Modelling of soil salinity and halophyte crop production

Environmental and Experimental Botany 92 (2013) 186–196

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Modelling of soil salinity and halophyte crop production E. Vermue ∗ , K. Metselaar, S.E.A.T.M. van der Zee Soil Physics, Ecohydrology and Groundwater Management, Department of Environmental Sciences, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands

a r t i c l e

i n f o

Article history: Received 5 June 2012 Received in revised form 9 October 2012 Accepted 11 October 2012 Keywords: Root water uptake Crop modelling Drought stress Salinity stress Halophytes Interaction of abiotic stresses

a b s t r a c t In crop modelling the soil, plant and atmosphere system is regarded as a continuum with regard to root water uptake and transpiration. Crop production, often assumed to be linearly related with transpiration, depends on several factors, including water and nutrient availability and salinity. The effect of crop production factors on crop production is frequently incorporated in crop models using empirical reduction functions, which summarize very complex processes. Crop modelling has mainly focused on conventional crops and specific plant types such as halophytes have received limited attention. Crop modelling of halophytes can be approached as a hierarchy of production situations, starting at the situation with most optimal conditions and progressively introducing limiting factors. We analyse crop production situations in terms of water- and salt limited production and in terms of combined stresses. We show that experimental data as such may not be the bottleneck, but that data need to be adequately processed, to provide the basis for a first analysis. Halophytic crops offer a production perspective in saline areas, but in other areas long-term use of low quality irrigation water for halophyte production can result in serious soil quality problems. An overview is given of potential problems concerning the use of (saline) irrigation water, leading to the conclusion that soil quality changes due to poor quality water should be considered in determining the areas selected for halophyte growing. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Over 800 million hectares of land worldwide are affected by salinity (Munns, 2005). With the prospect of climate change, many areas are faced with shifts in rainfall patterns in combination with more local climatic extremes (Yeo, 1999). Together with an increasing pressure on the world’s food production due to an expanding human population, the pressure on crop production is ever increasing. Trying to understand how levels of production factors affect crop production has a long history in agronomic research going back to the studies of Liebig and Liebscher, leading to response function approaches for optimal fertilizer application (Paris, 1992) and a continuing discussion regarding resource use efficiency (Van Ittersum and Rabbinge, 1997). Crop production is determined by several crop production factors, which can be either biotic or abiotic. Well-known abiotic factors are salinity, drought and nutrient availability. When one or more crop production factors are beyond optimum, transpiration decreases and consequently biomass production will decrease. This reduction as a function of changes in the crop production factor, a reduction function, has

∗ Corresponding author. Tel.: +31 317 482532. E-mail address: [email protected] (E. Vermue). 0098-8472/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.envexpbot.2012.10.004

been empirically derived for many conventional crops, mostly for situations where one type of stress prevails. In field situations, combinations of stresses occur, such as the combination of drought and salinity stress in (semi-)arid areas. To deal with such situations, it is necessary to understand how stresses interact, and how these interactions can be described in a reduction function modifying the crop growth rate. The effect stresses have on crop growth depends on several environmental conditions, like soil type, radiation and precipitation (Shannon and Grieve, 1999). For most field situations these are not constant in time. Whereas response functions were and are used to describe and summarize static responses and average stresses over time, crop models allow formulation and testing hypotheses regarding dynamic responses, as the reduction functions modify the growth rate. Models can help us to explore the possible effects of changes in the variability and the mean of environmental conditions on crop growth, and focus costly experiments on relevant situations and conditions and pinpoint the most important temporally variable production constraints. From a hydrological point of view a model for crop production should regard the Soil Plant Atmosphere system as a continuum (SPAC) (Gradmann, 1928; Van den Honert, 1948). It should link the transport of water and solutes in the soil to a crop model to simulate vegetation development as a function of root water uptake and other environmental variables, such as light intensity and

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evaporative demand. In the SPAC concept, water – as crop transpiration (Penman, 1970) – links the different parts of this continuum. Primary production is linearly related with (sometimes scaled) transpiration (De Wit, 1958; Philip, 1966; Zwart and Bastiaanssen, 2004), also creating a continuum of the soil, plant production and atmosphere. Expecting an increasing scarcity of good quality irrigation water and the hazard of increasing salinity stress that may be due to climate change, halophytic crops might offer an interesting alternative to conventional crops (Rozema and Flowers, 2008). In crop modelling, halophytic crops are poorly represented and we are unaware of crop model studies that focus on halophytes. The ecophysiological behaviour of halophytes differs from glycophytic crops as the specific traits of halophytes have developed for the purpose of survival, instead of high productivity (Munns and Tester, 2008; Breckle, 2009). In this paper, we focus on the biomass production function used in crop modelling, in particular with regard to modelling halophytic crops. Besides the primary impact of salinity on plant transpiration, also other aspects that affect the sustainability of crop production under brackish or saline conditions are considered. 2. Crop production factors Plant growth depends on several abiotic and biological factors, such as climate (temperature, light and water availability), pests and diseases, soil (physical, biological and chemical conditions) and management practices. When these crop production factors are optimal for crop growth, plants can develop to their full potential. In sub-optimal conditions, plants experience stress and consequently crop growth will decrease. In the following section we will focus on environmental stresses and how these can be incorporated in crop growth modelling. 2.1. Root water uptake and water stress Root water uptake and water transport in the soil are simulated as a function of soil water pressure head, osmotic pressure head, soil hydraulic functions, root characteristics and meteorological conditions (Hopmans and Bristow, 2002; Skaggs et al., 2006). Quantification of root water uptake has a long history in science, which started with the papers of Gradmann (1928) and Van den Honert (1948). The rate of water flow through the SPAC is assumed to be proportional to the potential difference across the considered part of the continuum and is inversely proportional to the water flow resistance (Eq. (1)). T =v=−

