solve the middle equation of the three-piece linear model [Yr ⫽ 1 ⫺ b(C0.5 ⫺ Ct)] for C0.5, the mid-point of the declining slope, where Yr ⫽0.5, i.e., C0.5 ⫽ Ct ⫹ (0.5/b )Select additional C-points from the threshold-slope lines: ⫾ 0.5 dS m⫺1, ⫾ 1 dS m⫺1, ⫾ 2 dS m⫺1, etc. of C0.5 from the declining line, and C ⫽1, 2, and 3 dS m⫺1 from the horizontal line
Using the linear threshold-slope model, calculate relative yields (Yrlin) for the 10 or more selected points
Regress Yrlin with C by the modified discount function {Yr ⫽ 1/[1 ⫹ (C/C50)exp(sC50)]} to determine Yrm as a regression parameter; generally, thisYrm–value will deviate from 1.0
Subtract 1.00 from Yrm to determine the Yr offset
Rescale the linear relative yields (Yrlin) into nonlinear relative sigmoid yields (Yrs) with the Yr offset applied to all Yrlin values for the selected
C points
Regress the sigmoidal Yrs with C by the modified discount function {Yr ⫽ 1/[1 ⫹ (C/C50)exp(sC50)]} to determine C50 and s as regression parameters
Using p ⫽ exp(sC50), calculate p

has served as an approximation of the modified discountfunction. Its parameters Ct and b provided the basis forsalinity tolerance lists for 25 yr. The one exception is alist presented by van Genuchten and Gupta (1993)
based on the discount model of Eq. [3]. Their list relies
on two different regression parameters (C50 and p) to
index the salt-tolerance relationship between degree ofsalinity and relative crop yield.
In our companion paper (Steppuhn et al., 2005), we
submitted the argument that the product yields of agricultural crops relate more closely to the modified discount function rather than to the threshold slope model.
Unfortunately, only limited data are available for the
calculation of C50, s, the ST-Index, and for the generationof an associated crop salt-tolerance list. Thus, the objectives of this study were to evaluate different methods
for converting the respective linear threshold-slope parameters of Ct and b to C50 and s of the nonlinear modified discount function and to apply the most appropriateof these conversions to a current threshold-slope crop
list for salinity tolerance. Besides conversion to the nonlinear parameters of C50 and s, the selected methods
would serve to calculate p and the ST-Index, which, inturn, were used to generate a revised list of the relative
salinity tolerances in agricultural crops.

Fig. 1. Typical crop yield response to increasing root-zone salinity described by the modified discount and the threshold-slope functions.

CONVERSION METHODS
If the linear, threshold-slope response model of crop yieldwith increasing root-zone salinity serves as an approximationof the nonlinear modified-discount response function, it
should be possible to evaluate the parameters of the nonlinear
function from relationships on the basis of the linear approximation. In other words, if Ct and b are known for any crop,
this information can be used to estimate C50, s, and p for thecrop. In this paper, we evaluate several methods for convertingCt and b to C50 and s: (i) a direct method, (ii) an analyticalmethod, and (iii) several empirical methods.

Direct Conversion
The most general method of determining C50 and s from Ct
and b follows a selected-line-segment procedure (Table 1). In
this method, selected pairs of relative yield and root-zone
salinity are calculated from the two linear segments of thethreshold-slope model and used in nonlinear regressions to
fit a least-squares discount curve giving the parameter estimates of C50 and s. The merits of this method are that bothnonlinear parameters are determined together and that themethod universally applies to all salt-tolerance response data
which have been or will be analyzed with the threshold-slope
function.

Analytical Conversion
Typically, the response data of relative crop yield with increasing root-zone salinity vary. A nonlinear statistical fit of
the modified discount response function to such data by appropriate software, e.g., JMP (SAS, 1995), results in estimates of
C50 and s and in a fitted plot of the function (Fig. 1). A
threshold-slope analysis of the same data also provides a fitted
functional plot but with parameters Ct and b (Fig. 1). Theseplots reveal (i) that the functions each relate to the same data,(ii) that the inflection point of the discount curve likely falls
on or close to the threshold-slope line, (iii) that s ⬎ b (i.e.,the value of s from the discount curve is greater than theabsolute value of the slope b of the threshold-slope model),(iv) that the salinity levels for Cmid and C50 (where Yr equals
half of the salinity-free relative yields of their respective linear
and the nonlinear response functions) are very nearly equal,and (v) that, as indicated by van Genuchten and Hoffman(1984), Maas (1990), and Maas and Grattan (1999), the discount plot more precisely describes the response data.
Our analytical and some of our empirical conversions are
based on analyses of midpoints of the discount and the threshold-slope response models. The slope of the Eq. [3] discountcurve is given by its first derivative:(dYr/dC) ⫽ ⫺[1 ⫹ (C/C50)p]⫺2 (C/C50)p ⫺ 1 (p/C50) [6]STEPPUHN ET AL.: INDICES FOR SALINITY TOLERANCES OF AGRICULTURAL CROPS

223

If, for any value of C, the absolute value of the first derivative
is set equal to the steepness parameter s, then,s ⫽ |dYr/dC|.

From Eq. [6],s ⫽ [1 ⫹ (C/C50)p]⫺2 (C/C50)p ⫺ 1 (p/C50)orp ⫽ (sC50) [1 ⫹ (C/C50)p]2 (C/C50)1 ⫺ p

[7]
[8]

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

At the inflection point of the discount function, the second
derivative of Eq. [3] is equal to zero:dYr2/dC2 ⫽ ds/dC ⫽ 0which simplifies to[9](C/C50) ⫽ [(p ⫺ 1)/(p ⫹ 1)]

1/p

[10]

Substitution of Eq. [10] into Eq. [8] and simplification leads tosC50 ⫽ [(p2 – 1)/4p] [(p ⫹ 1)/(p ⫺ 1)]1/p[11]

which, as will be shown later, provides one method of quasiempirical regression between s and b.

