Weather Determined Relative Sensitivity of Plants to Salinity: Quantification and SimulationThe amelioration of plant salinity tolerance due to reduction in potential evapotranspiration is a long recognized phenomenon. In spite of this, salinity tolerance of plants is generally calculated from full season, time- and space-averaged response data. We hypothesized that the HYDRUS-1D model could be used to predict dynamic changes in plant salinity tolerance for a greenhouse vegetable crop over a full season and to determine best management practices regarding blending of saline with desalinated water for optimization of yields and water use efficiency (WUE). The specific objectives of the study were to determine dynamic vapor pressure deficit (VDP)–salinity response relationships of bell pepper plants grown in lysimeters and to apply them for hypothetical management scenarios when irrigating with blended desalinated and brackish water under commercial conditions. The transpiration response of bell pepper plants to salinity in the controlled lysimeter experiment was strongly influenced by variations in potential transpiration throughout the season. The plants were relatively tolerant during periods of low VPD and relatively sensitive during periods of high transpiration demand. Data were used to develop salinity response equations as a function of VPD. In a case study for Israel’s Arava Valley, transpiration and water productivity of bell peppers could be increased 5% by blending saline and desalinated water such that less saline water was applied during periods of relatively high sensitivity (high VPD) and more during periods of relative tolerance as compared to application of the same total of both sources of water blended at a constant ratio throughout the season. Sensitivity analysis of the dynamic crop response model revealed that such increases in water productivity would be even greater for more salt sensitive crops.
Understanding and treatment of the effect of salinity on crops is largely based on a conceptual model and empirical database presented by Maas and Hoffman (1977) and variations contributed by others (Feddes et al., 1978; van Genuchten and Hoffman, 1984). These models are based on full season yield (biomass production) response to time and depth averaged salinity. In these models, “sensitivity” refers to the extent to which biomass production is reduced by a given increase in salinity, while “toler-ance” indicates relatively low sensitivity. Plant related parameters that describe the severity of yield reduction due to salinity are presumed to remain the same over time in the models (van Genuchten and Hoffman, 1984), basically assuming that the plants grow under a steady state irrigation regime or can be described by average values. Under field condi-tions, however, the root zone is far from homogenous both in time and spatial dimensions. Cardon and Letey (1992) pointed out the importance of time variable parameters in the salinity response function, but they did not demonstrate this in their model. Bhantana and Lazarovitch (2010) showed that the salinity tolerance of pomegranate (Punica granatum L.) increased throughout the growing season, and Tripler et al. (2011) showed that from season to season the salinity tolerance of date palm (Phoenix dactylifera L., ‘Medjool’) decreased. In none of these cases were changes in salinity tolerance modeled as a function of a specific cause.
It has been recognized for some time that crops are injured by salt to a greater extent in warm compared to cool climates (Magistad et al., 1943). Hoffman and Rawlins (1971) demonstrated that some crops are more tolerant to salinity stress under higher relative humidity, while others are not. In accordance with this in the reporting of their well-known threshold slope model, Maas and Hoffman (1977) noted explicitly that “salt toler-ance is a relative value based on the climatic conditions under which the crop was grown.” Much of the information on which their published salt tolerance database (Maas and
We hypothesized that the HYDRUS-1D model could be used to predict dynamic changes in plant salinity tolerance for a greenhouse vegetable crop over a full season and to determine best manage-ment practices regarding blending of saline with desalinated water for optimi-zation of yields and water use efficiency.
T. Groenveld and N. Lazarovitch, Wyler Dep. of Dryland Agriculture, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Des-ert Research, Ben-Gurion Univ. of the Negev, Sede Boqer Campus, 84990, Israel. A. Ben-Gal and U. Yermiyahu, Soil Water and Environ-mental Sciences, Agricultural Research Organization, Gilat Research Center, Mobile Post Negev 2, 85280 Israel. *Corresponding author ([email protected]).
