Crop salt tolerance
under controlled field conditions in
The Netherlands, based on trials
conducted by Salt Farm Texel

Crop salt tolerance under controlled ﬁeld conditions in The Netherlands,
based on trials conducted at Salt Farm Texel.

December 2016

Dr. Arjen de Vos
Salt Farm Texel, Den Burg, The Netherlands.
Royal Institute for Sea Research (NIOZ), Den Burg, The Netherlands.
Dr. Bas Bruning
Salt Farm Texel, Den Burg, The Netherlands
Prof. dr. ir. Gerrit van Straten
Professor emeritus, Systems and Control,
Wageningen University, The Netherlands.
Ir. Roland Oosterbaan
International Institute for Land Reclamation and Improvement,
Wageningen (Alterra-ILRI), The Netherlands. Retired.
Prof. dr. Jelte Rozema
Systems Ecology, VU University, Amsterdam, The Netherlands
Prof. dr. ir. Peter van Bodegom
Department of Conservation Biology CML,
Leiden University, The Netherlands.

2

Table of contents
Summary....................................................................................................................................................................................................................................................................................... 4
Preface ............................................................................................................................................................................................................................................................................................. 5
Background ............................................................................................................................................................................................................................................................................... 5
Classiﬁcation of salinity ................................................................................................................................................................................................................................. 6
Salinity and crop growth .............................................................................................................................................................................................................................. 7

Open-air laboratory of Salt Farm Texel ........................................................................................................................................................................................... 9
Experimental setup............................................................................................................................................................................................................................................. 9
Root zone salinity .............................................................................................................................................................................................................................................. 11
Modelling root zone salinity................................................................................................................................................................................................................ 12

Crop data .................................................................................................................................................................................................................................................................................. 12
Potato ................................................................................................................................................................................................................................................................................ 12
Cabbage ......................................................................................................................................................................................................................................................................... 13
Onion ................................................................................................................................................................................................................................................................................. 13
Carrot ................................................................................................................................................................................................................................................................................. 13
Lettuce.............................................................................................................................................................................................................................................................................. 14
Barley ................................................................................................................................................................................................................................................................................. 14

Statistical analysis ........................................................................................................................................................................................................................................................ 15
Results .......................................................................................................................................................................................................................................................................................... 16
Root zone salinity .............................................................................................................................................................................................................................................. 16
Crop growth .............................................................................................................................................................................................................................................................. 18

Discussion ............................................................................................................................................................................................................................................................................... 25
Conclusions ........................................................................................................................................................................................................................................................................... 29
Recommendations ..................................................................................................................................................................................................................................................... 30
References .............................................................................................................................................................................................................................................................................. 32
Appendix 1............................................................................................................................................................................................................................................................................. 34
Appendix 2............................................................................................................................................................................................................................................................................. 37

3

Summary
Between 2012 and 2015 ﬁeld trials were performed at the open-air laboratory of Salt Farm Texel in The
Netherlands. Through its unique design, reliable ﬁeld trials can be conducted under highly controlled
conditions, irrigating crops with seven diﬀerent salt concentrations, each replicated eight times. In this
way it is possible to evaluate the crop salt tolerance of many diﬀerent species and varieties. In this report
the results of ﬁve potato varieties, seven carrot varieties, four onion varieties, three lettuce varieties, two
cabbage varieties and one barley variety are presented. Root zone salinity levels were suﬃciently constant
within the season and seasonal average salinities were close to targeted values across all years. These
ﬁndings are based on actual measurements of the root zone salinity, and a simple mass-balance model
to provide insight in the inter-sample behavior. Plant yields show considerable variation at similar salinity
levels but due to the high number of repetitions the salt tolerance can be charted in a reliable way. Results
show that for some of the tested crop species, varieties exist with a larger salt tolerance than was assumed
up until now. This implies that, at least on sandy soils using drip irrigation, these varieties can be cultivated
under moderate saline conditions without loss in yield. It also opens perspectives for cultivation on
moderately saline soils anywhere in the world.

4

Preface

Since 2006 diﬀerent ﬁeld trials regarding crop salt tolerance have been performed on the island of Texel,
the Netherlands. Details of this work can be found in De Vos (2011), among others. Although the trials
focused on obtaining practical results for breeders and farmers, the set up of the various trials was
scientiﬁcally solid, demonstrated by the publication of Bruning et al., in 2015. In 2016 the Dutch Ministry
of Economic Aﬀairs and Wageningen Environmental Research (Alterra) commissioned Salt Farm Texel to
publish the data on crop salt tolerance that had been collected between 2012 and 2015 at the open-air
laboratory of Salt Farm Texel (The Netherlands), which has resulted in this report. At the research facility
of Salt Farm Texel, controlled ﬁeld trials can be conducted at seven diﬀerent salinity levels. In the period
2012-2015 trials have been conducted with potato, carrot, barley, lettuce, cabbage and onion. This report
describes the results of these trials. To be able to compare these results with the standard crop salt
tolerance data currently in use in the international literature, salt tolerance is expressed in terms of a yield
reduction curve at various salinity levels.
Besides the authors of this report, this report has been reviewed by several other key persons: Dr. I.M.
van der Meer (Wageningen Plant Research), Prof. Dr. K.R. Timmermans (NIOZ-University of Groningen),
Dr. Ir. L.C.P.M. Stuyt (Wageningen Environmental Research) and Prof. Dr. Ir P. Vellinga (Wadden Academy).
During a meeting on Texel all results were discussed comprehensively and feedback on the report was
given and processed.

Background

Salinization is one of the major threats to agriculture worldwide, and is a major escalating problem.
Globally, 1 billion hectares of land is negatively aﬀected by salinity and of all irrigated arable land about
20% or 63 million ha is salt aﬀected (Ghassemi et al., 1995, Qadir et al., 2014). This number increases with
2000 ha every day and crop damage in the irrigated areas is estimated at US$ 27.3 billion every year
(Qadir et al., 2014). In the Netherlands it is expected that the salinization of arable land will increase up to
125.000 hectares (De Kempenaer et al., 2007). In general, crops produce lower yields at higher salinity levels
and in the worst case farmers have to abandon their ﬁelds and clear new land that adds to the pressure on
natural ecosystems and the associated biodiversity. Moreover, salinity is expected to increase even further
under current climate change predictions. Thus, with a growing human world population and climatic
changes on a global scale, salinity is an issue that will only grow in importance and urgently requires a
solution (Qadir et al., 2014). In 2008, Rozema and Flowers published an article in Science that emphasizes
the potential of cultivating salt tolerant crops since it can help address the threats of irreversible global
salinization of freshwater and soils.

5

Classiﬁcation of salinity
Table 1 shows a general classiﬁcation of saline water, based on the electrical conductivity (EC, in dS/m) and
the chloride concentration (speciﬁcally used as salinity standard in water management in The Netherlands).
In this table EC is converted to equivalent chloride concentration with the established correlation presented
in this report (ﬁgure 10). According to this table, water containing less than 150 mg Cl-/l or an EC lower than
0.7 dS/m is considered as non-saline or fresh water. The maximum salt concentration for water that is also
suitable for irrigation is, according to table 1, considered to be 2 dS/m or 480 mg Cl-/l. In diﬀerent areas in
The Netherlands the general guideline of 200-250 mg Cl-/l is used as the desirable upper limit for the
maximum salt concentration of surface water.

Table 1. Classiﬁcation of saline water (Rhoades et al., 1992) based on EC (in dS/m) and equivalent chloride concentration
of water (calculated from ﬁgure 10).

Water class

EC (in dS/m)

in mg Cl-/l

Non-saline

< 0.7

< 150

Slightly saline

0.7 - 2

150 - 480

Moderately saline

2 - 10

480 - 2940

Highly saline

10 - 25

2940 - 8250

Very highly saline

25 - 45

8250 - 15970

Brine

> 45

> 15970

Drinking and irrigation water
Irrigation water

Seawater=55 dS/m or 19,000 mg Cl-/l

The salts in irrigation water can aﬀect plant growth in several ways. When sprinkler irrigation is used, the
irrigation water can cause leaf burn or extra salt uptake by the leaves. In the case of drip irrigation, which is
used at the research facility of Salt Farm Texel, no direct contact between the irrigation water and the leaves
takes place and only root uptake of salts is considered. So results described in this report are based on drip
irrigation, but when sprinkler irrigation is used the outcome of the crop salt tolerance may diﬀer from the
results described in this report. Another disclaimer is that the soil type at the facility of Salt Farm Texel is sand.
It is well known that salts can inﬂuence the soil structure of clay soils to a great extend. The replacement
of especially calcium, bound to the clay fraction, by sodium may cause poor soil structure and waterlogging.
These (indirect) eﬀects of salinity on crop growth in salt aﬀected clay soils are not present in the trials
described in this report but should be considered under actual ﬁeld conditions. A well-known soil salinity
classiﬁcation that is often used as a general guideline in relation to crop growth is presented in table 2.
According to table 2 yields of many crops are likely to be restricted under these “moderate saline” conditions.