Empirical Conversion
Over the years, scientists at the U.S. Salinity Laboratory
have collected the results from a large number of salt tolerance
response tests conducted worldwide (Francois and Maas, 1978,
1985; Ulery et al., 1998). These data sets formed the basis forresponse-function studies by Maas and Hoffman (1977), vanGenuchten and Hoffman (1984), and van Genuchten and
Gupta (1993).
In the latter study, this database was divided into fourgroups: field, forage, vegetable, and fruit-tree crops. Most
fruit-tree data sets were discarded because of generally too
few or unreliable experimental data. Of the remaining data
sets, some were also judged to be unsuitable because of insufficient or unreliable data. Typically, the unused data contained
as few as three data pairs, exhibited severe scattering in thedata points, or clustered heavily within only one part of the
response function. The remaining salt tolerance database consisted of experiments involving 45 field crops, 62 forage crops,
and 57 vegetable and fruit crops, giving a total of 164 data
sets. These formed the core data utilized in this study fromwhich the values for Ct, b, C50, s, and p were obtained either
from the original reports of the experiments or from analysesof the original data.

Fig. 2. The modified discount C50 parameter derived from a regression
with the threshold salinity (Ct) of the threshold-slope linear modelfor the core data sets. (C50 ⫽ 2.786 ⫹ 1.891Ct) (R2 ⫽ 0.77, RMS
error ⫽ ⫾ 2.3 dS m⫺1)

and ⫾ 0.53 dS m⫺1, respectively. The statistical relationshipfrom this regression,

C50 ⫽ 0.988Cmid ⫺ 0.252[15]

indicated that both the slope and the intercept were statisticallysignificant (p␣ ⱕ 0.01) and that C50 very nearly equaled Cmid.
Converting b to s
A linear regression to establish a direct relationship of s asa function of b using the core data sets resulted in a R2 valueof 0.746 with the RMS error ⫽ ⫾ 0.058 (dS m⫺1)⫺1 (Fig. 4):s ⫽ 1.523b ⫺ 0.0015[16]

wherein the intercept was not statistically different from zero.
However, s can also be calculated from p by Eq. [11]. But, alinear regression of p as a fit of b using the same data correlatedwith R2 equal to only 0.164 (data not shown).

Converting Ct to C50
To ascertain if C50 could reliably be determined from Ct
empirically, values of the two parameters obtained from thecore data sets were linearly regressed (SAS, 1995). The threshold salinity (Ct) explained some 77% of the variation analyzed
in the C50 data within a root mean square error (RMS error)of ⫾ 2.3 dS m⫺1 (Fig. 2).
Another approach involved the middle segment of thethreshold-slope function. Solving this segment of Eq. [5] forC gaveAt C ⫽ Cmid,and, hence,C ⫽ [(1 ⫺ Yr)/b] ⫹ Ct
Yr ⫽ 0.5
Cmid ⫽ (0.5/b) ⫹ Ct

[12]
[13]
[14]

From Fig. 1, C50 would seem to be empirically related toCmid, especially if the inflection point of the discount curve
falls on or close to the threshold-slope line. Consequently, a
linear regression of C50 as a function of Cmid was conductedwith values from the core data sets (Fig. 3). The resulting
coefficient of determination (R2) and RMS error equaled 0.98

Fig. 3. The modified discount C50 parameter derived from a regression
with the salinity (Cmid) at 0.5 of the relative yield (Yr) in the threshold-slope linear model for the core data sets. (C50 ⫽ ⫺0.252 ⫹0.988Cmid) (R2 ⫽ 0.98, RMS error ⫽ ⫾ 0.53 dS m⫺1)

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224

CROP SCIENCE, VOL. 45, JANUARY–FEBRUARY 2005

Fig. 4. The steepness parameter (s ) of the modified discount function
derived from a regression with the slope (b ) of the three-piecelinear model for the core data sets. (s ⫽ 1.523b) [R2 ⫽ 0.746, RMS
error ⫽ ⫾ 0.058 (dS m⫺1)⫺1]

If, for convenience, the right side of Eq. [11] is expressed
as Fn(p ), and moved to the left side, and s is replaced by
1.52b of Eq. [16],Fn(p) ⫽ 1.52bC50[17]Further, if the expression for C50 in Eq. [15] is substituted into
Eq. [17] and consolidated,

Fn(p) ⫽ 1.50bCmid ⫺ 0.383b[18]

Next, if Cmid of Eq. [14] is substituted into Eq. [18]:Fn(p) ⫽ b(1.50Ct ⫺ 0.383) ⫹ 0.75

[19]Equation [19] suggests that a regression ofFn(p) ⫽ Function(bCt)[20]

using the core data set could provide an empirical link between
p and b. An exponential transformation leads to two otherpossible regression relationships,

Fn(p) ⫽ Function[exp(bCt)]
ln[Fn(p)] ⫽ Function(bCt)

[21]
[22]

In addition, Eq. [20] and the relationship, p ⫽ exp(sC50),
from Eq. [2] and [3] suggest that three more possible regression
fits of p or ln(p ) by (bCt) might serve as candidates for converting b to p and then to s:
Table 2. Coefficient of determination (R2) and root mean squareerror (RMS error) for six empirical relationships for converting
linear slope (b ) and threshold salinity (Ct) parameters to thediscount p parameter from the core data set.
RMS error
Relationship†

R

2

N‡

Fn(p )

Fn(p ) ⫽ exp(bCt)
Fn(p ) ⫽ (bCt)ln[Fn(p )] ⫽ (bCt)p ⫽ exp(bCt)p ⫽ (bCt)ln(p ) ⫽ (bCt)

0.60
0.58
0.57
0.55
0.54
0.48

158
158
158
161
161
158

0.147
0.151
0.157

p

0.763
0.771
0.771

† p ⫽ prevention parameter, b ⫽ slope of the relative yield with salinity
relationship, Ct ⫽ threshold salinity, Fn(p ) ⫽ function of p derived fromthe second derivative of the discount response equation set equal tozero and simplified: Fn(p ) ⫽ [(p2 – 1)/4p] [(p ⫹ 1)/(p ⫺ 1)]1/p.
‡ N ⫽ number of data pairs [ln(p ) ⬎ 0.0; 1.0 ⬍ p ⬍ 10.0].