Vadose Zone J. doi:10.2136/vzj2012.0180Received 8 Nov. 2012.
Original Research
Thomas GroenveldAlon Ben-Gal*Uri YermiyahuNaftali Lazarovitch
Hoffman, 1977) was built originated from experiments performed in the southwestern United States, limiting its relevance to places with similar climatic conditions (Maas, 1993).
The climatic variables that determine potential evapotranspiration (ETp) are temperature, radiation, wind speed, and relative humidity, which in many cases—particularly in protective structures—can be fairly represented by the VPD. Together with root zone water potential, VPD determines plant water potential (Li and Stang-hellini, 2001). Increased VPD is known to reduce the fruit growth rate in tomato because of increased transpiration, direct water loss from fruit, and water efflux from the fruit to the stem (Johnson et al., 1992, Leonardi et al., 2000). It appears that at the fruit level the mechanism involved in response to high VPD is water shortage and not assimilate shortage, since the VPD has an effect on fruit fresh weight but not on the accumulation of dry matter (Leon-ardi et al., 2000). Leonardi et al. (2000) noted that the effects of increased VPD in their experiment (1.6 vs. 2.2 kPa) were similar to those previously observed in response to moderate water and salt stresses (Ho et al., 1987; Adams, 1991; Mitchell et al., 1991). An et al. (2001) found that salinity stress was reduced in two soy-bean cultivars at increased relative humidity. Li and Stanghellini (2001) performed several experiments with two combinations of salinity and VPD and concluded that both electrical conductivity (EC) and potential transpiration affected water content of organs. They demonstrated that a reduction in VPD modified the effect of the root zone salinity by both increasing the percentage of market-able yield and increasing fresh fruit weight (Li et al., 2001). The reduction of fresh yield with increased root zone salinity occurred simultaneously with an increase in relative fruit sugar content (Li et al., 2001). Plants grown under a lower VPD had significantly higher WUE than those grown under a higher VPD. Cuartero et al. (2006) described a similar experimental setup, which resulted in salinity effects being minimized under high relative humidity. Karlberg et al. (2006) explored the effect of weather conditions and salinity on growth of tomatoes in two distinct seasons. They discussed the effect of salinity on WUE, described as depending largely on whether reductions in transpiration were due to increased osmotic gradient or to specific ion toxicity. They concluded that the WUE of a crop was the result of the prevailing weather during the growing season in combination with other environmental condi-tions. Ityel et al. (2012) experimented with a modified root zone that increased plant available water content. They noted a higher sensitivity to salinity with increasing VPD for bell pepper plants and a lower sensitivity for the modified root zone.
Use of climate control in greenhouses has been demonstrated to increase salinity tolerance (Li et al., 2001; Li and Stanghellini, 2001). Climate control is limited by humidity thresholds at which fungi may develop or nutrient uptake reduced (Gisleröd et al., 1987; Grange and Hand, 1987; Adams and Ho, 1993). Climate control physical systems are expensive and are only applicable to the con-fined space of a greenhouse. With the advent of desalination and
its increasing use in agriculture (Yermiyahu et al., 2007; Ben-Gal et al., 2009), modifications to irrigation water salinity throughout the season have become possible. In Israel many growers currently have both saline groundwater (GW) and DW available for inde-pendent use or mixing before irrigation. Desalinated water lacks some of the nutrients essential to plant growth that are abundant in the GW such as Ca, Mg, and S. One possible solution is to blend the water of these two sources to supply these nutrients. This process, however, also reintroduces unwanted salts such as NaCl to the irrigation water (Ben-Gal et al., 2009). Current blending management applies a fixed ratio of desalinated and saline water throughout a cropping season. Equipment allowing changes in the blending ratio over time, often already in place, open up the possi-bility to change the irrigation water salinity according to expected temporal plant salinity tolerance variations.