hleaf − hsoil htotal = Rtotal Rplant + Rsoil

(1)

where T (cm s−1 ) is the transpiration rate, v is the water flux density (cm3 cm−2 s−1 ), hsoil and hleaf (cm) are pressure heads in the soil and in the leaves (their difference is htotal ), and Rplant and Rsoil (s) (summed equal to Rtotal ) are hydraulic resistances of the soil and the plant, respectively (Feddes and Raats, 2004). Well-known stresses in crop growth and crop modelling are drought and salinity. Symptoms for both stresses – at least in glycophytes – are very similar, such as reduced growth rate, reduced shoot growth, and reduced leaf extension rate. Munns (2002) shows that the early responses to water and salt are essentially identical, and that salt specific effects occur in the older leaves. Both stresses are assumed to reflect reduced water availability, but are physically different. Drought stress occurs when soil moisture becomes depleted due to the absence of rainfall or irrigation or the lack of capillary rise from shallow groundwater and the evaporative demand can no

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longer be met. Soil water movement is induced by a gradient of soil water potential (h)1 (a negative pressure; the absolute value is referred to as suction). In case of root water uptake this means that the root water potential will be smaller than the bulk soil water potential, creating a flow of water towards the roots. To obtain the Darcian flux towards the roots, one has to know the hydraulic conductivity K (m/d) (Scheidegger, 1960), which describes how easy water can flow through the soil and is itself a strongly nonlinear and sometimes hysteretic nonlinear function of the soil water potential. As the soil becomes drier and the soil water pressure potential decreases (becomes more negative) the plant needs to decrease its root water pressure head even more to obtain sufficient water. It is known that plants are able to create root water pressure heads of −150 m (Feddes et al., 1976). In modelling, salinity is expressed as the osmotic head ho (m) or, more conveniently, by the saturated electrical conductivity ECe , which linearly depends on the ion concentration in solution. In contrast to the pressure head, the osmotic head does not affect the hydraulic conductivity K itself, but only influences the gradient of pressure head. The presence of high salt concentrations in the soil solution lowers the bulk soil water potential, reducing the gradient between root water potential and bulk soil water potential, making it more difficult for plants to take up water, which might result in water deficit (osmotic effect). In certain conditions this is a selfenhancing effect, as most plants avoid taking up salts, which then accumulate in the rhizosphere, causing a further reduction of the head gradient (Stirzaker and Passioura, 1996). However, the effect of salinity is not limited to an increased osmotic head. Other effects of salinity stress are ion toxicity and nutrient deficiency due to changes in solubility, transport rate to the roots, and impaired internal distribution of nutrients. It is not currently quantifiable to which extent these processes contribute to the final crop production (George et al., 2012). In current reduction function based crop modelling, nutrient deficiency effects are not yet clearly separated from the reduction due to osmotic stress. 2.2. Reduction functions In crop modelling, growth reductions due to drought or salinity stress are simulated by incorporating a reduction function, where the relative reduction in crop growth is a function of pressure head h (m) (drought) or electrical conductivity ECe (dS/m) (salinity). For both stresses, a comparable piece-wise linear function is used. For drought, this function is often referred to as the Feddes function, for salt stress as the Maas–Hoffman function (Fig. 1). In current ecohydrology, these functions are the most common ones (Rodríguez-Iturbe and Porporato, 2004; Shah et al., 2011), but alternative reduction functions have been proposed (Van Genuchten and Hoffman, 1984; Van Genuchten, 1987). All these functions are, however, purely empirical. The Feddes function (Fig. 1a) describes the reduction in transpiration (T) as a function of pressure head if water availability is insufficient (Feddes et al., 1978). Partitioning the potential transpiration over the rooting depth results in the potential rootwater uptake (Sp ). The decrease of S from the potential to an actual value is given by S = ˛(h)Sp

(2)

where Sp and S (cm s−1 ) are, respectively, the potential and actual root water uptake and ˛ is the reduction factor which varies between 0 and 1 and is a function of pressure head. If the crop production factors are non-limiting, uptake is solely dependent on

1

1 Pa of water pressure equals 10−2 cm.

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Fig. 1. (a) Root water uptake as a function of pressure head. (b) Root water uptake as a function of osmotic head. Quoted with permission by J.C. van Dam.

atmospheric and plant conditions. Hence, if water uptake and transpiration are optimal, the reduction factor ˛ is equal to unity. When water availability is limiting (Fig. 1a, for h3 to h4 ), root water uptake reduces and leaf stomata close. In this so-called falling rate phase, S no longer attains Sp and transpiration reduces linearly with pressure head: ˛ < 1. Another falling rate phase is observed for h2 to h1 . In this situation the falling rate phase is due to an excess of water, waterlogging, where a lack of oxygen supply to the plant roots develops. This can also cause the transpiration and consequently the root water uptake to drop. Osmotic effects on RWU (root water uptake) are often described by the Maas and Hoffman reduction function (Maas and Hoffman, 1977), shown in Fig. 1b and Eqs. (3) and (4).

and Grieve, 1999). Therefore, it is debatable whether the same parameter values still hold in different environmental conditions. Since they are location-specific, new experimental reduction functions for different soil, species/cultivar, and climate conditions require new experimental efforts. However, this is very timeconsuming and costly, and therefore, a process-based model that limits the experimental effort can be very cost-effective. In the last decades progress has been made for these types of models (De Jong van Lier et al., 2009), although not all processes are yet fully understood.