Fig. 5. Regression of the function Fn(p ) derived from the second
derivative of the discount equation set to zero with the exponentialof the product of the linear threshold-slope parameters, slope (b ), andthreshold salinity (Ct), for the core data sets. {Fn(p) ⫽ [(p2 – 1)/4p][(p ⫹ 1)/(p ⫺ 1)]1/p ⫽ ⫺0.245 ⫹ 0.862 [exp(bCt)]} (R2 ⫽ 0.60, RMS
error ⫽ ⫾ 0.147)

ln(p) ⫽ Function(bCt)p ⫽ Function[exp(bCt)]
p ⫽ Function(bCt)

[23]
[24]
[25]

The six regressions (Eq. [20] through [25]) were conductedwith a variable number of core data sets automatically entering
each regression depending on the number of sets that contained a value of p within the range of 1 ⬍ p ⬍ 10. As outlinedin Table 2, comparisons of the statistics from the six regressions
with bCt for converting b to p favor Eq. [21], and is plottedin Fig. 5. Once Fn(p ) was determined, we used a simple linear
regression (R2 ⫽ 0.988, 2.5 ⬍ p ⬍ 10.0),

Fn(p) ⫽ 0.10601 ⫹ 0.24075p[26]

to determine p from Fn(p) and the relationship, s ⫽ ln(p)/C50,
to obtain s.
Selecting Conversion Methods
Relative crop yields measured in eight salt-tolerance response experiments were used to compare the precision associated with parameter-conversion methods (Table 3). The methods for converting Ct and b to C50 and s were applied tothe measured data from three field, three forage, and twovegetable experiments with eight different crops. The data
were reported in four experiments taken from within the coredata sets and four from separate sets. The test experiments
provided values for the threshold salinity (Ct), linear slope(b ), and mid-point salinity (Cmid) used in the comparisons(Table 3). Nonlinear discount regressions with the actual experimental response data resulted in best-fit values for C50
and s for each test experiment against which the conversion
methods were compared. The methods used to convert thelinear parameters of the eight crop responses (experiments)
included the empirical conversions based on the respectivey ⫻ x regression fits of C50 ⫻ (Ct) and C50 ⫻ (Cmid) shown in
Fig. 2 and 3, s ⫻ (b ) in Fig. 4, and Fn(p ) ⫻ exp(bCt) withs ⫽ ln(p )/C50 in Fig. 5. The selected-line-segments procedurefrom Table 1 provided the third conversion method for both
C50 and s.

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STEPPUHN ET AL.: INDICES FOR SALINITY TOLERANCES OF AGRICULTURAL CROPS

Table 3. Threshold salinity (Ct) and slope (b ) determined by the three-piece linear model relating relative yield (Yr) to salinity (C ),and salinity (Cmid) at 0.5Yr for three field, three forage, and two vegetable crops on the basis of reported tests.†

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

Crop
Rye (grain)
Sorghum (grain)
Wheat
Harding grass
Perennial Ryegrass
Alfalfa
CarrotTurnip

Ct

b

Cmid

dS m⫺1(dS m⫺1)⫺1

dS m⫺1

Reference

9.40
6.80
2.88
4.62
5.60
1.25
1.01
0.75

0.0726
0.15900.15140.07630.07620.07510.17100.0885

16.29
9.95
6.18
11.17
12.16
7.91
3.94
6.40

Francois et al., 1989
Francois et al., 1984
USSL, 1979
Brown and Bernstein, 1953
Brown and Bernstein, 1953
Brown and Hayward, 1956
Magistad et al., 1943; Osawa, 1965
Francois, 1984

† USSL ⫽ Unpublished U.S. Salinity Laboratory data.

Table 4. Eight crop comparisons of the discount C50 parameter computed by three conversion methods [selected-line-segments, linear
threshold (Ct), and linear mid-salinity (Cmid)] with percent difference from the C50 derived from actual data points in parentheses.
C50
Crop and data source

Actual data points

Selected line segments

dS m⫺1Rye (grain)
Francois et al., 1989
Sorghum (grain)
Francois et al., 1984
Wheat
USSL, 1979
Harding grass
Brown and Bernstein, 1953
Perennial ryegrass
Brown and Bernstein, 1953
Alfalfa
Brown and Hayward, 1956
CarrotMagistad, 1943 and Osawa, 1965
TurnipFrancois, 1984

16.40
N ⫽ 12
10.18
N ⫽ 12
5.98
N⫽8
11.05
N⫽8
11.97
N⫽8
7.66
N ⫽ 12
4.04
N ⫽ 12
5.97
N⫽4C50 ⫽ (Ct)

C50 ⫽ (Cmid)dS m⫺1 (% of actual)17.41 (6.13)
N ⫽ 129.90 (⫺2.70)
N ⫽ 11
6.09 (1.88)
N ⫽ 11
10.88 (⫺1.55)
N ⫽ 1112.09 (1.05)
N ⫽ 107.68 (0.33)
N ⫽ 114.42 (9.41)
N ⫽ 12
6.51 (8.93)
N ⫽ 12

20.56 (25.33)

15.836 (⫺3.46)