Numerical models describe plant water uptake and temporal–spatial variable salinity in the root zone. Plant salinity tolerance can feasibly be considered in a transient manner, similar to the approach of Feddes et al. (1978) for water stress, by altering the parameters used in the salinity response models over time as a func-tion of the VPD. The macroscopic numerical model HYDRUS-1D (Šimůnek et al., 2008a) considers transient water flow, solute transport, and compensated plant root water and nutrient uptake. These are calculated as a function of soil hydraulic properties, irri-gation salinity and amount, root distribution, ETp, and ion uptake, as described in Šimůnek and Hopmans (2009). The water flow algorithm in HYDRUS-1D has been widely tested and used for different applications (Šimůnek et al., 2008b), including use of the root water uptake model (Zhu et al., 2009) with N transport and uptake (Cote et al., 2003; Gärdenäs et al., 2005; Hanson et al., 2006) or with salinity (Skaggs et al., 2006; Shouse et al., 2011; Oster et al., 2012; Ramos et al., 2012). Yet, until today, simula-tions of salinity effect on water uptake were performed solely using parameters that were constant over time.
We hypothesized that the HYDRUS-1D model could be used to predict dynamic changes in plant salinity tolerance for a green-house vegetable crop over a full season and to determine best management practices regarding blending of saline with desali-nated water for optimization of yields and water productivity. The specific objectives of the study were to determine dynamic VDP–salinity response relationships of bell pepper plants grown in lysim-eters and to apply them for hypothetical management scenarios when irrigating with blended desalinated and brackish water under commercial conditions.
6Materials and MethodsGreenhouse ExperimentBell peppers (Capsicum annuum var. Celica) were grown on an automated rotating system, consisting of 24 barrel-shaped lysim-eters (Lazarovitch et al., 2006), each with a 0.2-m radius and depth
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of 0.6 m, located in a greenhouse at the Gilat Research Center in the northwestern Negev, Israel (31°20¢ N, 34°40¢ E). The lysim-eters incorporated a 70-cm drainage extension of highly conduc-tive media (rockwool) to ensure negative soil water potential at the soil lower boundary without influences water flow through the system (Ben-Gal and Shani, 2002). The lysimeters were filled to a depth of 0.55 m with a sandy soil (91% sand, 1% silt, 8% clay). The lysimeters were flushed with water of their respective salinity treatment before transplanting the seedlings to leach out any con-taminants and to ensure that the drainage water EC was equal to the irrigation water EC at the start of the experiment.
The irrigation solution was composed of distilled water to which the nutrients essential for plant growth were added at the con-centrations considered optimal for bell pepper production (Ben-Gal et al., 2009). Fertilizers (potassium sulfate, calcium nitrate, monopotassium phosphate, magnesium nitrate, ammonium sul-fate, ammonium nitrate, and micronutrients) were added to reach 65 mg L−1 Ca, 30 mg L−1 Mg, 48 mg L−1 SO4–S, 0.3 mg L−1 B, 99 mg L−1 N, 23 mg L−1 P, and 176 mg L−1 K. NaCl was added at eight different concentrations: 0, 2.5, 5, 7.5, 10, 15, 20, and 40 mM. The resulting irrigation water EC levels were 0.9, 1.2, 1.4, 1.7, 2.1, 3.1, 3.9, and 6.7 dS m−1, reflecting the effect of nutrients as well as NaCl added to the irrigation water. Each salinity treatment was replicated three times. Nutrients contributed 0.6 to 0.9 dS m−1 to each treatment. The soil was sandy enough that effects of sodicity due to differing Na to Ca and Mg ratios were not expected. The pH of the irrigation water was stable at approximately 5.8 during the entire season. Irrigation solution EC was measured weekly, and its macronutrient composition was measured every 2 wk.