6

Table 2. Soil salinity classes and crop growth (Abrol et al., 1988). Soil salinity is based on the electrical conductivity of the extract of a soil
saturated paste (ECe, in dS/m).

Soil salinity class

EC (in dS/m)

Eﬀect on crop plants

Non-saline

0-2

Salinity eﬀects negligible

Slightly saline

2-4

Yields of sensitive crops may be restricted

Moderately saline

4-8

Yields of many crops are restricted

Strongly saline

8 - 16

Only tolerant crops yield satisfactorily

Very strongly saline

> 16

Only a few very tolerant crops yield satisfactorily

Salinity and crop growth
Elevated salinity levels in the soil pore water aﬀect plants in several ways. First, it lowers the osmotic
potential of the soil pore water. This makes water uptake more diﬃcult for plants since the plant has to
lower the osmotic potential of the roots to levels lower than the osmotic potential of the soil moisture
(water will move to the component with the lowest osmotic potential). Secondly, the NaCl molecules that
enter the plant with the water can cause physiological damage. Na+ ions especially can quickly reach toxic
levels within the plant. Finally, because of the high concentrations of Na+ in the soil pore water, increased
competition with potassium (K+) ions –essential to plant growth- for use of the same ion channels occurs
and plants may have diﬃculties absorbing suﬃcient K+.
Not withstanding the generally adverse eﬀects of salinity, plants diﬀer in their sensitivity to salinity. There is
large variation in salt tolerance between species, from the extremely sensitive (some cultivars of chickpea
(Cicer arietinum) die at 25 mM NaCl; Flowers et al., 2010) to many species that survive and reproduce at
seawater salinity (~500 mM NaCl) or even higher. Diﬀerent varieties or cultivars within one species can also
diﬀer in their tolerance to salinity (Khrais, 1996).
Most commonly, a species’ salt tolerance is described by the renowned Maas-Hoﬀman model (Maas
and Hoﬀman 1977, see Figure 1). According to Maas and Hoﬀman the salt tolerance of a crop can be best
described by plotting its relative yield as a continuous function of soil salinity. For most crops, this response
function follows a sigmoidal relationship. Maas and Hoﬀman proposed that this response curve could be
approximated by two line segments: one, a tolerance plateau with a zero slope, and the other,
a concentration-dependent line whose slope indicates the yield reduction per unit increase in salinity.
The point at which the two lines intersect designates the “threshold”, i.e. the maximum soil salinity that does
not reduce yield below that obtained under non-saline conditions. This two-piece linear response function
provides a reasonably good ﬁt for commercially acceptable yields plotted against the electrical conductivity
of the soil saturated paste (ECe). The threshold and slope concept has its greatest value in providing general
salt tolerance guidelines for crop management decisions. Based on the threshold and slope, a division in
sensitive, moderately sensitive, moderately tolerant and tolerant crops can be made (see ﬁgure 1).

7

The crop salt tolerance data of Maas and Hoﬀman (1977) and the FAO (Tanji and Kielen, 2002) have been
used as a reference to predict the eﬀect of increasing salinity on crop growth in The Netherlands previously
(Roest et al., 2003; Van Dam et al., 2007).
Farmers need to know the soil salinity levels at which yields start to decline and how much yield will be
reduced at levels above the threshold. However, Maas and Hoﬀman already indicated that more precise
plant response functions would be advantageous for crop simulation modelling. Van Genuchten and
Hoﬀman (1984) have described several non-linear models that more accurately describe the sigmoidal
growth response of plants to salinity.

Relative crop yield (%)

100 80 60 -

Yields unacceptable
for most crops

40 20 Sensitive

0|
0

|

|

|

|

Moderately
Sensitive

Moderately
tolerant

Tolerant

| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

5

10

15

20

25

30

35

ECe(dS/m)
Figure 1. Division for classifying crop tolerance to salinity (Maas and Hoﬀman, 1977).

As described, plant species and cultivars within species diﬀer in their tolerance to salinity. Most data that is
presented by Maas and Hoﬀman (1977) and the FAO (Tanji & Kielen, 2002 using the same model) is based
on a single variety of a speciﬁc crop and most trials were performed in a diﬀerent climate zone than the
climate zone of The Netherlands (temperate). Moreover, in many trails, salt and drought and/or heat stress
appear to coincide, while in The Netherlands drought and heat stress is much less of an issue. This makes
the trails performed at Salt Farm Texel highly important for the Dutch situation. At Salt Farm Texel, various
varieties of multiple crops have been tested under controlled ﬁeld conditions in the Dutch climate zone. For

8

most crops no heat stress occurs during the growth cycle in The Netherlands and the frequent irrigation
ensured that no drought stress occurred. On clay soils diﬀerent interaction between the salts and the clay
fraction can occur that can also inﬂuence crop performance. At the test facility on Texel the soil is mainly
sand which makes it possible to assess the crop performance based only on the salts that are present in
the pore water, in contrast to clay soils where more complex interaction can inﬂuence crop performance.
Because of this, plants are only faced with the challenges of water uptake and salt uptake and not with
any potential secondary eﬀects that may take place in salt aﬀected clay soils. So the trials described in this
report really focus on crop salt tolerance alone rather than a potential combination of stresses.

The goal of this report is to present an overview of the recent trials performed by Salt Farm Texel,
focussing on the crop salt tolerance, and to assess whether these results imply that salt tolerant
crops may be cultivated under elevated salinity levels. Is it possible to use irrigation water with a
salt concentration of 2 dS/m or higher? Is it possible to grow conventional crops on salt aﬀected
land in the range of 4-8 dS/m without major restrictions? The results presented in this report are
especially applicable for sandy soils using drip irrigation.

Open-air laboratory of Salt Farm Texel
Experimental setup

Salt Farm Texel has set up a large research facility under ﬁeld conditions in The Netherlands and this high
tech location resembles an open-air laboratory. In 2011, the top 30 cm of the soil was removed and mixed
to obtain a uniform topsoil. Drainage pipes were placed every ﬁve meters at 60 cm depth to obtain optimal
drainage. The soil consists of sand (about 93% sand, 3% loam, 2% clay and 2% organic matter), with soil
particle density around 2.5 Mgm-3, bulk soil density at saturation is about 1.5Mgm-3, and ﬁeld capacity
around pF 2. Despite homogenization, there are known diﬀerences between various corners of the test site
regarding porosity (between 0.39 and 0.42) and water holding capacity (between 0.24 and 0.27 kg(water)
kg-1(dry soil). A high irrigation intensity was maintained during all years (13.7 mm m-2 day-1 in 2012, 13.2
mm in 2013 up to 17 June and after this date this was reduced to 12.0 for the rest of the season, and 10.7
mm in 2014 and 2015) to keep the soil moisture content permanently close to the ﬁeld capacity. Leaching
of the irrigated water at the drain level takes place every day, so that the soil is ﬂushed through, and soil
salinity is kept as constant as possible. At the research station, fresh water and seawater can be mixed into
any desired salt concentration by means of a proportional-integral-derivate (PID) controller with frequencyregulated pumps. In total seven diﬀerent salt concentrations have been used, targeted at 1.7, 4, 8, 12, 16,
20 and 35 dS/m, at a minimum of 0.5 dS/m accuracy. Irrigation takes place by means of drip irrigation.
Not all crops were grown at the 35 dS/m treatment especially when it was known that crops could not

9

survive at this high level. Each salinity treatment is replicated eight times on plots of eight by 20 meters that
are randomly distributed over the ﬁeld. In this way, 56 (eight replicas x seven salt concentrations) individual
plots of 160 m2 each are irrigated (Figure 2; total research location is one hectare). Within each plot,
diﬀerent crops can be tested simultaneously. After planting, crops were allowed to germinate under fresh
water conditions before the salt treatment started. Similar to most greenhouse experiments reported in the
literature, the salt treatment started when crops reached the second or third true leaf stage. The dates of
the start of the salt treatment were 29th of May, 27th of May, 30th of May and 8th of June for 2012, 2013, 2014
and 2015, respectively.
Climatic conditions are monitored by a Davis Vantage Pro2 line weather station that records precipitation
and evapotranspiration, among others.

Figure 2. An aerial impression of the open-air laboratory of Salt Farm Texel where the trials were performed.
In total 56 plots of 160 m2 are irrigated with seven diﬀerent salt concentrations, with each salt concentration
consisting of eight randomly divided repetitions.