S = ˛(ho )Sp

In most field situations different types of stresses occur simultaneously and consequently interact. In this section we focus on two types of stress interactions, i.e. salinity and drought, and salinity and nutrients.

˛(ho ) = 1 −

(3) a (homax − ho ) 360

(4)

where 360 is a factor to convert the EC-based slope (a (% dS−1 m)) to osmotic head (cm) (U.S. Salinity Laboratory Staff, 1954). The Maas and Hoffman relationships between relative yield as a function of the EC or the osmotic head of the irrigation water have been established on the basis of experiments in California. These relationships are response functions assumed to hold during the entire crop growth period and are applied in simulation models to reduce crop growth rates. A critical, species and cultivar dependent threshold (homax ) to adverse effects is assumed. Up to this threshold it is assumed that a plant does not experience any effects that reduce crop production, whereas above this threshold, plant growth is adversely affected. The parameterization of Eq. (4) varies with crop type, and cultivar, reflecting the differences regarding the sensitivity for salinity. The presented reduction functions for drought and salinity are piecewise defined linear functions, which summarize very complex processes that depend on several production factors. This is in agreement with a parsimonious approach at the crop level. However, as computational resources improved, the concept was applied to different soil layers and horizons. In their review, Hopmans and Bristow (2002) recapitulated that these empirical approaches work well for non-stressed and uniform soil conditions, but that a different approach is required for non-uniform conditions, and when only part of the root zone experiences stress. Their point was supported by Darrah et al. (2006), Luster et al. (2009), Lamhamedi et al. (2006), Srayeddin and Doussan (2009), Moradi et al. (2009), and Kuhlmann et al. (2012). The Feddes- and Maas–Hoffman reduction functions are empirical functions that were determined for specific experimental conditions, such as soil texture, soil hydraulic functions, climate (evaporative demand, radiation and precipitation). Most of these conditions are known to influence the tolerance to stress (Shannon

2.3. Interaction of stresses

2.3.1. Salinity and drought In arid and semi-arid regions in the world, but also during prolonged dry periods in humid areas with saline groundwater, plants may experience a combination of drought and salinity stress. The need to combine both water and osmotic stress in a single reduction function leads to the question how these stresses interact. The same holds for the combination of water logging (flooding) and salinity stress, which is a common situation for coastal salt marshes (Colmer and Flowers, 2008). In literature different concepts are found that can often be categorised as the “additive” or the “multiplicative” concept, which is familiar from the statistical analysis of agronomic experiments. The additive concept was initially formulated by Nimah and Hanks (1973). In the root water extraction term the total head is calculated by adding the soil water pressure head and the osmotic head (Eq. (5)): S = ˛(h + ho )Sp

(5)

Cardon and Letey (1992) tested this concept and found the model insensitive to salinity, unless extra parameters are introduced to account for the plants’ differential sensitivity to pressure and osmotic head, i.e. the effect of one unit of ho differs from the effect of one unit of h, which is due to the non-linear changes of the soil moisture content and the hydraulic conductivity with pressure head h. The extra parameters suggested include values of pressure and osmotic heads for which ˛ is reduced by 50%. These coefficients have to be determined experimentally and are considered to be plant-, soil- and climate-specific (Homaee, 1999). Despite the lack of a physical basis, several authors found that the additive concept describes their data well (Wadleigh, 1946; Meiri and

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Shalhevet, 1973; Childs and Hanks, 1975; du Plessis, 1985; Bresler and Hoffman, 1986). For the multiplicative concept (Eq. (6)) reduction factors for drought and salinity stress are multiplied instead of added, according to: S = ˛w (h)˛s (ho )Sp

(6)