Three pepper seedlings (Capsicum annuum var. Celica) were trans-planted to each lysimeter on 13 Sept. 2009 and thinned to one plant after 2 wk. Each lysimeter was irrigated 2 L d−1 with solution of its respective salinity for the first 30 d and from then on was irri-gated at 130% of the average evapotranspiration (ET) measured in each treatment (leaching fraction of 0.23), which was determined twice a week by a water balance:
ET I D S= - - [1]
where I (kg) is irrigation, D (kg) is drainage, and DS (kg) is the difference in the lysimeter mass between the beginning and end of the period over which the water balance was determined. Evapo-ration was kept minimal by mulching the soil with air-permeable geotextile around the plant so that ET was assumed to equal tran-spiration (T). The twice weekly mass balance allows the assump-tion that plant dry matter increases were negligible compared to water fluxes. Plant protection and maintenance were according to local commercial practices. Drainage water EC was measured twice a week and its macronutrient and NaCl concentration were measured every 2 wk.
The experiment was conducted from September to December, and the relative humidity and temperature were continuously measured to calculate the VPD within the greenhouse. The soil solution salinity was assumed to be equal to the drainage water salinity as found in previous similar studies (Ben-Gal et al., 2008). Flow-ers were removed regularly throughout the season so that growth was only vegetative. In this way the relative transpiration was kept similar to the relative plant biomass. Total biomass yield was deter-mined as above ground (leaves and shoots) biomass removed and dried after 98 d.
Assessing Salinity Sensitivity—Weather RelationshipsA modified version of HYDRUS-1D (Šimůnek et al., 2008a) was used to simulate plant response of peppers irrigated with varied irrigation water salinity. Yield was reduced as soil solution salinity increased according to the sigmoid-shaped function of van Genu-chten and Hoffman (1984):
a
p e
e50
1
EC1
EC
PYY
=æ ö÷ç ÷+ç ÷ç ÷çè ø
[2]
where Ya is actual yield, Yp is potential yield, ECe50 (dS m−1) is the EC of saturated paste extract of soil (ECe) at which the yield is 50% of the potential yield, and P (–) is an empirical shape parameter that determines the steepness of the reduction of yield between the maximal and 50% yield (van Genuchten and Gupta, 1993). The ECe50 and P parameters are empirically derived but nonethe-less have identifiably biophysical characteristics (Steppuhn et al., 2005). To consider a change in plant salinity tolerance in time steps smaller than a full season, a linear correlation between relative transpiration and relative yield was considered:
a a
p p
Y TY T
= [3]
where Y is the yield, T is transpiration, and the subscripts a and p stand for actual and potential, respectively (de Wit, 1958; Ben-Gal et al., 2003). Changes in plant salinity tolerance over time were determined by recalculating the ECe50 parameter according to 10-d averaged soil solution salinity and transpiration. The P parameter of Eq. [2] was kept fixed throughout the season and was determined by optimizing both it and the ECe50 to fit the season–total yield data. The software used for these calculations was the solver program of Microsoft Excel.
The threshold–slope salinity reduction model in HYDRUS-1D includes a database of parameter values for different crops based on Maas (1990). To normalize the effect of soil type on water content and salinity seen by the plant roots, these values are based on the
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EC of the saturated paste extract (ECe in dS m−1). HYDRUS-1D converts the ECe–based values to the equivalents in terms of soil solution at fi eld capacity before determining the reduction in transpiration due to the calculated salinity at every depth in the root zone (Skaggs et al., 2006). To apply it in HYDRUS-1D, the ECe50 parameter of the van Genuchten and Hoff man (1984) salin-ity reduction function was converted to the equivalent value at fi eld capacity, which we will refer to as ECfc50. To model the eff ect of VPD on plant salinity tolerance as observed in the experiment, the HYDRUS-1D model was manipulated such that the ECfc50parameter changed transiently as a function of the VPD:
fc50 *VPD
EC VPD
bæ ö÷ç=a ÷ç ÷çè ø [4]
where VPD* is a reference VPD taken as 1 kPa, a (dS m−1) and b (–) are empirical parameters describing the dependence of the ECfc50 parameter on the VPD. For modeling purposes, an asymp-totic function was chosen as the VPD range of the dataset used exceeded that of the experimental data. Th e a and b parameters were determined so that the ECfc50 values would not extend beyond those measured in the experiment.