15.65 (53.78)

9.58 (⫺5.88)

8.22 (37.50)

5.85 (⫺2.18)

11.52 (4.21)

10.78 (⫺2.44)

13.38 (11.89)

11.76 (⫺1.69)

5.14 (⫺32.85)

7.56 (⫺1.30)

4.70 (16.35)

3.64 (⫺9.90)

4.49 (⫺24.84)

6.13 (1.71)

Table 5. Eight crop comparisons of the discount steepness parameter s computed by three conversion methods [selected-line-segments,s ⫻ Fn(p ), and s ⫻ (b )] with percent difference from the s derived from actual data points in parentheses.
s
Crop and data source
Rye (grain)
Francois et al., 1989
Sorghum (grain)
Francois et al., 1984
Wheat
USSL, 1979‡
Harding grass
Brown and Bernstein, 1953
Perennial ryegrass
Brown and Bernstein, 1953
Alfalfa
Brown and Hayward, 1956
CarrotMagistad, 1943 and Osawa, 1965
TurnipFrancois, 1984

Actual data points(dS m⫺1)⫺1
0.1072N ⫽ 120.2202N ⫽ 120.2308N⫽80.1151N⫽80.1114N⫽80.1128N ⫽ 120.2592N ⫽ 120.1251
N⫽4

Selected line segments
0.0891 (⫺16.87)
N ⫽ 120.1971 (⫺10.48)
N ⫽ 110.2290 (⫺0.79)
N ⫽ 110.1142 (⫺0.78)
N ⫽ 110.1096 (⫺1.66)
N ⫽ 100.1154 (2.27)
N ⫽ 110.2173 (⫺16.17)
N ⫽ 120.1142 (⫺8.71)
N ⫽ 12s ⫻ Fn(p )†(dS m⫺1)⫺1 (% of actual)0.1054 (⫺1.67)

s ⫻ (b )0.1105 (3.16)

0.2175 (⫺1.23)

0.2417 (9.77)

0.2341 (1.44)

0.2306 (⫺0.08)

0.1161 (⫺0.83)

0.1162 (0.96)

0.1160 (4.14)

0.1161 (4.17)

0.1157 (2.61)

0.1143 (1.36)

0.2510 (⫺3.17)

0.2604 (0.46)

0.1422 (13.65)

0.1348 (7.75)† Regression fit of Fn(p ) ⫻ [exp(bCt)] and s ⫽ ln(p )/C50‡ STTL, unpublished data, U.S. Salinity Laboratory.

RESULTS AND APPLICATION
OF CONVERSIONS
Given the inherent variability associated with productyields from crops grown in environments with increasing
root-zone salinity, the errors in parameter conversions
from linear to nonlinear response functions could notreasonably be expected to fall much less than ⫾10%of the actual values. The direct regression method [C50fit ⫻ (Ct)] for converting Ct to C50 using the two parame-ters failed to achieve the ⫾ 10% error level in sevenout of eight test experiments (Table 4). Both the selected-line-segments and the fitted C50 ⫻ (Cmid) methods
realized C50 values for all eight test experiments fallingwithin the 10% error limit. In five out of the eight
experiments, the C50 error stayed within a limit of ⫾5%
in the selected-line-segment method and six out of eightin the C50 fit ⫻ (Cmid) method.
In comparing methods for converting b to s, the226

CROP SCIENCE, VOL. 45, JANUARY–FEBRUARY 2005

Table 6. Salinity tolerance of agricultural crops.†
Nonlinear tolerance parameter

Crop
Common name

Botanical name‡

Tolerance§
based onC50 (ECe)
Salinity
dS/m p Shape s Steepness tolerance index

References

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

Fiber, grain, and special crops
Artichoke, Jerusalem
Barley¶ (irrigated)

Helianthus tuberosus L.
Hordeum vulgare L.

Tuber yield
Grain yield

5.29
17.53

2.17
3.80

0.146
0.076

6.06
18.87

Barley# (dryland)
Canola or rapeseed

Grain yield
Seed yield

7.51
12.86

2.18
12.46††

0.104
0.213

8.29
15.60

Canola or rapeseed
Canola# (dryland)
Corn‡‡

Hordeum vulgare L.
Brassica campestris L.
[syn. B. rapa L.]
B. napus L.
B. napus L.
Zea mays L.

Seed yield
Seed yield
Ear FW

14.42
7.10
5.54

13.50††
2.46
2.75

0.198
0.126
0.183

17.27
8.00
6.56

CottonGossypium hirsutum L.

Seed cotton
yield

16.86

3.80

0.079

18.19

Crambe

9.32

2.52

0.099

10.25

5.54
11.35

2.75
18.88

0.183
0.259

6.56
14.29

Hayward and Spurr, 1944
Francois et al., 1990

Kenaf
PeanutRice, paddy§§

Crambe abyssinica Hochst.
Seed yield
ex R. E. Fries
Linum usitatissimum L.
Seed yield
Cyamopsis tetragonoloba (L.) Seed yield
Taub.
Hibiscus cannabinus L.
Stem DW
Arachis hypogaea L.
Seed yield
Oryza sativa L.
Grain yield

Francois, 1994a
Steppuhn et al., 2002
Bernstein and Ayers, 1949b (p.
41–42); Kaddah and Ghowail,1964
Bernstein, 1955 (p. 37–41), 1956
(p. 33–34); Bernstein andFord, 1959aFrancois and Kleiman, 1990

12.01
4.61
6.83

8.35
7.67
3.48

0.177
0.442
0.183

14.13
6.65
8.08

Rye
Sorghum
SoybeanSecale cereale L.
Grain yield
Sorghum bicolor (L.) Moench Grain yield
Glycine max (L.) Merrill
Seed yield

15.84
9.57
7.16

5.76
10.16
8.85

0.111
0.242
0.305

17.59
11.89
9.34

Sugar beet¶¶
Sugarcane

Beta vulgaris L.
Saccharum officinarum L.