10

Root zone salinity
Root zone salinity is carefully monitored using three diﬀerent methods. First, soil salinity is measured
according to the international standard of extracting water from a saturated paste soil sample (ECe, in
dS/m). This has been done for a limited number of samples each year (n=81 for all four years combined).
Secondly, from all the plots and at least two times per year, soil samples (each composed of ten subsamples
collected within a 2 m radius of the pore water samplers, sample depth is 30 cm) are measured according
to the commonly used 1:2 method (EC1:2). For this, one part soil (dried and sieved (2 mm)) is diluted with
two parts demineralized water (method 1:2 volume:volume). The conductivity of this solution is determined
and the correlation between ECe and EC1:2 is determined (see Figure 3). To save labour time, the number of
saturated paste measurements was reduced in favour of more EC1:2 measurements after the observation
was made that the correlation between these two methods was highly signiﬁcant (ECe=EC1:2*5,3, with
r2=0,95: see Figure 3) and highly constant throughout the years. Thirdly, pore water samplers (rhizon
sampler) consisting of a ceramic element put under vacuum, which extracts soil moisture, were placed in
half of the replicate plots in 2012 and 2013 and in all plots in 2014 and 2015 at three diﬀerent depths (0-10,
20-30 cm and 50-60 cm, with 10 cm length of the suction). The 50-60 cm depth was only used to determine
the salinity of the leaching fraction. For crop growth analysis the average of the 0-10 and 20-30 cm depth
was used. With no visible roots present below 30 cm in the ﬁeld, the average of the top two suction cups
represents the average root zone for at least 90% of the roots. Correlations between the conductivity of
these extractions (ECpore) and ECe were also highly signiﬁcant (ECe=ECpore*0,69, with r2=0,84, n= 711: see
Figure 3). For this calibration all three depths of the suction cups were used and plotted against the soil
salinity of the individual plots with ECe calculated from EC1:2 using the calibration formula from Figure 1
when no direct data of ECe were available. Because of the strong correlations between the three methods
for measuring salinity, all salinity measurements have been expressed in terms of ECe. For measuring the
electrical conductivity in the water phase a WTW Cond 3310 was used.

Figure 3. Correlation between ECe and EC1:2 (left, with n=81) and between ECe and ECpore (right, with n=711). Data from all four years of
testing have been used. The formula of the best ﬁt line and the R2 are shown in the top left corners of the graphs. See main text “root zone
salinity” for detailed methods.

11

Modelling root zone salinity
Soil and pore water samples were collected a number of times during the season. In order to have an idea
about the inter-sample behaviour, the soil salinity at Salt Farm Texel was simulated (modeled) using water and
salt balances as described by J.W. van Hoorn and J.G. van Alphen (1994). This model can predict the root zone
salinity, to provide insight in the inter-sample behavior. More details about this model can be found in Appendix 1.

Crop data

Various varieties have been tested for most crops, and some varieties have been tested for multiple years
(details in Table 3). Some crops have been planted in blocks (potato, cabbage, lettuce), whereas others have
been planted in rows (onion, carrots). Details about the diﬀerent crops are presented below. A contiguous
area covered by one variety within a 8 x 20 m2 ﬁeld is called a plot. Within such a plot either a row or a block
on a certain amount of plants were used to analyse the yield.

Potato
Eight potato plants per plot were planted (four plants per ridge, two adjacent ridges, total of eight plants
per plot which equals 1.8 m2 (ridges spaced 75 cm and tubers spaced at 30 cm) and diﬀerent varieties were
separated by planting two extra plants with a diﬀerent tuber colour to make harvesting easier (Figure 4).
Before planting, the initial weight of the eight tubers was determined. Potatoes were planted on 17th of
April, 1st of May, 28th of April (Achilles) or 29th of April (Miss Mignonne) and 7th of May in 2012, 2013, 2014
and 2015 respectively. Harvest took place on 19th of July, 29th of August, 12th of August and 18th of August in
the subsequent years, resulting in a 93, 120, 105-106 and 103 day growth period after planting and 51, 94,
74 and 71 days of salt treatment during that growth period, respectively. Number of replicate plots in 2012
were eight, in 2013 4 for treatments 1.7 and 8 dS/m and eight for the other treatments, and four replicas
per treatment in the years 2014 and 2015. Fresh and dry weights of the tubers were determined. Herbicide
use and spraying against late blight was conform standard agricultural practises.

Figure 4. Impression of the potato trials. Tubers are hand planted and each variety consists of eight plants
(two ridges with four plants) in each plot.

12

Cabbage
Blocks of ten seedlings of cabbage, equalling 2.5 m2 per plot, were planted on June 10, 2014. Seedlings with two
true leaves were planted directly in the saline soils (irrigation started 11 days earlier) (Figure 5). The cabbage was
harvested on August 26. Only the fresh weight of the above ground biomass (excluding the older leaves that
were touching the ground) was taken into account when determining the yield, combining four plants per plot.
For broccoli, the same approach as for cabbage was used, and only the edible part that is sold on the market was
harvested to determine the yield. Each salt treatment was replicated eight times.

Onion
On April 29, 2014 the diﬀerent onion varieties were sown. Each variety was sown in a single row, with 25 cm
spacing between the variety rows. Each row was 8 m long in every plot, so that the area per variety was 2.0 m2.
Sowing was performed with a mechanical sowing machine, set at one million seeds per hectare sowing density.
The salt treatment started when seedlings were about 6 cm in height. Harvest took place on September 16,
2014, after a 109 days period of salt treatment. For the harvest a length of 1.0 m per row was selected. Each salt
treatment was replicated eight times.

Figure 5. Impression of onion, carrot and cabbage (under green net) 13 days after the start of the salt treatment (image left) and 67 days
after the start of the salt treatment (image right).

Carrot
On April 29, 2014 the diﬀerent carrot varieties were sown. Varieties were sown in a row, with 25 cm spacing
between rows. Sowing was performed with a mechanical sowing machine, set at 1.2 million seeds per
hectare sowing density. Each variety consisted of a row of 8 m in every plot, which equals 2.0 m2. The salt
treatment started when seedlings were about 8 cm in height. Harvest took place on September 30, 2014,
after a 113 days period of salt treatment. Fifteen plants within a 1.5 m row were collected and fresh weight
of the whole plants (above and below ground biomass) was determined. Each salt treatment was replicated
eight times. In 2015, the sowing date was May 19. Seedlings reached the third true leaf stage at the start of
the salt treatment. Harvest took place on October 7, after a 121 days period of salt treatment. In 2015, salt
treatments were only replicated four times and a 1 m row was harvested.

13

Table 3. Overview of the crop growth data presented in this preliminary report

14

Crop

Variety

Year tested

Harvested part

Repetitions

Potato

Miss Mignonne
Achilles
Foc
Met
927

2012, 2013, 2014, 2015
2012, 2013, 2014, 2015
2015
2015
2015

Tubers, fresh weight

Carrot

Cas
Nat
Ben
Ner
101
102
Pri

2014
2014
2014
2014
2015
2015
2015

Whole plant, fresh weight

8
8
8
8
4
4
4

Onion

Alo
Red
San
Hyb

2014
2014
2014
2014

Aboveground biomass,
fresh weight

8
8
8
8

Lettuce

Batavia, heading, red
Butterhead, var. Suzan
Butterhead, Lob

2015
2015
2015

Aboveground biomass,
fresh weight

4
4
4

Cabbage

White cabbage, early variety
Broccoli

2014
2014

Edible part of above ground
biomass, fresh weight

8
8

Barley

Que
Que

2014
2014

Seeds, fresh weight
Stems, fresh weight

8
8

4-8
4-8
4
4
4

Lettuce
Seedlings that had developed 3-4 true leaves were planted on June 2, 2015, six days before the salt
treatment started. Of the Batavia variety, eight plants per plot were planted, whereas Butterhead varieties
had three plants per plot. Planting distance was 40 cm between plants and 40 cm between rows. Each
salt treatment was replicated four times. At harvest, the fresh weight of the above ground biomass was
determined.

Barley
In 2014 seeds were sown on April 25, harvest took place on August 14. The whole plot (40 m2) was
harvested with a small combine. Only the salt treatments 1.7, 4, 8 and 12 dS/m were harvested, each with 8
repetitions. In 2015 seeds were sown on May 9 and harvest took place on September 10. In 2015 plots of 2
m2 were harvested. By this time many seeds unfortunately had already fallen on the ground so yield could
only be expressed as shoot biomass (excluding all seeds). The maximum salt concentration that was used
was 20 dS/m (EC irrigation water) and 8 repetitions per salt treatment were used.