Simulati ng Water Management of Bell PeppersWe demonstrate an example of possible consideration of weather–plant response to salinity relationships via the simulation of bell pepper transpiration over a full season under four salinity treat-ments, namely saline GW, DW, water blended at the same ratio the entire season (BW), and transiently blended water (TBW). Th e BW consisted of DW and GW mixed at a ratio of 3:1, a rate expected to supply suffi cient Ca, Mg, and S for nonlimited plant growth (Ben-Gal et al., 2009). In the TBW treatment, the irriga-tion water electrical conductivity (ECiw) was changed over time to match the plant salinity tolerance:
iw fc50EC ECd= +e [5]
where d (–) and e (dS m−1) are empirical parameters determined by minimizing the total irrigated amount under the condition that the season–total mixing ratio stayed the same as that of the BW treatment. Th e total season irrigation amount was minimized by solving for the lowest season total irrigation amount under the constraint that the season total ratio between saline and desali-nated water stayed the same as the BW treatment. In this way, the treatments received identical amounts of Ca, Mg, and S from the GW. Th e season total mixing ratio stayed the same so that the BW and TBW could be compared. Th is linear conversion formula was used for simplicity; however, the optimal relationship between ECiw and ECfc50 may be nonlinear.
Each of the salinity treatments were irrigated at the rate required to achieve 95% of potential yield, up to an I/ETp (irrigation/potential evapotranspiration) of 2.5 for GW, as determined using a modifi ed version of Shani et al.’s (2007) ANSWER model. As opposed to empirical steady state models that determine the leaching require-ment for a specifi c growth rate (Letey et al., 2011), the ANSWER model considers plant sensitivity to water stress, ETp, and soil hydraulic parameters in an analytical expression. Th e three treat-ments of fi xed salinity (DW, BW, GW) were irrigated at a fi xed I/ETp rate. Th e I/ETp rate of the TBW treatment varied between 1.18 and 2.5 throughout the season as the irrigation water salinity changed, to achieve 95% yield according to the same model. An overview of the treatments can be found in Table 1. Examples of ANSWER model application for bell peppers in the same soil and climate can be found in Ben-Gal et al. (2008, 2009).
Th e simulations were run with environmental data (Ben-Gal et al., 2009) from the August 2006 to April 2007 growing season at the Zohar Research and Development station located just south of the Dead Sea (30°57¢ N, 35°23¢ E, 350 m below sea level). In this
Table 1. Simulation of pepper response to irrigation water salinity and management options.
Treatment† EC‡ I/ETp§ LF¶ I T WP# Cl drain†† N uptake‡‡ N drain‡‡
† Th e four treatments applied: DW is desalinated water, BW is blended water, GW is groundwater, and TBW is transiently blended water.‡ EC is electrical conductivity of the water.§ I/ETp is applied irrigation (I) divided by potential evapotranspiration (ETp).¶ LF is target leaching fraction (drainage/irrigation).# Water productivity (WP; T/I).†† Cl drained from root zone.‡‡ N uptake and N drained from root zone.
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desert region, summer temperatures are extremely high and the winter temperatures are mild, allowing a winter growing season when there is a high market demand for vegetables. Seedlings are planted in the late summer when the potential transpiration is still relatively high, produce fruit through the winter season with low potential transpiration, and stop producing when the transpirational demand becomes exceedingly high in the spring (Fig. 1). Th e reference evapotranspiration (ET0) was measured in a Class A pan located outside the net house, and the crop factor (Kc) recommended for bell pepper by the extension service of the Israeli Ministry of Agriculture was used to estimate the potential transpiration (Tp) for the plants growing inside the net house. Th e VPD was calculated from the relative humidity and temperature measurements performed inside the net house at 2-m height.