Storage root
Short DW

15.04
9.80

3.86
2.41

0.090
0.090

16.39
10.68

Sunflower
Triticale
Wheat, leavened
bread (irrigated)

Helianthus annuus L.
⫻ Triticosecale Wittmack
Triticum aestivum L.

Seed yield
Grain yield
Grain yield

14.37
25.53
12.63

2.99
2.64
3.92

0.076
0.038
0.108

15.46
26.51
14.00

Wheat, leavened
bread (irrigated)
Wheat, leavened
bread# (dryland)
Wheat, flat bread#(dryland)
Wheat, pastry#
Wheat (semidwarf)##(irrigated)
Wheat, Durum(irrigated)
Wheat, Durum#(dryland)Triticum aestivum L.

Grain yield

5.85

3.85

0.242

7.89

Francois et al., 1992
Shalhevet et al., 1969
Ehrler, 1960; Narale et al., 1969;
Pearson, 1959; Venkateswarlu
et al., 1972
Francois et al., 1989
Francois et al., 1984
Abel and MacKenzie, 1964;
Bernstein et al., 1955 (p.
35–36); Bernstein and Ogata,
1966
Bower et al., 1954
Bernstein et al., 1966; Dev andBajwa, 1972; Syed and ElSwaify, 1972
Cheng, 1983; Francois, 1996
Francois et al., 1988
Asana and Kale, 1965; Ayers etal., 1952; Hayward and Uhvits,
1944
USSL, 1979

Triticum aestivum L.

Grain yield

2.76

1.67

0.186

3.27

Steppuhn and Wall, 1997

Triticum aestivum L.

Grain yield

2.97

2.25

0.273

3.78

Steppuhn and Wall, 1997

Triticum aestivum L.
Triticum aestivum L.

Grain yield
Grain yield

6.06
24.71

3.65
3.09

0.214
0.046

7.35
25.84

Steppuhn and Wall, 1997
Francois et al., 1986

T. turgidum L. var. durum
Desf.
T. turgidum L. var. durum
Desf.

Grain yield

18.58

2.93

0.058

19.65

Francois et al., 1986

Grain yield

5.36

3.67

0.243

6.66

Steppuhn and Wall, 1997

Alfalfa

Medicago sativa L.

Shoot DW

8.49

2.57

0.111

9.43

Alfalfa#
Barley (forage) ¶Medicago sativa L.
Hordeum vulgare L.

Shoot DW
Shoot DW

6.20
12.63

1.80
3.92

0.095
0.108

6.79
14.00

Bermudagrass†††

Cynodon dactylon L. Pers.

Shoot DW

14.28

4.02

0.097

15.68

Bromegrass, smooth
Broadbean
Clover, alsike
Clover, Berseem

Bromus inermis Leyss.
Vicia faga L.
Trifolium hybridum L.
T. alexandrinum L.

Shoot DW
Shoot DW
Shoot DW
Shoot DW

16.10
6.47
5.35
9.90

4.53
2.58
2.66
2.36

0.094
0.146
0.183
0.087

17.61
7.42
6.32
10.76

Clover, ladino

Trifolium repens L.

Shoot DW

5.35

2.66

0.183

6.32

Clover, red

T. pratense L.

Shoot DW

5.35

2.66

0.183

6.32

Bernstein and Francois, 1973;
Bernstein and Ogata, 1966,
Bower et al., 1969; Brown andHayward, 1956; Gauch andMagistad, 1943; Hoffman etal., 1975
Steppuhn et al., 1999
Dregne, 1962; Hassan et al.,1970aBernstein and Ford, 1959b (p.
39–44); Bernstein andFrancois, 1962 (p. 37–38);
Langdale and Thomas, 1971
McElgunn and Lawrence, 1973
Ayers and Eberhard, 1960
Ayers, 1948aAsghar et al., 1962; Ayers andEberhard, 1958 (p. 36–37);
Ravikovitch and Porath, 1967;
Ravikovitch and Yoles, 1971
Ayers, 1948b; Gauch
and Magistad, 1943
Ayers, 1948b; Saini, 1972

Flax
Guar

Newton et al., 1991
Ayers et al., 1952; Hassan
et al., 1970aSteppuhn, 1993
Francois, 1994aGrasses and forage crops

Continued next page.

227

STEPPUHN ET AL.: INDICES FOR SALINITY TOLERANCES OF AGRICULTURAL CROPS

Table 6. Continued.
Nonlinear tolerance parameter

Crop

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

Common name

Botanical name‡

Tolerance§
based onC50 (ECe)
Salinity
dS/m p Shape s Steepness tolerance index

Clover, strawberryT. fragiferum L.

Shoot DW

5.35

2.66

0.183

6.32

Corn (forage)‡‡Zea mays L.

Shoot DW

8.20

2.52

0.113

9.13

Cowpea (forage)
Fescue, tall

Vigna unguiculata (L.) Walp. Shoot DWFestuca elatior L.
Shoot DW

6.71
12.92

3.08
2.84

0.168
0.081

7.83
13.96

Fescue, tall# (dryland)

Festuca arundinacea
Schreber
Alopecurus pratensis L.

Shoot DW

7.97

1.94

0.083

8.63

Shoot DW

6.38

2.54

0.146

7.31

Shoot DW

10.79

3.49

0.116

12.04

Kochia#, Sask.
New Mexico
Lovegrass‡‡‡Phalaris tuberosa L. var.
Stenoptera (Hack) A.S.
Kochia scoparia (L.) Schrad.
Kochia scoparia (L.) Schrad.
Eragrostis sp. N.M. Wolf

Shoot DW
Shoot DW
Shoot DW

21.42
21.64
7.60

3.28
3.29
2.65

0.055
0.055
0.128

22.61
22.83
8.58

Orchardgrass

Dactylis glomerata L.