Statistical analysis

Our statistical model for determining crop salt tolerance, based on Maas and Hoﬀman (1977) has three
parameters: ECe_thr (the threshold ECe in dS/m), S (the slope in units yield per dS/m) and the unaﬀected
yield, i.e. the yield at no salinity stress, Y0. The parameters are estimated by minimizing the sum of squared
diﬀerences between the model values and the observations. It has been demonstrated (for proofs, a
scientiﬁc publication containing all details is in preparation) that under the conditions of Salt Farm Texel
this method yields unbiased estimates, even in the presence of uncertainty in the ECe. The parameter
uncertainties, and prediction error bounds, were obtained with a method based on the Jacobian matrix
(i.e. the matrix of the derivatives of the residuals to the parameters) in conjunction with the Cramér-Rao
bound (Draper and Smith, 1966; Lewis, 1986; Ljung, 1987; Montgomery et al, 2001). In the threshold model,
there is the risk of local minima in the search for the minimum sum of squares. The ﬁrst starting value
for the maximum yield prior to the search was chosen as the mean over the lowest seven measured EC
values, together with an estimate for slope and threshold, and these starting values were permutated a
number of times to obtain the lowest sum of squares. To allow for comparison between crops and between
years, the average yield value (in tons per hectare) at low salinity levels was taken as 100% and yields at
increasing salinity levels as a percentage of this value. Besides the Maas and Hoﬀman approach also the
Van Genuchten-Gupta (1993) approach, that assumes a more gradual S-shaped decline function of crop
yield with increasing salinity, was used to analyse the crops salt tolerance data. This model, too, has three
parameters. Details of the models and the estimation method are given in Appendix 2.

15

Results

Root zone salinity
Salinity levels in the individual plots have been regularly monitored throughout the four growing seasons
(year 2012, 2013, 2014 and 2015). First of all, the salinity levels of the salt treatments (EC irrigation water)
were plotted against the observed salinity levels of the pore water (EC pore) of which the results are
presented in Figure 6. The seasonal average salinity levels found in the pore water correspond well to the
salinity levels of the irrigation water that was used. During the ﬁrst 2 years some ﬁelds with the lower salt
concentrations (treatment 1.7 and 4 dS/m) were above the intended salt concentration. This was mostly due
to the ﬁne-tuning of the irrigation system in the beginning and the non-availability of fresh water. Overall,
thanks to the rather strong irrigation, Salt Farm Texel succeeded very well in maintaining a target pore
water EC in the root zone of the crops. The pore water EC is slightly higher than the irrigation EC, which
is to be expected because during the season there is more evapotranspiration than rainfall, resulting in a
concentration eﬀect.

Figure 6. The salinity level of the irrigation water (x-axis) plotted against the observed seasonal average salinity levels of the pore water
(y-axis). Each individual blue dot represents one plot in one year and all 4 years (2012-2015) are used to determine the correlation between
EC pore water and EC irrigation water for the image on the left side. On the right side only the years 2014 and 2015 were used. The * means
that one outlier was excluded.

In ﬁgure 7 the EC measurements of the pore water in time during the four seasons are presented (excluding
the ﬁrst two weeks of irrigation), which show the temporal dynamics in salt concentration. Especially 2014
and 2015 show very little variation during the season.

16

Figure 7. Salinity measurements (based on EC pore water) during 2012 (top left), 2013 (top right), 2014 (bottom left) and 2015
(bottom right) with n=4 for 2012 and 2013 and n=8 for 2014 and 2015.

Also the modelling of the root zone salinity shows little temporal variation. In appendix 1, two graphs are
presented which show both the measured (indicated as yellow stars, average of the top two depths of
measurements (0-10 and 20-30 cm depth)) and the simulated (based on Appendix 1, equation 9) salinity
levels of the pore water. This simulated root zone salinity also takes evapotranspiration and rainfall into
account. Results clearly show that the model predicts the root zone salinity very well and that, although that
there is some variation in time, root zone salinity remains close to the intended salt concentration. In farmer’s
practises, irrigation often takes place when ﬁeld capacity is down to about 80%, which corresponds to a

17

25% increase in salt concentration. Based on this “natural variation” under ﬁeld conditions it was also the
intention of Salt Farm Texel to maintain the root zone salinity within 25% of the intended concentration.
Figure 6 shows that this variation of the average root zone salinity is indeed smaller than 25% of the salinity
level of the irrigation water. Only some deviation occurred during occasional rainfall events up to 10 mm
and recovery after intensive rainfall is quick.
Although the analysis of the pore water salinity of the root zone shows strong correlation with the salinity
level of the irrigation water, pore water salinity is not often used in greenhouse experiments or ﬁeld surveys.
The international standard to express root zone salinity is based on the salinity level of the extract of a
saturated paste of a soil sample (ECe). Also, although the mean salinity levels at each point in time showed
relatively small variations within each group of ﬁelds with the same treatment (see error bars in ﬁgure 7).
Consequently, also the time averaged values over the growing season of each ﬁeld within a treatment
group show little variation. Despite this, it was decided to use the seasonal mean salinity and crop yield
measurements of the individual ﬁelds of each treatment as separate data points, rather than clustering the
points per treatment, to ensure the optimal comparison between crop growth and salinity level. The results
from ﬁgures 6, 7 and appendix 1 justify the calculation of one average salinity level per plot per year in order
to evaluate crop tolerances to salinity. In all subsequent analyses, soil pore water salinities are expressed in
equivalent saturated paste electrical conductivities (ECe), based on the correlations in Figure 2.

Summary results soil salinity at Salt Farm Texel:
Root zone salinity is sampled frequently and accurately and results, including the modelled
inter-sample root zone salinity levels, show that little variation occurs during the season.
In addition, pore water salinity is closely related to irrigation water salinity. Based on these
results it is concluded that, at the open-air lab of Salt Farm Texel, it is possible to conduct
reliable experiments regarding crop salt tolerance under actual field conditions.

Crop growth
In ﬁgures 8 and 9 examples are given of the output of the growth analysis, based on the statistical analysis
as explained in the “experimental set up”. Figure 8 is based on the growth of white cabbage (early variety).
In this ﬁgure the solid dots represent the measured relative yields per plot (relative to the estimated
absolute yield), the solid blue line represent the best ﬁt, the dashed green line is the 95% conﬁdence
interval for the prediction with simultaneous repetition of the experiment, and the ellipse represents the
approximate 95% conﬁdence contour of the threshold value and the unaﬀected yield, assuming that the
estimated slope is correct. Figure 9 is based on the growth of potato, variety Miss Mignonne, combining
four years of data. In ﬁgure 9 the conﬁdence contours are not shown, as these are involved in multiple year
estimation, but the resulting conﬁdence intervals on the threshold parameter are listed in table 5.

18

Figure 8. Relative yield of white cabbage (early variety). The threshold 4.6 dS/m and the salinity level at
which yield is reduced by 50% is 11.7 dS/m.

Figure 9. Relative tuber yield of Miss Mignonne for all years combined. The threshold of Miss Mignonne
of all years combined is 4.1 dS/m and the salinity level at which tuber yield is reduced by 50% is 11.6 dS/m.

19

In Table 4 an overview is given of the 90% and 50% yield corresponding EC levels when the Van GenuchtenGupta (1993) growth curve is used to interpret the results. This Van Genuchten-Gupta model does not
deﬁne a threshold value and only salinity levels of the 90% and 50% yield are given in table 4. In table 5 all
results are summarized, based on a similar analysis as presented in ﬁgure 8 and 9, including threshold and
slope values as reported by Maas and Hofmann (1977) and the FAO (Tanji and Kielen, 2002) which is often
used to indicate the crop salt tolerance. Some details of these analyses are highlighted in the “discussion”
section of this report and more details will be shared in the next publication (in prep.). Additionally, also the
upper and lower values of the 95% conﬁdence interval of the threshold value and the ECe at which 50% yield
reduction occurs are given. In table 6 an overview is given of the threshold values based on the EC levels
of the irrigation water. In this table a comparison is made with the recent report on crop salt tolerance by
Stuyt et al. (2016). In both table 4 and 6 the salinity values based on ECe (in dS/m) are also expressed as mg
chloride per litre, using the correlation presented in ﬁgure 10 (based on samples taken form the irrigation
water during 5 subsequent years).

Results in Table 5 and 6 show that the salt tolerance of some varieties of the tested crops indicate
that salt aﬀected soils up to a salinity level of 4-6 dS/m (ECe) and irrigation water up to a salinity
level of 5 or even 7 dS/m can be used for crop production without loss in yield, implying that
moderate saline conditions may be suitable for crop production under conditions similar to the
experimental set up described in this report.

Figure 10. The correlation between the EC of the irrigation water (in dS/m) and the chloride concentration (in mg/l)
of the same irrigation water (n=10, including two samples from 2016).

20

Table 4. Soil salinity levels, based on ECe (electrical conductivity of the extract of a saturated soil sample, the “saturated paste method”)
and mg Cl-/l (based on values of ECe), that result in a 90% yield (10% reduction) or a 50% yield. Values are based on the yield data per plot
using the Van Genuchten-Gupta model to ﬁt the data. Values of chloride were obtained by using the correlation of ﬁgure 10.