This concept was first proposed by Baule in 1918 to quantify yield response to multiple nutrients (Paris, 1992). The concept does not account for the interdependency of water and osmotic stressors and, like the additive concept, lacks a physical basis (Homaee, 1999). It implies that a certain reduction factor for salinity stress and for drought stress would have the same reduction in yield as vice versa. Experiments show that this is not realistic (Sepaskhah and Boersma, 1979; Shalhevet and Hsiao, 1986). Nevertheless, this concept is widely adopted in RWU modelling (Van Genuchten, 1987; Homaee et al., 2002; Shani et al., 2007). 2.3.2. Indirect effects A major production factor is the availability of macro- and trace nutrients, which is partly soil type and partly soil management (fertilization) dependent. It has been observed that the presence of high concentrations of salts will affect chemical conditions and consequently the bio-availability of some nutrients. Salinity or the occurrence of high concentrations of salts usually concerns alkali and earth alkali cations and anions such as chloride and (bi)carbonates. High salinity can lead to nutrient disorders by affecting the nutrient availability: by competitive uptake, by different transport or partitioning within the plant and/or by increased plant requirement of a nutrient due to decreased activity (Grattan and Grieve, 1992). Nutrient interactions can occur at the root surface or within the plant. Most commonly these interactions involve chemical bonding under influence of pH and organic matter, and competition among ions with similar properties (antagonism) (Fageria, 2001). Salinity and mineral–nutrient interaction experiments contain two potential limiting factors: salt and nutrients, their interaction can affect the salt-tolerance in three different ways as described by Bernstein et al. (1974): no effect on salt tolerance, increased salt-tolerance, decreased salt tolerance. A meta-analysis of salinity–nutrient interaction experiments is not straight forward as experimental conditions often differ. For example: plants are grown in solutions, while in other experiments sand or soils are used to grow plants as pointed out by Grattan and Grieve (1999). It is known that root development differs for plants grown in soil or in solution. Besides, also bio-availability of especially P will differ as their availability is mainly determined by the solid phase. Another difference among experiments is the salt composition used in the soil solution. In some experiments a single salt solution is used, while in other different salts are combined. According to Grattan and Grieve (1999) most studies used only NaCl as the salinizing agent. Below we will present a few examples of nutrient and salinity interactions. For a more complete overview of nutrient and salinity interactions the reader is referred to George et al. (2012) and Grattan and Grieve (1999). Potassium is an important plant nutrient that contributes to the low osmotic potential in the stele of the roots (Marschner, 1995). The uptake of potassium is hampered by a small K/Na ratio as an antagonist effect occurs with sodium due to the ions having similar physicochemical properties and they compete for uptake sites at the plasma membrane (Shabala and Cuin, 2007). As a consequence of the enhanced uptake of Na+ compared to K+ in the presence of salinity the plasma membrane depolarizes (Shabala and Cuin, 2007), which hampers the uptake of K+ . Sodium mainly accumulates in the leaf blades, after being transported with the transpiration stream. Symptoms of excess sodium are necrotic

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areas on the tips, margins or in the interveinal areas. Salt tolerance is ameliorated through salt exclusion or specific traits for salt inclusion while preventing water deficit. If salt concentrations and composition favours calcium carbonate precipitation, lime-induced chlorosis by iron deficiency (Schinas and Rowell, 1977), and a reduced bio-availability of trace nutrients such as Mn, Zn at large pH-values may occur. Other components (Se and B) may become so mobile at high pH, so that toxicity effects are possible.

3. Crop modelling A concept, which helps analysing crop (and therefore halophyte) production, is that of different production situations and the classification of these situations in terms of their limiting production factors (Penning de Vries et al., 1989). In this analysis, a situation in which both nutrients and water are non-limiting is defined as the reference situation, of potential production, in which only light and temperature are assumed to determine production rates. Classic agronomic production situations, also amenable to experimentation, are those in which a single factor, either water or a nutrient is limiting, and a more complicated situation arises when nutrient and water stress occur within one growing period. Hence, a hierarchy of production systems can be defined starting from the potential production, to production systems limited by a single production factor, to systems in which several factors are limiting. In addition to yield limiting factors, there are yield reducing factors such as pests and diseases, or competitive effects of weeds, which can occur at any production level. Such a hierarchical analysis is also familiar from agronomy in those cases where crop yield is analysed in multifactor experiments, e.g. in fertilizer trials. The analysis of these experiments led to the formulation of so-called response functions (static multifactor yield prediction models) reviewed, e.g. by De Wit (1992) when analysing resource use efficiency. Response function analysis initially left out the effects of the soil itself. Introducing a fraction recovery, i.e. the relation between nutrient application and crop uptake allowed to some extent the explanation of variability between soils. Irrigation efficiency in terms of transpiration over irrigation application also functions as a black box model for the soil water balance. Response function analysis focused on the role of the soil when the relation between nutrient application and nutrient uptake was summarized in terms of the fraction recovered, and on terms of the soil water balance when discussing irrigation efficiency. With the introduction of dynamic simulation models, additional layers of explanation could be added, and the analysis of production situations now requires one crop growth model for the reference situation, which is then modified by modules describing the dynamics of production factors (water, nutrients), and their interaction. An example of one such modular modelling approach developed for use in rice is described by Penning de Vries et al. (1989). Modelling approaches are reviewed by, e.g. Bouman et al. (1996), and model suites for different production levels have been presented (e.g. SALTMED (Ragab et al., 2005), APSIM (Keating et al., 2003), DSSAT (Jones et al., 2003), STICS (Brisson et al., 2003)). These models differ in the complexity of the module calculating the reference level. In SALTMED the reference level is a constant, a maximum yield, in other models canopy photosynthesis is an explicitly defined function of meteorological data (radiation or light, and temperature), sometimes linear in a light use efficiency approach (DSSAT, STICS and APSIM), sometimes more complex, taking diffuse and direct radiation, and the daily course of radiation, into account (SUCROS, Bouman et al., 1996; CoupModel, Karlsberg et al., 2006). The model used in this paper is SWAP (Kroes et al., 2009), which contains a light-use efficiency based crop model, referred to

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Fig. 2. Salinity reduction functions for (a) Kochia scoparia (Salehi et al., 2009) and (b) Kochia scoparia (Kafi et al., 2010).