Shoot DW

9.20

2.38

0.094

10.07

Ryegrass, perennial

Lolium perenne L.

Shoot DW

11.78

3.91

0.116

13.14

Sesbania

Sesbania exaltata (Raf.) V.L. Shoot DW
Cory
Sphaerophysa salsula (Pall.) Shoot DW
DC
Sorhum sudanense (Piper)
Shoot DW
Stapf.
Lotus pedunculatus Cav.
Shoot DW
L. corniculatus var
Shoot DW
tenuifolium L.
Vicia angustifolia L.
Shoot DW
Agropyron sibiricum (Willd.) Shoot DW
Beauvois
A. cristatum (L.) Gaertner
Shoot DW

9.08

2.60

0.107

10.05

Foxtail, meadow
Hardinggrass

Sphaerophysa

References
Ayers, 1948b; Bernstein andFord, 1959b (p. 39–44); Gauch
and Magistad, 1943
Hassan et al., 1970b;
Ravikovitch, 1973; Ravikovitch
and Porath, 1967
West and Francois, 1982
Bower et al., 1970; Brown andBernstein, 1953 (p. 44–46)
Steppuhn, 1997
Brown and Bernstein, 1953
(p. 44–46)
Brown and Bernstein, 1953
(p. 44–46)
Steppuhn, 1990
Steppuhn, 1990
Bernstein and Ford, 1959b(p. 39–44)
Brown and Bernstein, 1953
(p. 44–46); Wadleigh et al.,
1951
Brown and Bernstein, 1953
(p. 44–46)
Bernstein, 1956 (p. 33–34)

8.98

2.60

0.107

9.94

14.00

2.50

0.065

14.92

4.62
9.63

3.81
4.33

0.289
0.152

5.96
11.09

7.20
15.56

3.34
2.58

0.168
0.061

8.41
16.50

14.32

4.50

0.105

15.82

Shoot DW

7.72

2.17

0.100

8.49

Ayers, 1948a,b (p. 23–25)
Ayers, 1948a,b (p. 23–25); Ayers,
1950
Ravikovitch and Porath, 1967
Bernstein and Ford, 1958
(p. 32–36)
Bernstein and Ford, 1958
(p. 32–36)
Steppuhn, 1997

Shoot DW

7.16

1.97

0.095

7.84

Steppuhn, 1997

Shoot DW

18.92

3.35

0.065

20.13

Shoot DW
10.65
2.65
Vegetable, nut, and fruit crops

0.091

11.62

Bernstein and Ford, 1958
(p. 32–36)
Brown and Bernstein, 1953

Shoot growth

3.83

3.03

0.289

4.94

Apricot
Artichoke
Asparagus
Bean, common

Prunus duclis (Mill.) D.A.
Webb
Prunus armeniaca L.
Cynara scolymus L.
Asparagus officinalis L.
Phaseolus vulgaris L.

Shoot growth
Bud yield
Spear yield
Seed yield

3.39
10.07
28.50
3.34

3.45
5.83
2.38
2.63

0.366
0.175
0.030
0.289

4.63
11.83
29.37
4.30

Bean, mungBeet, red¶¶

Vigna radiata (L.) R. Wilcz.
Beta vulgaris L.

Seed yield
Storage root

3.91
9.19

3.43
3.52

0.315
0.137

5.15
10.45

Blackberry

Rubus macropetalus Doug.
ex Hook
Rubus ursinus Cham. andSchlechtend
Brassica oleracea L.
(Botrytis Group)
B. oleracea L. (Capitata
Group)

Fruit yield

3.48

3.20

0.335

4.64

Bernstein et al., 1956; Brown
et al., 1953
Bernstein et al., 1956
Francois, 1995
Francois, 1987
Bernstein and Ayers, 1951;
Hoffman and Rawlins, 1970;
Magistad et al., 1943; Nieman
and Bernstein, 1959; Osawa,
1965
Minhas et al., 1990
Bernstein et al., 1974; Hoffman
and Rawlins, 1971; Magistad
et al., 1943
Ehlig, 1964

Fruit yield

3.48

3.20

0.335

4.64

Ehlig, 1964

Shoot FW

7.88

3.02

0.140

8.99

Head FW

6.62

2.66

0.148

7.60

CarrotDaucus carota L.

Storage root

4.26

2.48

0.213

5.17

CeleryApium graveolens L. var
dulce (Mill.) Pers.

Petiole FW

9.49

2.45

0.094

10.39

Bernstein and Ayers, 1949a(p. 39); Bernstein et al., 1974
Bernstein and Ayers, 1949a(p. 39); Bernstein et al., 1974;
Osawa, 1965
Bernstein and Ayers, 1953a;
Bernstein et al., 1974;
Lagerwerff and Holland, 1960;
Magistad et al., 1943; Osawa,
1965
Francois and West, 1982

Sudangrass
Trefoil, Big
Trefoil, narrowleaf
birdsfootVetch, common
Wheatgrass, crested,
CommonWheatgrass, crested
Fairway
Wheatgrass,intermediate#
Wheatgrass, slender#
Wheatgrass, tall
Wildrye, beardless
AlmondBoysenberry
Broccoli
CabbageContinued next page.

Thinopyrum intermedium(Host) Bark. and Dewey
Elymus trachycaulus (Link)
Bark. and Dewey
Agropyron elongatum (Hort)
Beauvois
Elymus triticoides Buckl.

Francois and Bernstein, 1964
(p. 52–53)
Bower et al., 1970

228

CROP SCIENCE, VOL. 45, JANUARY–FEBRUARY 2005

Table 6. Continued.
Nonlinear tolerance parameter

Crop

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

Common name

Botanical name‡Corn, sweet

Zea mays L.