Soil Salinity ECe, in dS/m
Crop

Variety

Potato

Soil salinity, in mg Cl-/l

90% yield

50% yield

90% yield

50% yield

Miss Mignonne
Achilles
Foc
Met
927

4.6
3.9
4.0
3.7
5.5

11.0
11.4
11.1
11.3
12.7

1230
1021
1051
963
1505

3277
3411
3310
3378
3852

Carrot

Cas
Ner
Nat
Ben
101
102
Pri

5.7
4.0
n.d.
n.d.
3.9
6.3
4.7

13.2
11.0
n.d.
n.d.
7.9
9.5
7.6

1565
1051
n.d.
n.d.
1021
1751
1260

4022
3277
n.d.
n.d.
2258
2779
2162

Onion

Alo
Red
San
Hyb

4.7
6.8
5.0
4.8

8.6
9.9
9.6
7.2

1260
1908
1350
1290

2485
2911
2812
2035

Lettuce

Batavia, heading, red
Butterhead, Suzan
Butterhead, Lob

n.d.
3.6
1.5

n.d.
8.5
6.6

n.d.
933
349

n.d.
2452
1845

Cabbage

White cabbage, early
Broccoli

6.0
6.7

11.5
13.3

1658
1877

3445
4057

Barley

Que seed 2014
Que shoot 2015

5.2
3.0

12.3
6.8

1411
760

3715
1908

n.d. = non-determinate species, no perceivable salt tolerance

21

Table 5. Overview of the salt tolerance of the crops and varieties tested by Salt Farm Texel in the period 2012-2015. Salt tolerance is
expressed as the threshold value (maximum salt concentration (as ECe, in dS/m) without yield loss), the 95% conﬁdence interval of the
threshold value (upper and lower values of this interval are listed between brackets in the threshold column), the slope (expressed as the
percentage of yield decrease per unit of salinity (1 dS/m) beyond the threshold), and the salinity level at which 50% yield reduction occurs
(“50% yield”, as ECe, in dS/m). Also included are the values given by the FAO (Tanji and Kielen, 2002).

Values
Crop

Variety

Potato

Miss Mignonne
Achilles
Foc
Met
927
FAO reference

Carrot

Threshold

Slope (% per dS/m)

50% yield

4.1 (2.9 - 5.2)
2.9 (1.5 - 4.4)
2.1 (0.3 - 3.8)
1.9 (0.2 - 3.7)
3.4 (1.8 - 5.1)
1.7

6.6
5.6
5.2
5.0
5.2
12

11.6
11.9
11.7
12.0
13.1
5.9

Cas
Ner
Nat
Ben
101
102
Pri
FAO reference

4.5 (1.8 - 7.3)
3.6 (0.5 - 6.6)
n.d.
n.d.
3.0 (0.3 - 5.8)
5.0 (1.9 - 8.1)
2.1 (0 - 6.0)
1.0

5.6
6.1
n.d.
n.d.
9.0
11.2
9.0
14

13.4
11.8
n.d.
n.d.
8.6
9.4
7.6
4.6

Onion

Alo
Red
San
Hyb
FAO reference

2.4 (0 - 7.6)
5.9 (2.7 - 9.2)
3.2 (0 - 7.2)
3.4 (0 - 8.0)
1.2

7.7
11.7
7.4
11.6
16

8.9
10.2
10.0
7.7
4.3

Lettuce

Batavia, heading, red
Butterhead, Suzan
Butterhead, Lob
FAO reference

n.d.
2.3 (0 - 8.7)
1.8 (0 - 10.7)
1.3

n.d.
6.8
5.8
13

n.d.
9.6
10.3
5.1

Cabbage

White cabbage, early
FAO reference
Broccoli
FAO reference

4.6 (2.9 - 6.2)
1.8
5.6 (1.2 - 10.1)
2.8

7
9.7
6.3
9.2

11.7
7.0
13.6
8.2

Barley

Que seed 2014
Que shoot 2015
FAO reference

3.3 (0 - 7.3)
1.7 (0 - 3.6)
8.0

5.3
8.4
5.0

12.8
7.6
18.0

n.d. = non-determinate species, no perceivable salt tolerance

22

Table 6. The threshold values of the various crops, expressed as the salt concentration of the irrigation water (expressed as dS/m and
mg Cl-/l). To calculate values listed in this table, the initial thresholds and the 95% conﬁdence interval of the threshold value from Table 5
were used and correlations between ECe and EC pore water from Figure 3 and correlation between EC pore water and EC irrigation water
from Figure 6 were used. To calculate the chloride concentration, the correlation found in ﬁgure 10 was used. The “Stuyt et al.” reference
is taken from the report of Stuyt et al., 2016.

Treshold for irrigation water,
in dS/m

Treshold for irrigation water,
in mg Cl-/l

Treshold

Range

Treshold

Range *

Crop

Variety

Potato

Miss Mignonne
Achilles
Foc
Met
927
“Stuyt et al.”

5.2
3.6
2.5
2.2
4.2
3.7

3.6 - 6.7
1.7 - 5.6
0.1 - 4.8
0.0 - 4.6
2.1 - 6.5

1411
933
619
537
1110
838

933 - 1877
401 - 1534
17 - 1290
0 - 1230
509 - 1814
500 - 1200

Carrot

Cas
Ner
Nat
Ben
101
102
Pri
“Stuyt et al.”

5.7
4.5
n.d.
n.d.
3.7
6.4
2.5
3.8

2.1 - 9.5
0.4 - 8.5
n.d.
n.d.
0.1 - 7.5
2.2 - 10.5
0 - 7.7

1565
1200
n.d.
n.d.
963
1782
619
868

509 - 2779
79 - 2452
n.d.
n.d.
17 - 2130
537 - 3110
0 - 2194
800 - 950

Onion

Alo
Red
San
Hyb
“Stuyt et al.”

2.9
7.6
4.0
4.2
3.8

0 - 9.9
3.3 - 12.0
0 - 9.3
0 - 10.4

732
2162
1051
1110
867

0 - 2911
846 - 3614
0 - 2713
0 - 3077
875 - 1050

Lettuce

Batavia, heading, red
Butterhead, Suzan
Butterhead, Lob
“Stuyt et al.”

n.d.
2.8
2.1
3.7

0 - 11.3
0 - 14.0

n.d.
704
509
848

0 - 3378
0 - 4298
425 - 1300

Cabbage

White cabbage, early
“Stuyt et al.”
Broccoli
“Stuyt et al.”

5.9
4.5
7.2
2.9

3.6 - 8.0

1627
1093
2035
600

933 - 2291
1025 - 1150
297 - 4022
-

Que seed 2014
Que shoot 2015
“Stuyt et al.”

4.1
2.0
8.9

1080
482
2626

0 - 2779
0 - 1200
1150 - 4100

Barley

1.3 - 13.2
0 - 9.5
0 - 4.5

n.d. = non-determinate species, no perceivable salt tolerance
* Reported range for conﬁdence intervals are 95%, for “Stuyt et al. (2016)” this is 40%
1

Data for Brassica oleracea convar. Capitata var. sabauda

2

Wheat and barley combined

23

24

Discussion

The diﬀerent measurements of root zone salinity have shown that pore water salinity is highly determined
by the salinity of irrigation water for the majority of the growing season. These pore water samples
represent the root zone salinity that plant roots are actually experiencing. In this regard, pore water
samples are very suitable to analyse crop growth at various salinity levels. However, in the scientiﬁc
literature, pore water samples are not often used, probably partly because they can only be extracted in
sandy or loamy saturated soils. So, to compare the results of Salt Farm Texel to those of other experiments
done elsewhere it is necessary to use values of soil saturated paste extracts (ECe). This conversion based
on the calibration in ﬁgure 3, will result in some additional variation. The least squares method assumes
that the independent variables, here the EC irrigation water, are known with large precision. However, as
the observed ECe is a time average of samples, the ECe is, itself, an uncertain value. In principle, this socalled errors-in variables case (also known as type 2 in regression literature) may lead to biased estimates.
Therefore it has been tested what the eﬀect of random noise in ECe on the estimates is. In order to do this,
100 sets of synthetic data were generated with the theshold model (see Appendix 2 for more details), using
known realistic parameters, and by injecting random noise in the observed ECe, as well as the observed
yields (ﬁgure 11). It was established that the centre points of the 100 estimates are close to the true
estimates. This means that the method, in practice, yields unbiased estimates as shown in table 7.

Figure 11. Results of the 100 sets of synthetic data that were generated with the breakpoint model, using known realistic parameters,
and by injecting random noise in the observed ECe as well as the yield.

25

Table 7. Results of the centre point of the 100 estimates of Y0, EC threshold and slope (S) and the true estimates. The estimates of the
“true” and “estimated” values are comparable, meaning that the method, in practice, yields unbiased estimates. Sample sd stands for
standard deviation.