as “simple” and a leaf-photosynthesis based growth model in which also temperature plays an important role. In the following section we derive the salinity reduction equation similar to the Maas and Hoffman function from experimental data for the halophyte Kochia scoparia based on data presented by Salehi et al. (2009) and Kafi et al. (2010). In a next step we show the interaction of drought and salinity stress for two halophytic species: Crambe maritima and Salicornia dolichostachya. In the last part of this section growth reduction of three different halophytes (C. maritima, S. dolichostachya and K. scoparia) for a complete Dutch growing season was simulated with the SWAP model. We have formulated a crop growth model for production of halophytes at a reference level using global radiation as input based on the light interception approach as used, e.g. Lee et al. (2003), but in addition included a respiration term proportional to the standing biomass. The model was parameterized using data from Kafi et al. (2010), and Salehi et al. (2009) for K. scoparia. The model requires 4 empirical parameters (light use efficiency, light extinction coefficient (expressed per unit leaf biomass or unit leaf area), fraction partitioned to the leaves (assumed constant over time), and a total biomass maintenance respiration coefficient). For the experiment by Salehi et al. (2009) in which additionally leaf area index was measured, specific leaf area is an additional parameter. Crop development is defined by sowing and harvesting date. Assuming water itself is non-limiting we selected a salt treatment at which production in the experiment was maximal. For K. scoparia this salt concentration is 7 dS/m (Salehi et al., 2009) or 5 dS/m (Kafi et al., 2010). The model versions were implemented in Excel. The required weather data, were retrieved from the “global summary of the day” (NOAA, 2012), and global radiation estimated using FAO standard equations (Allen et al., 1998). Parameters were estimated using the Excel solver. Goodness of fit criterion was a weighed sum of squares, with the variance between treatments as the weighing factor. The most important objective was to estimate the salinity reduction function from the data. The reduction factor ˛ and the parameter vector (light use efficiency, light extinction coefficient, fraction partitioned to leaves, biomass maintenance respiration coefficient) for the optimal production situation were estimated from the experimental data. To test the sensitivity of this result, we also compared simultaneously fitting the parameters on all datasets to first fitting the optimal treatment, and subsequently fitting the reduction parameters. The central result is shown in Fig. 2a and b, and shows the reduction in production as a function of salinity based on a fit on all state variables for the two experiments. Results also showed that simultaneously fitting all parameters improves the overall fit slightly, suggesting that the full dataset is more informative than the partial sets (cf. Table 1). Assuming that not only dry matter conversion is affected by salinity, but also

that respiration changes with salinity improved the fit for the more extensive dataset (Salehi et al., 2009) but did not clearly improve the fit for the smaller dataset (Kafi et al., 2010). This was to be expected, first because estimates of respiration parameters are sensitive to the measurements near final harvest, i.e. cumulative biomass over the growing season, as then the effect of respiration is largest, and secondly because the data from Salehi et al. (2009) are described by a logistic function which in itself suggests a strong respiration component. This improvement could not be achieved by any of the other parameters. Finally, it is interesting to note that fitting every dataset individually resulted in the best overall fit, but resulted in a variation in individual parameters that are not clearly linked to salinity (results not shown). This suggests that the approach using a reduction factor may be very helpful in making the most of situations with limited data. In a second step we take the crop modelling exercise one theoretical step further in the hierarchy of production situations by introducing a combination of limiting production factors: salinity and drought. From data presented by De Vos et al. (2010) and Katschnig et al. (in press) we first obtain the response functions for a production situation with different salt levels for C. maritima (De Vos et al., 2010) and S. dolichostachya (Katschnig et al., in press), as depicted in Fig. 3. Solution concentrations in mM NaCl were converted to electrical conductivities according to the FAO guideline: 10 mmolc /L NaCl is approximately 1 dS/m (Rhoades et al., 1992). For C. maritima, the reduction function shows a single threshold. Up to this threshold the halophyte performs optimal (first production situation). For salt concentrations higher than the threshold (EC > 9.8 dS/m), salinity becomes the reducing factor and with increasing salt concentrations the production continues to reduce. For S. dolichostachya, a single optimum value (EC = 26.1 dS/m) is observed (Fig. 3b). For any other value production is reduced. Unlike for K. scoparia and C. maritima a range of salinities with constant high production was not found for S. dolichostachya. For the production factor drought stress the same response function (Feddes function) is assumed for both halophytes. No effects of water-logging are taken into account. Optimal production is possible up to pressure heads of −4 m. It was assumed that no growth is possible for pressure heads beyond −100 m. The stresses interact according to the multiplicative approach. For both crops the interaction of stresses is depicted in Fig. 4 by means of surface contours.2

2 In Fig. 4a, for the relative transpiration contour of 1, the line is distorted by slight numerical instabilities that are meaningless. Also in Fig. 4b, such minor instabilities are visible.

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Table 1 Quality of fit for different parameter estimation procedures and the datasets available, showing a clear difference in fit between the reduction function approach and the all parameters approach. Shown is the sum of squares of fitting weighted so that variates with largest variances have smallest weight. Kafi et al. (2010), their fig. 1 Parameters for optimal salinity and reduction factor estimated simultaneously using all subsets Parameters for optimal salinity and reduction factor estimated one subset at a time Parameters for optimal salinity, reduction factor and additional respiration parameter estimated simultaneously using all subsets Parameters for optimal salinity, reduction factor and additional respiration parameter estimated one subset at a time All parameters estimated for each single subset

Kafi et al. (2010), their fig. 3

Salehi et al. (2009)

40 42 41

12 15 12

217 320 189

34

15

252

16

8

102

Fig. 3. (a) Maas and Hoffman reduction function for Crambe maritima (De Vos et al., 2010) with a threshold of 9.8 dS/m. (b) Maas and Hoffman reduction function for Salicornia dolichostachya, with a threshold EC of 26.1 dS/m.