CowpeaCucumberTolerance§
based on

5.54

2.75

0.183

6.56

Vigna unguiculata (L.) Walp. Seed yield
Cucumis sativus L.
Fruit yield

8.71
6.02

4.91
3.29

0.183
0.198

10.30
7.21

Date palm

Phoenix dactylifera L.

Fruit yield

17.42

2.60

0.055

18.38

Eggplant

Fruit yield

7.99

2.32

0.105

8.83

GarlicGrape

Solanum melongena L. var
esculentum Nees.
Allium sativum L.
Vitus vinifera L.

Bulb yield
Shoot growth

7.06
6.38

4.65
2.54

0.218
0.146

8.59
7.31

Grapefruit
Guava

Citrus ⫻ paradisi Macfad.
Psidium guajava L.

4.59
9.43

2.57
4.09

0.206
0.149

5.54
10.84

GuayuleParthenium argentatum A.
Gray
Citrus limon (L.) Burm. F.
Lactuca sativa L.

Fruit yield
Shoot and root
growthShoot DW
rubber yield
Fruit yield
Top FW

12.60
12.03
5.09
4.83

9.27
7.23
2.70
2.60

0.177
0.164
0.195
0.198

14.83
14.01
6.08
5.79

Lemon
Lettuce
Muskmelon

Ear FW

C50 (ECe)
Salinity
dS/m p Shape s Steepness tolerance index

References
Bernstein and Ayers, 1949b(p. 41–42)
West and Francois, 1982
Osawa, 1965; Ploegman and
Bierhuizen, 1970
Furr and Armstrong, 1962;(p. 11–13); Furr and Ream,
1968; Furr et al., 1966
Heuer et al., 1986
Francois, 1994b
Groot Obbink and Alexander,
1973; Nauriyal and Gupta,
1967; Taha et al., 1972
Bielorai et al., 1978
Patil et al., 1984
Maas et al., 1988
Cerdá et al., 1990
Ayers et al., 1951; Bernstein
et al., 1974; Osawa, 1965
Mangal et al., 1988; Shannon
and Francois, 1978
Bernstein and Ayers, 1953b;
Bernstein et al., 1974;
Hoffman and Rawlins, 1971;
Osawa, 1965
Mangal et al., 1989
Bielorai et al., 1988; Bingham etal., 1974; Dasberg et al., 1991;
Harding et al., 1958
Cerdá et al., 1982
Bernstein et al., 1956; Brown etal., 1953; Hayward et al., 1946
Bernstein, 1954 (p. 36–37);
Osawa, 1965; USSL§§§
Hoffman et al., 1989
Bernstein et al., 1951
Kumamoto et al., 1992; Grieve
and Suarez, 1997
Hoffman and Rawlins, 1971;
Osawa, 1965
Langdale et al., 1971; Osawa,
1965
Francois, 1985

Fruit yield

6.62

2.33

0.128

7.46

Onion (bulb)

Cucumis melo L.
(Reticulatus Group)
Allium cepa L.

Bulb yield

4.02

2.66

0.244

5.00

Onion seedOrangeAllium cepa L.
Citrus sinensis (L.) Osbeck

Seed yield
Fruit yield

6.91
4.80

2.32
2.61

0.122
0.200

7.75
5.76

Pea
Peach

Pisum sativum L.
Prunus persica (L.) Batsch

7.77
3.78

3.50
3.35

0.161
0.320

9.02
4.99

PepperCapsicum annuum L.

Seed FW
Shoot growth,fruit yield
Fruit yield

4.76

2.76

0.213

5.77

Plum; prune
PotatoPurslane

Prunus domestica L.
Solanum tuberosum L.
Portulaca oleracea L.

Fruit yield
Tuber yield
Shoot FW

3.91
5.54
11.12

6.34
2.75
5.08

0.472
0.183
0.146

5.76
6.56
12.74

RadishRaphanus sativus L.

Storage root

4.73

2.55

0.198

5.67

SpinachSpinacia oleracea L.

Top FW

8.22

2.59

0.116

9.18

Squash, scallop

Cucrbita pepo L. var
Fruit yield
melopepo L. Alef.
C. peop L. var melopepo (L.) Fruit yield
Alef.
Fragaria ⫻ ananassa Dutch. Fruit yield

5.60

4.31

0.244

7.46

9.29

4.42

0.160

10.78

2.23

3.07

0.503

3.36

Fleshy root
Fruit yield

5.72
7.21

2.61
2.96

0.168
0.151

6.68
8.29

Fruit yield

6.86

2.59

0.139

7.81

Francois, 1985; Graifenberg
et al., 1996
Ehlig and Bernstein, 1958;
Osawa, 1965
Greig and Smith, 1962; USSL§§§
Bierhuizen and Ploegman, 1967;
Hayward and Long, 1943,
Lyon, 1941; Shalhevet andYaron, 1973
Caro et al., 1991

Storage root

6.13

2.32

0.137

6.97

Francois, 1984

Top FW

13.50

2.58

0.065

15.45

Francois, 1984

Squash, zucchini
Strawberry
Sweet potato
TomatoTomato, cherry
TurnipTurnip (greens)

Ipomoea batatas (L.) Lam.
Lycopersicon lycopersicum(L.) Karst. ex Farw. [syn.
Lycopersicon esculentum
Mill.]
L. lycopersicum var.
Cerasiforme (Dunal) Alef.
Brassica rapa L. (Rapifera
Group)
Brassica rapa L.