Sample mean

Sample sd

True

Estimated

True

Estimated

Mean estimated correlation
Y0

ECthr

S

Y0

60

59.9930

1.7504

1.7079

1

-0.6950

0

ECthr

4

4.1291

0.8669

0.8105

-0.6950

1

-0.6198

S

-3

-3.0365

0.1856

0.1881

0

-0.6198

1

This does not mean that for an individual data set the estimated parameters are equal to the true ones,
yet on average the parameters are unbiased.
Overall the irrigation strategy results in a uniform salt concentration within replicates of one salt treatment
and little variation between years. In addition, crop growth is linked with salinity levels of individual plots
to obtain the most accurate analysis of crop response to increasing salinity, so some variation within the 8
repetitions of one salt treatment is of less importance. Maas and Hoﬀman (1977) also concluded that several
studies support the hypothesis that plants respond to the mean salinity of the root zone. Hence, using the
average seasonal salinity level per plot is an accurate way of linking crop response to increasing salinity under
otherwise constant conditions. Therefore, at the open-air laboratory of Salt Farm Texel, it is indeed possible
to conduct reliable ﬁeld trials with respect to root zone salinity. Although under open ﬁeld conditions more
variation can be expected than in greenhouse experiments, the results from the open-air lab are more
suitable for comparing the eﬀect of salinity on crop growth under ﬁeld conditions at other locations. The
data was collected under speciﬁc conditions at the research location of Salt Farm Texel (sandy soil, optimal
drainage, and drip irrigation, with high intensity of irrigation, leaching fraction close to 90%). Locations with
a diﬀerent soil type, diﬀerent drainage intensity and diﬀerent irrigation techniques obviously will result in
diﬀerent eﬀective EC values in the root zone, and hence will lead to other responses of crop yield to irrigation
salinity. However, without prove of the contrary, the yield reduction functions themselves can be assumed valid.
Despite stable root zone salinity levels, crop growth shows much more variability. Yields at comparable
salinity levels can vary greatly, the reasons for this variation are not yet known (although it coincides with
variation found in controlled ﬁeld experiments elsewhere). It could indicate the existence of other as of
yet unknown other limitations during part of the season, and this may aﬀect the reduction curves found.
However, despite the high variability, it was possible to obtain reliable results for most crops about the
threshold and slope values thanks to the high number of replicates and the chosen statistical analysis. This
approach resulted in the 95% conﬁdence intervals for threshold values. The fact that the lower bound of
the 95% conﬁdence interval in many cases was higher than zero makes it likely that threshold values for
crop salt tolerance do indeed exist. In some cases (particularly when there were few observations below
the tentative threshold value) our model did not produce reliable threshold estimates. In these cases, plant
response to salinity was best described as a linear regression with a negative slope (for instance lettuce,
data not shown). So for crops and varieties that produced a 95% conﬁdence interval where the lower bound

26

was zero it is indeed possible that no threshold exists. In most cases the lower boundary of zero is caused
by a relative large variation in crop yield data (as can be seen by the large spread in EC values for the lower
and upper boundary) and possibly additional testing with a larger number of repetitions and/or with a
larger area of harvest can reduce this large diﬀerences between the upper and lower boundary of the 95%
conﬁdence interval and produce more reliable values of crop salt tolerance. The threshold-slope model is a
model based on agronomical considerations, which allow for an easy, two–parameter (threshold and slope)
description of crop salt tolerance but this model may not accurately reﬂect plant physiology. But when
the results of the “threshold and slope model” are compared with the results of the Van Genuchten-Gupta
model (which follows a more realistic plant physiological approach) then similarities can be seen. The Van
Genuchten-Gupta model does not assume a threshold value, but values of the 90% yield can be directly
compared to those of the Maas-Hoﬀman model. It appears that ECe values with 90% and 50% yield are
indeed comparable, as shown in Table 8. Especially the EC values for the 50% yield are very similar, whereas
the 90% yield values show more variation. On average both model show comparable results.

Table 8. Soil salinity levels, based on ECe, that result in a 90% yield (10% reduction) or a 50% yield.
Values are based on the Van Genuchten-Gupta model and the Maas-Hoﬀman model.

Soil salinity level (in dS/m)
90% yield
Crop

Variety

Potato

Soil salinity level (in dS /m)
50% yield

Van Genuchten

Maas-Hoﬀman

Van Genuchten

Maas-Hoﬀman

Miss Mignonne
Achilles
Foc
Met
927

4.6
3.9
4.0
3.7
5.5

5.6
4.6
4.0
3.9
5.3

11.0
11.4
11.1
11.3
12.7

11.6
11.9
11.7
12.0
13.1

Carrot

Cas
Ner
Nat
Ben
101
102
Pri

5.7
4.0
n.d.
n.d.
3.9
6.3
4.7

6.3
5.2
n.d.
n.d.
4.1
5.9
3.2

13.2
11.0
n.d.
n.d.
7.9
9.5
7.6

13.4
11.8
n.d.
n.d.
8.6
9.4
7.6

Onion

Alo
Red
San
Hyb

4.7
6.8
5.0
4.8

3.7
6.8
4.5
4.3

8.6
9.9
9.6
7.2

8.9
10.2
10.0
7.7

Lettuce

Batavia, heading, red
Butterhead, Suzan
Butterhead, Lob

n.d.
3.6
1.5

n.d.
3.8
3.5

n.d.
8.5
6.6

n.d.
9.6
10.3

Cabbage

White cabbage, early
Broccoli

6.0
6.7

6.0
7.2

11.5
13.3

11.7
13.6

Barley

Que seed 2014
Que shoot 2015

5.2
3.0

5.2
2.9

12.3
6.8

12.8
7.6

27

A comparison with the FAO data (Tanji and Kielen, 2002) can be somewhat tricky. It is stated in this FAO
report that “the data serves only as a guideline to relative tolerances among crops. Absolute tolerances
vary, depending upon climate, soil conditions and agricultural practices”. For instance, in gypsiferous soils,
plants will tolerate an ECe about 2 dS/m higher than indicated. Soil analysis from the open-air lab of Salt
Farm Texel (data provided separately, data in Dutch) shows that the soil is rich in available magnesium and
sulphate but poor in calcium. In this regard the soil is non-gypsiferous and higher levels of salt tolerance as
indicated by the FAO paper are not expected. Also Maas and Hoﬀman (1977), the publication that is mostly
used as a reference to crop salt tolerance, concluded that “salt tolerance depends upon many plant, soil,
water and environmental variables and, hopefully, a discussion of these interacting variables will caution
both those using these data and those conducting salt tolerance investigations.” It is diﬃcult to compare all
literature that is used to set the guideline of the FAO and it will be even more diﬃcult to compare diﬀerent
variables such as stage of growth, crop varieties, soil fertility, soil water and aeration, and environmental
factors. Maas and Hoﬀman (1977) highlight that the most common method of measuring soil salinity is to
determine the electrical conductivity of saturation extracts (ECe) from the active root zone. Soil samples
should be taken just after irrigation (Maas and Hoﬀman, 1977) and at the research facility of Salt Farm
Texel the last irrigation event was never longer than 6 hours ago. In 2016 samples were taken exactly 5
hours after irrigation for each salinity treatment and this resulted in a very strong correlation between EC
pore water and ECe (ECe=0,71*EC pore water, R2=0.96). This indicates that the relative large variation seen
in ﬁgure 3 is partly caused by the diﬀerence in time between irrigation and soil sampling, yet the average
in ﬁgure 3 represents the true value very well. Using ECe was recommended because the saturation
percentage is easily determined in the laboratory and is related to the ﬁeld-moisture range of soils varying
widely in texture. For many soils, the soluble salt concentration of the soil solution at ﬁeld capacity is about
twice that at saturation. Of course this is a rough average since the correlation depends strongly on the
timing of irrigation and sampling. The literature review of Maas and Hoﬀman makes no comments about
reporting crop salt tolerance based on salinity levels of irrigation water and subsequently calculated back
to soil salinity levels. So it appears that reported ECe values in the Maas and Hoﬀman report are based on
actual soil samples. This implies that values of ECe and corresponding crop yields from the open-air lab of
Salt Farm Texel and the crop salt tolerance data of Maas and Hoﬀman can be compared.
When the salt tolerance of the crops and varieties presented in this report are compared with the FAO
reference (table 5) it appears that for some varieties of the crops the salt tolerance is at least a factor two
higher and in some cases even a factor three. This suggests that crops grown at Dutch ﬁeld conditions may
be more salt tolerant than previously suggested.
More recently, Stuyt et al. (2016) reviewed all sources of information about crop salt tolerance that have
become available in the Netherlands between 1950 and 2015. In table 6 the results of Stuyt et al. (2016)
have also been included to make an additional comparison of crop salt tolerance. To make this comparison,
the data presented in this report has been calculated from ECe to EC pore water to EC irrigation water,
using the correlation of ﬁgure 3 and 6. To calculate values of chloride, the correlation of ﬁgure 10 was used.