Fig. 4. Contours of relative transpiration for Crambe maritima (a) and Salicornia dolichostachya (b) as a function of pressure- and osmotic head. Osmotic heads were derived using both Eqs. (8) and (9) in De Jong van Lier et al. (2009).

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Table 2 Maas and Hoffman parameters for the halophytes. Species

Threshold (dS/m)

Slope (%)

Crambe maritima (De Vos et al., 2010) Kochia scoparia (Salehi et al., 2009) Kochia scoparia (Kafi et al., 2010)

9.8 7 15

8.6 or 4.9 0.7 4.8

The contour area shows different optimum conditions for growth for both halophytes. For C. maritima optimum growth occurs for ho < −64 m and h < −4 m, while for S. dolichostachya optimum levels of ho are difficult to identify on the basis of (Fig. 4b). This figure shows that the relative transpiration is higher than 0.9 for osmotic heads in between −188 m and −146 m and for pressure heads larger than −4 m. For C. maritima, no transpiration is observed for ho > −135 m. For Salicornia the limiting osmotic head for growth is found beyond EC > 45 dS/m. A combination of stresses constrains the conditions for optimal crop production. As each species has its own specific reduction function for both drought and salinity stress, the area with optimal conditions is quite sensitive to the species considered. In the last step, we consider changing abiotic conditions during a growing season. For most field situations, environmental conditions are transient. Root zone salinity is subject to these changes and will vary accordingly. Modelling can help to gain more insight in the consequences of these transient conditions and focus on most relevant experimental conditions. Modelling transient environmental conditions in combination with crop development requires the use of a solute and water transport routine in combination with a crop model to simulate plant responses to these changing conditions. SWAP is such a model that simulates transport of water and solutes in the vadose zone in interaction with vegetation development (Kroes et al., 2009). A field situation with combined drought and salinity stress for different halophytes is simulated with SWAP. The routine calculating the reference production level for each halophyte integrates intercepted radiation over the canopy depth, and includes temperature dependence. To show the difference in salinity tolerance among the halophytes during the growing season, other parameters in the crop routine are assumed to be constant. Based on the data presented by De Vos et al. (2010), and the analyses presented above on the Kochia species, threshold and slope values were determined and are presented in Table 2. For S. dolichostachya the measurements as presented by Katschnig et al. (in press) were used. Depending on the weight given to the last two datapoints for C. maritima (salinities: 200 and 300 mM NaCl) the slope varies between 4.9% and 8.6%. For all halophytes the simple crop routine was used. We used an adapted version of SWAP 3.2. (www.swap.alterra.nl) with the salt reduction function as tabulated input, allowing for growth reduction at low salinities as observed for S. dolichostachya. In the original SWAP 3.2 version, only a threshold and a slope can be specified for the Maas and Hoffman function. Outside the range of measurements, in this case EC < 4.9 dS/m and EC > 42.4 dS/m, the relative production is kept at its last known value. Simulations were done for an extremely dry Dutch year (1976) for a loamy sand soil (B3 Dutch Staring series (Wösten et al., 2001)). Meteorological data on a daily basis were obtained from the Royal Dutch Meteorological Institute (KNMI) for the Vlissingen meteorological station. Salts were added by surface irrigation. Irrigation dates were defined for C. maritima (slope 1), which was irrigated with fresh water whenever the ratio of actual transpiration and potential transpiration reached a threshold (0.8). These irrigation dates were then applied to the other halophytes with irrigation water of different EC levels (0, 8, 13, 18, 23, 28, 33, 38 and 43 dS/m, results only partly shown here). We considered solutes

without specific effects, i.e. mainly NaCl (considered typical for Dutch coastal situations). For drought stress the same response function was used as in the previous exercise. In Fig. 5 the simulations for the halophytes for four salinity levels of irrigation water are presented. For good quality irrigation water (EC = 0 dS/m) S. dolichostachya shows a considerable reduction in growth from the beginning of the growing season and onwards. For the other halophytes reduced growth begins in June, which is due to drought stress and which is similar for all these halophytes. In case of irrigation water of 18 dS/m differences in crop yield start to occur, which become more evident for irrigation water with higher salt concentrations. S. dolichostachya shows a gradual increase in relative transpiration from July onwards, while the reduction in relative transpiration increases for the Crambe species. The sequence of reduction in relative transpiration changes slightly with increased concentration of the irrigation water. As a general rule, the Maas and Hoffman threshold value determines the order for irrigation water with low EC. For irrigation water with concentrations exceeding the threshold, the slope mainly determines the reduction in transpiration. Based on the salinity reduction functions one would expect that for irrigation water of 43 dS/m C. maritima and K. scoparia (Kafi et al., 2010) would reveal negligible growth, even without drought stress (C. maritima-slope 8.6% negligible growth for EC > 21 dS/m, Crambe maritime-slope 4.9% for EC > 30 dS/m and K. scoparia (Kafi et al., 2010) for EC > 36 dS/m). The SWAP simulations with irrigation water concentrations of 43 dS/m show a relative transpiration for C. maritima of 0.67 (slope = 8.6%), 0.75 (slope = 4.9%) and for K. scoparia 0.80 (Kafi et al., 2010). These relative transpiration values are a combination of both salinity and drought stress. Regular precipitation, in this case 42.95 cm compared to a total irrigation gift of 22 cm, has a favourable effect on crop production. Precipitation enables plant growth, by decreasing drought and salinity stress, and appears to be an appreciable factor for the simulated conditions. The earlier, though insightful, illustration using Fig. 4 is therefore not adequate to predict plant growth and crop production.