Th e van Genuchten–Mualem model (van Genuchten, 1980) was used to simulate water fl ow without considering hysteresis in the HYDRUS-1D simulations. For solute transport, the equilibrium model was used with the Crank–Nicholson implicit scheme for the time weighting and Galerkin fi nite elements for the space weight-ing scheme. Th e longitudinal dispersivity of the soil was 10 cm, and the bulk density was 1.5 g cm−3. Th e eff ect of the matric and osmotic stresses was considered to be multiplicative, as suggested by Shani and Dudley (2001). Root distribution, kept constant throughout the simulation, was linear assuming maximum at sur-face and 0 at 50 cm depth. Th e parameters for S-shaped water stress reduction (van Genuchten, 1987) were kept as the default values, namely the pressure head at which there is 50% reduction as −800 cm and the P3 parameter (dimensionless shape of drought stress function) as 3. Soil hydraulic properties used were from Carsel and Parrish (1988) for sandy soil.
Relati ve Importance of Plant Salt Sensiti vity ParametersAft er simulating seasonal transpiration according to the experimen-tally determined VPD–ECfc50 relationship, parameters a and b(Eq. [4]) and P (Eq. [2]) were varied to explore the eff ect of climatic conditions on the plant salinity tolerance over a broader range. Th e seasonal Cl load for each treatment was calculated from the irriga-tion water concentrations and amount. Th e amount of N taken up and leached out from below the root zone was calculated with the model using passive uptake (Šimůnek and Hopmans, 2009).
6Results and DiscussionExperimental Vapor Pressure Defecit–Salinity Response Relati onshipsIncreasing irrigation water salinity strongly reduced average accu-mulated transpiration rates in the lysimeter experiment (Fig. 2). Th e most saline treatment reduced transpiration to less than half of that of the least saline treatment. Increases in transpiration over time were approximately linear since transpiration demand decreased due to cooling weather as plant size increased. Th e slope and intercept of the linear relationship between relative transpiration and relative yield (normalized to maximum values within the experimental dataset) were not diff erent from 1 and 0, respectively, at a 95% confi dence interval (P < 0.0001) (Fig. 3, Eq. [3]). Th is relationship enabled relative transpiration determined daily to be used as an indicator of the plant response to salinity over time. Th e ECe parameter of Eq. [2] also varied over time and was estimated from the drainage water salinity. Figure 4 shows the EC of the drainage water of the eight salinity treatments measured throughout the season, with the diff erent treatments clearly distinguished. Th e fact that the drainage water salinity did not increase much over time demonstrates apparent steady
Fig. 1. Th e Class A pan evaporation at the Zohar Experimental Station during the 2006 to 2007 growing season (ETp) and the vapor pressure defi cit (VPD) inside the net house.
Fig. 2. Average accumulative transpiration of the eight salinity treat-ments in the lysimeter experiment. Error bars are standard deviation of fi nal values.
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state conditions caused by regular irrigation and the constant leaching fraction of 0.23 (Tripler et al., 2012).
Parameter optimization of the van Genuchten–Hoff man salin-ity reduction function (Eq. [2]) for the full season data resulted in ECfc50 of 6.5 (dS m−1) and P of 1.65 (R2 = 0.77). Th e ECfc50parameter was recalculated for short time steps throughout the season (Fig. 5), with the P parameter fi xed at 1.65. Contrary to the common assumption that the parameter stays the same, the ECfc50 parameter varied greatly over time, tripling in value in a
period of less than 2 wk (aft er DAT 55). Th e VPD measured in the greenhouse was inversely correlated to the ECfc50 param-eter, indicating that when the transpiration demand dropped, plant salinity tolerance increased. Th e ECfc50 values plotted as a function of the VPD in Fig. 6 results in an inverse correlation with a power equation having an R2 value of 0.68. Th e choice to describe the correlation by means of a power equation instead of a linear equation is because it is assumed that as the VPD increases the ECfc50 parameter will level off gradually, with the heat stress associated with extremely high VPD causing stomata closure fol-lowing severe plant stress (Avissar et al., 1985) before the ECfc50
Fig. 3. Th e relationship between whole season relative transpiration and relative dry biomass yield for the bell pepper lysimeter experi-ment. Ta is actual transpiration, Tp is potential (maximum) transpira-tion, Ya is actual dry above ground biomass, and Yp is potential (maxi-mum) biomass.