28

In table 6 also a comparison between the conﬁdence intervals has been made but it should be noted that
the conﬁdence intervals presented in this report are based on 95% whereas the results in the Stuyt et al.
report are based on 40%, making a direct comparison between the two intervals more diﬃcult. However,
the results clearly show that the diﬀerences in table 6 are considerable smaller than the diﬀerences in
table 5 where the FAO reference is used to compare levels of salt tolerance. The results found by Stuyt et
al. (2016) and the results presented in this report are comparable for various crops and varieties, although
for potato, carrot and onion some varieties exist that appear to show greater levels of salt tolerance. For
lettuce and barley it appears that the salt tolerance levels are lower then has been reported by Stuyt et al.,
although this comparison is diﬃcult to make since the data set for these two crops are limited and results
show considerable variation. Comparison of chloride concentrations may be tricky since the correlation
that was based on the analyses of the actual irrigation water in this report is diﬀerent from the standard
correlation formula that is often used in The Netherlands (mg Cl-/l irrigation water = 221*ECirrigationwater1,1244 vs.
151*ECirrigationwater1,31 (Van Dam et al., 2007), respectively). When values of chloride are based on calculations
rather than actual analysis the comparison may result in positively or negatively biased diﬀerences. In fact,
we feel that basing salinity policies on chloride is less preferable.
Considering the crops and varieties tested, i.e. potato, carrot, onion, lettuce, cabbage and barley, this
report clearly shows that there is more potential for conventional crop production under “moderate
saline” conditions than is generally assumed. Irrigation water with a salinity level between 2 and 10 dS/m
(considered as “moderately saline” in table 1) has successfully been used in the described ﬁeld experiments
in this report, with yields close to 100% (compared to the control treatment) when an EC of 4 or even 8 dS/m
is used. When soil salinity levels are considered, the results also indicate that moderate saline conditions
(ECe of 4-8 dS/m) can be suitable for crop production with yields close to 100%.

Conclusions
• Root zone salinity can be controlled at the open-air laboratory of Salt Farm Texel and the salinity
levels show minimal variation within the season and between years.
• Pore water salinity of the root zone is highly similar to the salinity level of the irrigation water.
• The model for the prediction of root zone salinity fits the observed salinity levels with a high accuracy.
• Based on the measurements of root zone salinity it is possible to conduct reliable experiments
under actual field conditions at the open-air lab of Salt Farm Texel.
• Plant growth shows considerable variation at similar salinity levels but due to the high number of
repetitions the salt tolerance indicators (based on threshold and slope values) can be charted in a
reliable way
• Moderate saline conditions may be suitable for crop production under certain conditions,
with yields close to 100%.

29

Recommendations

The natural variation in yield of ﬁeld crops between plots results in variation in yield, which is one of
the reasons for the rather large uncertainty range (95% conﬁdence interval) of the threshold value.
In addition, there is natural variation between years, because of varying meteorological conditions
(a.o. temperature, rainfall). Therefore it is recommended to perform experiments for at least another
year, in order to obtain more precise estimates of the threshold and the conﬁdence interval.
To facilitate the screening of many cultivars, and given the limitations in space and (ﬁnancial) resources,
it was for some crops necessary to limit the number of planted and harvested plants per cultivar, and
sometimes the number of repetitions as well. It is recommended that future experiments with the most
promising varieties use the present basic experimental set up of Salt Farm Texel, i.e. a minimum surface
area of 2 m2 with 8 repetitions (16 m2 in total), to obtain the most reliable results.
To obtain more insight in the plant physiological response to salinity and the salt tolerance of diﬀerent
growth stages it is advisable to perform several measurements and harvests during the growth season
rather than to focus on a single end-harvest to determine crop salt tolerance.
In case crops are sown from seed, salinity eﬀects on seed germination may diﬀer from that on crop
development. For practical reasons irrigation with saline water may start once seed germination has been
completed and similar sized young crop plants start to develop. Such, salt tolerance of the crop may be
distinguished from salt tolerance of seed germination. Additional experiments regarding the salt
tolerance of the germination phase may be needed.
In all cases it should be attempted to distinguish direct soil moisture salinity eﬀects on crops from
indirect salinity eﬀects on soil properties such as soil structure, soil drainage and aeration. On the sandy
soil of Salt Farm Texel these indirect eﬀects are limited, they may be more pronounced in heavier soils.
In practice, soil salinity varies to some extent over time in response to irrigation, rainfall and capillary
rise of saline groundwater. To determine the eﬀect of a short period of increased salinity and the ability
of a speciﬁc crop to recover after such a short period, additional controlled ﬁeld experiments should be
performed that focus on such conditions.
Sprinkler irrigation can result in direct leaf damage or leaf uptake of salts, whereas the experiments in
this report are based on drip irrigation. To distinguish between diﬀerent types of irrigation and its eﬀect
on crop growth under saline conditions, additional experiments should be performed.
The current crops and varieties that were tested do not always match the crop varieties that are used
most frequently by Dutch farmers. This may cause reluctance to use the results to support agricultural
policy or more ﬂexible water management. Therefore, it would be desirable to test the salt tolerance of
the most abundant crop varieties used by farmers under the controlled ﬁeld conditions as provided by
Salt Farm Texel.

30

Results from this report suggest that the choice of the crop variety may contribute to maintaining good
yields under saline conditions. Yet, yields and apparent salinity tolerance vary between varieties and
between years, which may be due to fertilizer application, soil amendments, (the timing of) irrigation,
drainage and many other factors. More research on these topics is advisable.
The prediction of the eﬀect of salts on crop growth starts with the actual measurement of the level of soil
salinity. It is advisable that all actors in The Netherlands use the same standard for soil salinity. Although
the soil saturated paste extract (measured as EC (in dS/m) is time consuming to perform, it represents
the most robust standard.
Focussing on the increasing danger of salinization in The Netherlands and the major salinity issues
that the world already faces, the creation of a “Centre of Expertise” for salt tolerant crops and saline
agriculture in The Netherlands can have a worldwide impact on food security. Not only can Dutch farmers
beneﬁt from such a Centre of Expertise, but also many Dutch agro-companies such as breeders can be
part of this centre to open-up a worldwide market. With crop damage due to salinity already estimated
at US$ 27.3 billion every year at present (Qadir et al., 2014), the solution to this challenging problem may
be proﬁtable as well.

31

References
Abrol IP, Yadav JSP, Massoud FI. 1988. Salt aﬀected soils and their management.
FAO soil bulltin 39. FAO, Rome.
Bruning B, van Logtestijn R, Broekman R, de Vos AC, Parra Gonzales A, Rozema J. 2015. Growth and
nitrogen ﬁxation of legumes at increasing salinity under ﬁeld conditions: implications for the use of green
manures in saline environments. AoB Plants Special Issue: Physiology and ecology of halophytes-plants
living in salt-rich environments, 1-8.
De Vos, AC. 2011. Sustainable exploitation of saline resources. Ecology, ecophysiology and cultivation of
potential halophyte crops. Thesis VU University Amsterdam, the Netherlands.
De Kempenaer JG, Brandenburg WA, van Hoof LJW. 2007. Het zout en de pap, een verkenning bij
marktexperts naar langetermijnmogelijkheden voor zilte landbouw. Utrecht.
Draper NR, Smith H 1966. Applied regression analysis. Wiley, New York.
Flowers TJ, Gaur PM, Gowda CLL, Krishnamurthy L, Samineni S, Siddique KHM, Turner NC, Vadez V,
Varshney RJ, Colmer TD 2010. Salt sensitivity in chickpea. Plant Cell Environment 33, 490-509.
Ghassemi F, Jakeman AJ, Nix HA. 1995. Salinization of land and water resources: Human Causes, extent,
management and case studies. UNSW Press, Sydney, Australia, and CAB International, Wallingford, UK
Khrais T 1996. Evaluation of salt tolerance in potato (Solanum spp.). Thesis. Department of Plant Science,
Mcgill University, Macdonald Campus.
Lewis FL 1986. Optimal estimation with an introduction to stochastic control theory. Wiley, New York
Ljung L 1987. System identiﬁcation – theory for the user. Prentice-HallEnglewood Cliﬀs, New Jersey.
Maas EV, Hoﬀman GJ, 1977. Crop salt tolerance-current assessment. Journal of the Irrigation and Drainage
Division, American Society of Civil Engineers 103: 115-134.
Montgomery DC, Peck EA, Vining GG. 2001. Introduction to linear regression analysis. Wiley, New York
Qadir M, Quillerou E, Nangia V, Murtaza G, Singh M, Thomas RJ, Crechsel P, Noble AD 2014. Economics
of salt-induced land degradation and restoration. National Resources Forum, United Nations. DOI:
10.1111/1477-8947.12054
Rhoades JD, Kandiah A, Mashali AM. 1992. The use of saline water for crop production. FAO irrigation and
drainage paper 48. FAO, Rome.