4. Environmental aspects of salinity Environmental aspects of salinity cover a broad range of topics given that salinity may be used to refer to an excess of easily soluble salts or to a poor balance between common cations and anions in soil and water. Major macro components involved in salinity are the alkali and earth-alkali cations (of sodium, potassium, calcium, magnesium) and the anions chloride, carbonates/bicarbonates, and sulphate. These compounds derive from, e.g. soil weathering in situ, deposition of airborne salt, saline water from sea water intrusion, and connate water, which is water present at the moment of soil/sediment deposition such as during the Holocene transgressions (De Louw et al., 2011). In the consideration of sustainability of agriculture under saline conditions, we first consider salts that originate from using substandard quality water for irrigation. This may lead to ‘secondary salinity’ because in essence, water with salts are added to the soil, the water is lost through evapotranspiration, and the salts remain, because these are not needed by plants in the large quantities in which they become available (K, Ca, and Mg). Thus primary production is equivalent with ‘up-concentrating’ salts in the water that is not used for transpiration. This awareness has resulted in the recognition that salts that are introduced into the root zone have to be removed, leached, unless they are taken up in equal measure in the course of biomass growth (e.g. nutrient elements such as N, P, K). If removal does not occur, salt levels will grow indefinitely, and crop failure is just a matter of time. The concept of the leaching requirement (LR) has

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Fig. 5. Relative transpiration for four different halophytes as a function of time. Transpiration is simulated for a complete growing season with irrigation water with different levels of salinity.

been developed to identify how much irrigation water should be applied (Di is the quantity of irrigation water in mm/year) with a concentration of salts equal to Ci , if the long term salinity level in soil may at most become equal to Cs : LR =

DL C = i Di Cs

(7)

where DL is the amount of leached water. In Eq. (7), salinity can be given as a concentration (e.g. mol/L) or as a value of the electrical conductivity (e.g. dS/m). With this very useful leaching requirement, if the crop’s salt tolerance is known to be Cs , it is possible to estimate how much water should be added in excess of the plant water use for transpiration to remove sufficient salt (Richards, 1954). Hence, use of irrigation water of designated quality (Ci ) is only sustainable if enough water is available. The consumed water, equal to the annual evapotranspiration (ET) is equal to Di − DL , if no runoff occurs. The major take home message is that leaching the quantity DL is essential. Usually, Eq. (7) provides a low estimation of the leaching requirement, because leaching occurs only if a soil is at field capacity (Hadas et al., 1973). If a soil is drier, it will lose little water and salts by drainage, whereas if it is wetter, drainage will remove salts including nutrients that are needed by plants. In a natural sequence drainage occurs shortly after rainfall and irrigation, after which the soil will attain the field capacity and gradually dry out further. As the soil progressively dries, salinities proportionally increase according to a simple mass balance: CFC FC = Ct t

(8)

where subscript FC refers to field capacity, t to any other moment in time, and  is the volumetric water content. Most of the time,  is smaller than the relatively large value at field capacity. Since the soil should be drained and leached of salts, sufficient rainwater is needed, where necessary supplemented with irrigation water with a concentration of salts smaller than the threshold for the crop to be grown. In this respect the use of fresh water, brackish or waste water is essentially similar, only the salt concentrations differ. Drainage requires that the groundwater level is deep enough

below the soil surface to allow drainage, or that a well-functioning drainage system is available. Obviously, if saline agriculture is practised, downstream pollution of groundwater and rivers by saline drainage is a factor of concern. Whether a soil can be adequately drained depends on the soil type. Sandy soils drain faster than loamy soils, and both drain better than clayey soils. However, transitions in drainage rates are gradual as many soils contain sand, loam and clay in varying proportions. A good first impression is gained from tabulated values of the hydraulic conductivity (Wösten et al., 2001; Misasny and McBratney, 2002). In addition to the average drainage capacity, the uniformity of drainage is also an issue for plants that do not allow for salts in their root zone. Usually, sandy soils show more uniform drainage than other soils. If it is not possible to leach salts, as a drainage infrastructure is not available and/or the groundwater level is too shallow, long-term irrigated agriculture is impossible. If groundwater is too shallow, water will move upward by capillary forces (e.g. Eagleson, 1978; Vervoort and van der Zee, 2008) also transporting salt upward. This is the so-called ‘primary salinization’, where salts are transported by capillary flow of brackish water from groundwater into the root zone. This upward movement is more pronounced if soils are clayey or loamy and groundwater levels are more than say 30 cm below the root zone. For sandy soils it is generally smaller. If groundwater levels are shallower, or if waterlogging occurs, agriculture is probably not feasible if groundwater is brackish or saline. In Hungary, where the groundwater supply of salts is a major problem, it has been advised for the above reason to keep groundwater levels at 2.5–3 m below soil surface (Varrallyay, 1989; Szabolcs, 1989). For groundwater levels at these depths, only few soil types exhibit significant capillary upflow and will be susceptible to salinization from below. In modelling studies it has also been shown that a distance of 2.5–3 m between groundwater level and root zone indeed limits capillary upward flow (Shouse et al., 2011; Shah et al., 2011). In addition to the osmotic and indirect nutrient availability resulting from too much salt, an imbalanced soil solution composition may also cause problems, even if there is no excess. Whereas