Fig. 4. Average drainage water salinity of the eight salinity treatments in the bell pepper lysimeter experiment over time. EC is electrical con-ductivity. Error bars are standard deviation of fi nal values.
Fig. 5. Vapor pressure defi cit (VPD) and the electrical conductivity of soil solution at fi eld capacity causing 50% reduction in transpiration (ECfc50). ECfc50 and VPD calculated for 10-d running averages at the bell pepper lysimeter experiment.
Fig. 6. Th e electrical conductivity of soil solution at fi eld capacity caus-ing 50% reduction in transpiration (ECfc50) as a function of vapor pressure defi cit (VPD) at the bell pepper lysimeter experiment. Th e exponential decay function is displayed with best fi t a and b param-eters (Eq. [4]).
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would reach zero. Th e best fi t a and b parameters were 4.61 (dS m−1) and −0.61 (–), respectively.
Simulated Water Management StrategiesSimulated accumulated transpiration (Fig. 7) was lowest for GW (294 mm with 1661 mm irrigation) and highest for DW (478 mm with 763 mm irrigation). Th e resultant Cl load to the GWwas 37 g m−2 for the DW treatment, as opposed to 1299 g m−2 for the GW treatment. Irrigation with BW resulted in reduced transpiration eventually reaching 5% less than that of DW. Th e BW and TBW treatments received the same amount of saline and desalinated water over the course of the season (Table 1) and therefore had identical Cl load (Fig. 7 and Table 1). When considering the aand b parameters calculated from the experimental data, the Pparameter fi xed at 1.65 and the d and e parameters optimized at 0.294 (–) and 0.384 (dS m−1), respectively, the TBW treatment transpired around 5% greater than the BW treatment by the end of the season (Table 1). Th e increased transpiration was due to manipulation of irrigation water salinity considering diff erences in plant salinity tolerance over time; the transpiration of the TBW was higher than the BW treatment during the periods of high
potential transpiration (compare Fig. 1 and 7) when the treatment received less saline water. During the cooler times in the growing season, the opposite occurred as the TBW irrigation water was more saline than the BW treatment. Irrigation with lower salinity water allows higher water productivity (Table 1) since more of the water is utilized in transpiration (= biomass production) and less for leaching salts. Manipulating irrigation water salinity such that higher salinity was applied during periods of relatively low sensitiv-ity was shown to increase water productivity by 5% compared to a constant blending scheme. Equal irrigation water N concentra-tion was given to the plants and therefore there was much more N leached from the root zone with the GW treatment where a high LF was used to leach out the salts (Table 1).
Th e diff erences in transpiration between the TBW and BW treat-ments presented thus far refl ect modeling based on parameters determined from the lysimeter experiment. Results are expected to be a function of specifi c crop sensitivity. Figure 8 shows the eff ect of increasing and decreasing the P parameter and the range of ECfc50 values (equivalent to changing the a and b parameters of Eq. [4]) on the percentage diff erence in transpiration (%D). Van Genuchten and Gupta (1993) evaluated the P values of 204 plants from published studies. Th ey reported a median value of 3.05 and a reasonable normal distribution with most falling between 1 and 5. A plant with higher sensitivity to salinity (lower ECfc50) would have an increasing %D over this range of P values, whereas less sensitive plants to salinity (higher ECfc50) would have decreasing %D when the P value increases above 3 (Fig. 8). Increases in %Dare the steepest in the low P value range for all ECfc50 ranges. Th is exemplifi es that the potential to manipulate dynamic, meteorologi-cal conditions related response by changing irrigation water salin-ity or altering leaching fractions is crop dependent and highest for those with the greatest overall sensitivity to salinity.