32

Roest CWJ, Van Bakel PJT, Smit AAMFR. 2003. Actualisering van de zouttolerantie van land- en
tuinbouwgewassen ten behoove van de berekening van de zoutschade in Nederland met het RIZAinstrumentarium. Alterra. Actualisering zouttolerantie gewassen.
Rozema J, Flowers T. 2008. Crops for a salinized world. Science 322: 1478-1480.
Stuyt LCPM, Blom-Zandstra M, Kselik RAL, 2016. Inventarisatie en analyse zouttolerantie van
landbouwgewassen op basis van bestaande gegevens. Wageningen, Wageningen Environmental research,
Rapport 2739. http://edepot.wur.nl/391931
Tanji KK, Kielen NC. 2002. Agricultural drainage water management in arid and semi-arid areas. FAO
irrigation and drainage paper 61. FAO, Rome.
Van Dam AM, Clevering OA, Voogt W, Aendekerk ThGL, van der Maas MP. 2007. Zouttolerantie van
landbouwgewassen. Deelrapport Leven met Zout Water. PPO nr 32 340194 00.
Van Genuchten MTh. and S.K. Gupta SK. 1993. A Reassessment of the crop tolerance response function.
Journal of the Indian Society of Soil Science, Vol. 41, No. 4, pp 730-737.
Van Hoorn JW and Van Alphen JG 1994. Salinity Control. In: H.P. Ritzema (Ed.), Drainage Principles and
Applications, p. 533-600. ILRI publication 16, International Institute for Land Reclamation and Improvement,
Wageningen, The Netherlands. 1994. ISBN 90 70754 3 39.

33

Appendix 1.

Modelling root zone salinity to provide insight in the inter-sample behavior.
The water balance of the root zone reads:

I + R + Cr = E + P + Δw

(1)

Here, I is the irrigation, R the rainfall, Cr the capillary rise of soil water from the underground, E the
evapotranspiration, P the percolation of soil water to the underground, and Δw the change in soil water
content. The units may be mm/day.
Under the Salt Farm conditions (daily amounts of irrigation water exceeding the evapotranspiration and the
presence of an intensive subsurface drainage system keeping the water table continuously below the root
zone and close to the drain depth), capillary rise of soil moisture from the underground into the root zone
does not occur, the soil water content is permanently close to the saturation point and the change in soil
water content is negligibly small. Hence Eq. 1 simpliﬁes to:

I+R=E+P

(2)

By multiplying the water ﬂow with the salt concentration of the ﬂowing water one obtains the salt balance.
As the salt concentrations of rainfall and evaporation are negligibly small, the salt balance can be written as:

I.Ci = P.Cp + Δs

(3)

Here, Ci is the salt concentration of the irrigation water, Cp the salt concentration of the percolation water,
and Δs the change in salt storage in the soil. The units of salt concentration may be expressed in terms of
electrical conductivity (EC) in dS/m or mS/cm.

The salt concentration of the percolation water Cp is in a complicated way related to the salinity history in
the soil proﬁle. As a simpliﬁcation, it is postulated that the salt concentration is simply proportional with the
salt concentration of the pore water:

Cp = F.Cs

34

(4)

Here, Cs is the average salt concentration of the pore water, and F is an empirical (possibly time varying)
parameter, called the leaching eﬃciency of the soil pore system. By deﬁnition the leaching eﬃciency
represents the ratio of the salinity of the percolation water to the average salinity of the soil pore water.
The leaching eﬃciency accounts for irregular patterns of downward ﬂow through the irregular soil pore
system, which may also vary with depth, and for the irregular distribution over time of salts dissolved in the
water inside the pore system.
At each time step, the change of the salt concentration of the soil water in the root zone equals:

Cf – Co = Δs/W

(5)

where Cf is the ﬁnal average salt concentration Cs of the soil water at the end of the time step, Co is the
initial salt concentration Cs of the soil moisture at the beginning of the time step, and W is the amount of
water contained in the soil pores of the root zone, equaling:

W = D.T

(6)

where D is the depth of the root zone and T the total pore space of the soil in the root zone. In the actual
calculation presented later the soil is divided in three layers, and the equations have been adapted
accordingly.

During a small time step the average salt concentration of Cs can be taken as:

Cs = 0.5*(Co+Cf)

(7)

Combining Eq. 3, 4, 5, 7 and 7, one gets:

Cf = Co + I.Ci/D.T – 0.5*F.P.(Co+Cf)/D.T

(8)

or explicitly in Cf:

Cf = [Co + I.Ci/D.T – 0.5*F.P.Co/D.T] / [1 + 0.5*F.P/D.T]

(9)

35

This equation 9 is used for the soil salinity simulation model, see the 2 examples below. The solid line in
these 2 examples represent the simulated pore water salinity. It is clear that little variation occurs during
the season, even after intensive rainfall in the year 2015. Although there is a reduction in the root zone
salinity level during this intensive rainfall period, this reduction remains within the 25% variation (regarding
the salinity level of the irrigation water) that is normally present under ﬁeld conditions (where ﬁeld capacity
often drops by 20% causing a 25% increase in soil salinity levels).

Example of simulation
Overview of the measured (hash: #)
and modelled (solid line) root zone
salinity (based on pore water salinity)
during the season of 2014 (top ﬁgure,
data shown of one plot irrigated with
12 dS/m) and during the season of 2015
(bottom ﬁgure, data shown of one plot
irrigated with 8 dS/m).

36

Appendix 2.

The threshold model (Maas-Hoﬀman)
The threshold or breakpoint model is given by
(1)

where
Y

is the yield at a particular ECe (dependent variable)

ECe

is the associated ECe (independent variable)

Y0

is the yield without saline stress (a parameter)

ECe_thr

is the threshold (breakpoint) ECe (a parameter)

S

is the slope, i.e. the loss in yield per unit ECe beyond the breakpoint (a parameter).

The yield is expressed in appropriate yield units (possibly diﬀerent by crop), and the ECe is expressed in
dS/m. The slope in the equation above is negative, and is expressed in appropriate yield units per dS/m.
Once an estimate of the unaﬀected yield Y0 is available, a plot in terms of percentage of unaﬀected yield

can be presented as

(2)

where
S%

is the percentage yield loss per unit of dS/m beyond the breakpoint.

Note that in the breakpoint model there is a discontinuity in the derivative of the yield to ECe at
ECe=ECe_thr.

37

The S-shaped model (Van Genuchten-Gupta)
This model is described by
(3)

where the additional symbols are
ECe50

is the ECe at which the yield has dropped to 50% of the maximum yield Y0 (parameter)

p

is a dimensionless shape parameter.

Once an estimate of the unaﬀected yield

is available, a plot in terms of percentage of unaﬀected yield is

given by
(4)

Note that in contrast to the breakpoint model, the parameters in the relative plot remain the same.
Also, there are no discontinuities in the derivative of yield to ECe.

Parameter estimation
The parameters have been estimated by minimizing the sum of squared diﬀerences between model and
data. Given a set of observations EC e (i),Y(i),

i=1,2,…,N the sum of squares is deﬁned by
(5)

where
p

is the vector of parameters

Yobs (i)

is the observed yield at the i-th ECe value

Y (i;p)

is the modelled yield at the i-th ECe value, for the parameters in vector p

38

The parameter estimates are found by minimizing p ; in mathematical terms

(6)
where the carret indicates the estimated value.
Conﬁdence intervals of the parametersThe co-variance matrix of the parameters is given by
(5)

Here, N is the number of samples, np the number of parameters and J is the Jacobian matrix, formed by
the derivatives of each output observation to the parameters. Hence, J is a N × np matrix. Consequently,
the covariance matrix P is a np × np (symmetric) matrix.
The standard deviation of the estimate is
(6)

and the correlation coeﬃcient for the oﬀ-diagonal elements is
(7)

An approximate 95% conﬁdence interval (Cramér-Rao lower bound) for parameter j when all other
parameters are at their optimal value is
(8)

The percentage is approximate since the model is non-linear in the parameters.
It must be noted that the individual parameter conﬁdence intervals can be used as such, but that combining
individual parameter conﬁdence intervals of several parameters can be misleading if there is correlation
between the estimates. The conﬁdence region is ellipsoidal, and the corner points of the region deﬁned by
Equation (8) are usually outside the conﬁdence region. In practical terms: the estimates of the threshold
and the slope are generally positively correlated, meaning that a lower threshold must be compensated by
a ﬂatter slope to maintain a good ﬁt. But the combination of a threshold on the low end of the conﬁdence
region and a slope on the high end is unlikely.

39

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