FW ⫽ fresh weight; DW ⫽ dry weight.
† Table based on Table 3–1, Maas and Grattan, 1999, and controlled tests of crop-yield response to increasing root-zone salinity gradually applied to theplants as early seedlings. These data are applicable when rootstocks of woody crops are used that do not accumulate Naⴙ or Cl⫺ rapidly or when theseions do not predominate in the soil.
‡ Botanical and common names follow the convention of Hortus Third (Liberty Hyde Bailey Hortorium Staff, 1976) where possible.
§ In gypsiferous soils, plants will tolerate about 5–10% greater salinity than indicated.
¶ Less tolerant during seedling stage, ECe at this stage should not exceed 4 or 5 dS/m.
# These data are based on tests following dryland agricultural practices, where seeds are planted directly in saline seedbeds.
†† These values for p were obtained from Fn(p ) ⫽ bCt of Fig. 5.
‡‡ Grain and forage yields of DeKalb XL-75 grown on an organic muck soil decreased about 26% per decisiemen/meter above a threshold of 1.9 dS/m
(Hoffman et al., 1983).
§§ Because paddy rice is grown under flooded conditions, values refer to the electrical conductivity of the soil water while the plants are submerged. Less
tolerant during seedling stage.
¶¶ Sensitive during germination and emergence, ECe should not exceed 3 dS/m.
## Data from one cultivar, Probred.
††† Average of several varieties. Suwannee and Coastal are about 20% more tolerant, and common and Greenfield are about 20% less tolerant than the
average.
‡‡‡ Average for Boer, Wilman, Sand, and Weeping cultivars (Lehmann seems about 50% more tolerant).
§§§ Unpublished U.S. Salinity Laboratory data.

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

STEPPUHN ET AL.: INDICES FOR SALINITY TOLERANCES OF AGRICULTURAL CROPS⫾10% error limit of the actual was again used. The
selected-line-segments method recorded b-to-s conversions within this limit for five out or the eight test experiments (Table 5). The fitted Fn(p) ⫻ exp(bCt) with s ⫽lnp/(C50) and the fitted s ⫻ (b) methods respectively
registered seven and eight out of eight test experiments
within an error of ⫾10% or less. Within an error limitof ⫾5% of the actual, the three methods [selected-linesegments, s fitted ⫻ (b), Fn(p) fitted ⫻ (bCt), with s ⫽lnp/(C50)], respectively. recorded four, seven, and six
test experiments out of the eight.
One of the most recent published lists of agricultural
crop tolerances to root-zone salinity is arrayed according
to four crop groups: “fiber-grain-special,” “grasses-forage,”“vegetable-fruit,” “woody” (Tables 3-1 and 3-2, Maas
and Grattan, 1999). The threshold and slope values
listed for each crop in these tables were converted toC50, s, p, and the ST-Index using the regression fits of
C50 ⫻ (Cmid) and s ⫻ (b), and the relationships of p ⫽exp(sC50) and ST-Index ⫽ C50 ⫹ sC50, respectively(Table 6). The parameter values in Table 6 also include
those obtained in crop-yield response tests conductedunder dryland agricultural conditions, where seeds wereplaced directly into salinized seedbeds.

229

several nonlinear models, including Eq. [3], more accurately describe the actual response of plant crops tosalinity than the threshold-slope linear model (Eq. [5]).
Extension personnel and plant growth modelers need
to work with a more precise nonlinear response analog.
However, all but one of the crop lists available to them
are based on a linear response. Table 6 offers an alternative list based on the nonlinear discount function. Also,
as information becomes available on the response ofcrops to irrigation with saline water containing various
specific ions, response values under these conditions
can be incorporated into Table 6. In cases where only
estimates of C50 are available, van Genuchten and Gupta(1993) suggest an assumption that p ≈ 3.00 (s ≈ 1.099/C50).
Or, one could let ST-Index ≈ C50, resulting in an index
with a lower value.
ACKNOWLEDGMENTS
With thanks, the authors acknowledge the valuable contributions of Mr. K.G. Wall, Dr. Y.W. Jame, Dr. S. Yang-Steppuhn, and staff members of the George E. Brown, Jr. Salinity
Laboratory and the Semiarid Prairie Agricultural ResearchCentre to this research.

REFERENCESDISCUSSION
Many factors influence the yield of agricultural crops
besides the response to increasing root-zone salinity(Maas and Grattan, 1999; Steppuhn et al., 2005). In viewof the myriad of influences which affect the relationshipof product yield with salinity, a single-value index ofcrop tolerance to root-zone salinity would seem appropriate and useful for comparing agricultural crops. The
ST-Index, based on the nonlinear parameters of C50 ands (Eq. [4]), fills this need. This index identifies a salinity
value equal to the 50% reduction in crop yield from thatof the nonsaline yield plus a measure of the tendency tomaintain some product yield as the crop is subjected toincreasing salinity levels approaching C50, that is, STIndex ⫽ C50 ⫹ sC50.
The concept of an index for rating the salinity tolerance of agricultural crops was followed earlier (Ayers
et al., 1951; U.S. Salinity Laboratory Staff, 1954; Brown
and Hayward, 1956). The practice then was to simply use
C50 as the index. Now, with the benefit of the modified
discount response function (Eq. [2]), we propose adding
the term, sC50, to the earlier index. Although simple,
the ST-Index shows sensitivity. For example, testing withcanola demonstrates a salinity tolerance approaching thatof barley, Hordeum vulgare L. (Francois, 1994a; Steppuhn et al., 2002). Under dryland agricultural practices,
the ST-Indices for Brassica napus L. canola and barley
grain crops equal 8.00 and 8.29, respectively (Table 6).
Under irrigation-agricultural practices, the respective
ST-Index-values equal 17.27 and 18.87. These indicesalso show the pronounced effects of seeding into saline
seedbeds, as required in dryland agriculture, compared
to seeding where fresh water is applied to establish thecrop under irrigated cultivation.
Maas (1990) and Maas and Grattan (1999) stated that

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