Fig. 7. (a) Cumulative transpiration and (b) cumulative leached chlo-rides of the four salinity treatments. Desalinated water (DW), tran-siently blended water (TBW), blended water (BW), groundwater (GW).
Fig. 8. Th e eff ect of increasing and decreasing P (Eq. [2]) and ECe50(Eq. [4]) on the percentage of diff erence in transpiration between the transient and constant blended water treatments (%D).
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In this work the problematic use of whole season data to model responses occurring over short time periods is addressed. Certainly, one could raise a similar concern regarding the use of root zone averaged conditions for driving two- or three-dimensional solutions of root uptake. In our case, the high leaching fractions and shallow rooting depths in both the lysimeter experiment and in the simu-lations were expected to create near one-dimensional conditions regarding water content and solute concentration in the root zone, rendering the assumption reasonable. This of course will not always be the case; therefore, root depth or density specific water uptake reduction due to salinity should be rectified in future studies.
6ConclusionsThe transpiration response of bell pepper plants to salinity in a con-trolled lysimeter experiment was found to be influenced by varia-tions in potential transpiration throughout the season. The plants were relatively tolerant during periods of low VPD and relatively sensitive during periods of high transpiration demand. Data were used to develop salinity response equations as a function of VPD. In a case study for Israel’s Arava Valley, transpiration and water productivity of bell peppers could be increased 5% by blending saline and desalinated water such that less saline water was applied during periods of relatively high sensitivity (high VPD) and more during periods of relative tolerance as compared to application of the same total of both sources of water blended at a constant ratio throughout the season. Sensitivity analysis of the dynamic crop response model revealed that such increases in water productivity would be even greater for more salt sensitive crops.
We believe these results to be of interest, in spite of the fact of the limitations set by the use of only a single, unique crop (bell pepper) as a case study. Additional limitations to the approach presented in the current study were dictated by the HYDRUS model. Consideration of plant and root growth in response to conditions, not allowed for by HYDRUS, is certainly necessary for truly accurate simulations.
It is crucial to understand the effect of weather on plant salinity tolerance to adjust the salinity reduction parameters determined under one set of conditions for use under different conditions. The threshold–slope type salinity functions are time-proven robust models used worldwide; by considering the transient nature of their parameters, they will remain as relevant in numerical models as they have been until now in steady state models. Other aspects that can potentially be explored with this transient approach to salinity stress modeling include the effects of LF, rooting depth, or irrigation frequency on salinity and temporal drought stress.
Increasing transpiration by responding to changes in plant salin-ity tolerance is a potential management practice where different water qualities are available. Higher water quality comes at a price, however, and the tradeoff between increased yield and higher cost of inputs (water) may be calculated by means of this model. By
knowing the plant sensitivity to salinity over time a controlled stress may be introduced, to induce flowering for example. Other applications of the transient salinity reduction model may be in fertigation management to manipulate interactions between salin-ity and nutrient uptake, in determining which crops are optimal to grow under certain environmental conditions, or to predict value of adjusting environmental conditions by means of climate control.
AcknowledgmentsThis work received funding from The Chief Scientist of Israel’s Ministry of Agricul-ture and Rural Development (Grant 304-0393), by the I-CORE Program of the Plan-ning and Budgeting Committee and the Israel Science Foundation (Grant 152/11), and from North-Central Arava Research and Development. Thanks are due to Eu-gene Presnov, Inna Faingold, and Ludmila Yusupov of the Gilat Research Center for technical field and laboratory support.
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