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generally divalent cations predominate in soils in humid and subhumid areas, in sodic (or alkali) soils the ratio of monovalent to divalent cations is distorted as in particular sodium (Na) becomes quantitatively more important. This leads to adverse effects as the cation exchange complex of soils, that buffers the soil solution’s composition by adsorption/desorption of cations, is loaded with Na+ at the expense of the usually abundant Ca2+ cations. Due to its lesser charge, the monovalent sodium is less able to counter the negative charge of several common (clay) minerals that have swelling/shrinking behaviour. Consequently, soils containing such swelling minerals may degrade structurally. For the same reasons, sodium is less able to facilitate bonding between organic matter (a major soil cementing agent) and the solid phase. Accordingly organic matter is dissolved by peptization and removed by leaching and runoff. Sodicity (excessive Na) is often due to salinity (Bolt, 1982; Bresler et al., 1982), and therefore classified as a typical salinity problem (Rengasamy, 2006). Also if anions such as bicarbonate dominate, a typical salinity problem arises, where the soil pH may increase to very high values. Such alkaline soils form an aggressive environment for plants and soil life in general. If either groundwater or irrigation water contain relatively much sodium compared with divalent cations as Ca and Mg (often expressed as the Sodium Adsorption Ratio, SAR, see Richards (1954) for numerical criteria), the hazard of sodicity should be managed. Soil structure deterioration may occur if the exchangeable sodium percentage (ESP, amount of sodium over amounts of calcium and magnesium adsorbed at the cation exchange complex, times 100%) exceeds about 15%. If the salinity of a soil suddenly decreases and if this soil contains swelling clays (e.g. montmorillonite, illite, but not kaolinite), this combination results in a soil with a very low hydraulic conductivity (Bresler et al., 1982; Shainberg and Singer, 1990; Van der Zee et al., 2010). Hence, sodicity problems are predominantly expected in loamy and clayey soils, and if a distinct dry season (accumulation of salts and Na) is followed by a distinct wet season (salt leaching, decreasing salinity) or by irrigation with good quality water. The problems with soil sodicity are also of importance if waste water, e.g. domestic waste water, is used for irrigation, as is now the case in large areas of Australia (e.g. the Murray Darling Basin). Commonly, Na concentrations of domestic waste water are large, due to salt in our food. For this reason in addition to a salinization risk, sodicity problems are expected to occur in soils with swelling/shrinking clay mineralogy. In contrast to salt accumulation in the soil solution, sodicity changes the composition of the cations adsorbed by the cation exchange complex. This implies that changes are slow. However, as a threshold is passed, structural deterioration may occur rather suddenly and this has to be avoided. Therefore, a proper risk assessment, often with models, is essential. If waste water is used for irrigation, the biological oxygen demand of water (BOD) is often a measure of the suspended concentration of organic matter. Since organic matter may adsorb Na, in particularly also in areas with sodic soils, the effective input of Na may be much larger than is expected from only measuring dissolved Na in water. For the sodicity risk, one has to be aware of this. Likewise, water has to be screened for the anion composition. As soon as carbonates and bicarbonates become a prominent fraction of the anionic composition, the risk of alkalinity has to be taken into account, in view of induced nutrient deficiencies and toxicity. For sodic soils, leaching is obviously difficult and therefore also remediation, by removing sodium and replacing it with calcium, may be cumbersome. Though pot experiments may show promising effects (Singh and Bajwa, 1991), in general much labour, high costs, and large quantities of, e.g. gypsum are required that were often found prohibitive under field conditions (Szabolcs, 1989).

Nevertheless, several more or less complicated remediation strategies have been developed, based on a thorough understanding of the underlying chemical processes (Bolt and Bruggenwert, 1976). These are, however, beyond the scope of this paper. In practice, however, the success of remediating sodic soils has been modest at best. Also for alkaline soils, that may have both a high pH and calcite hard pans that impede water infiltration and drainage, remediation may be a difficult task. For the latter soils, addition of acids might be a fast desktop solution, but the amounts of acid needed are quite significant. Slower approaches that use acid/ligand exudation by crop root systems may be a more promising way, provided the crop/vegetation can establish itself. 5. Conclusion In this paper, we presented an ecohydrological perspective on modelling issues that is focussed on halophyte crop modelling and saline agriculture (environmental) sustainability. This perspective is not intended to be complete, but rather aimed to identify where our ecohydrological discipline meets that of the biologists, plant physiologists, and agronomists. At these disciplinary interfaces lie opportunities for interdisciplinary advances, and in ecohydrology, such advances are appreciated as being essential. It appears that water and crop modelling often has to make use of existing and valuable empirical analyses, such as the often quoted work by Maas and Hoffman (1977). We emphasize, though, that it is difficult to extrapolate such an empirical basis to other conditions (of soil, climate, salt composition, and crop), even though it is nowadays the core of crop–salinity interactions. Hence, for halophyte crop production, for which the empirical base that is common to ecohydrology and soil sciences is rather limited at this moment, a large experimental effort is needed to enable modelling. Specifically for halophytes, the parameters of the functions ˛ may sensitively influence the response for water and salt stress. In a broader sense, also the soil hydraulic parameters, such as the hydraulic conductivity may affect the outcome significantly, also because it can vary over orders of magnitude. The above analysis shows that experimental data as such may not be the bottleneck, and that available data allow to derive reduction functions which lump the effect of stresses and may also suggest model modifications, such as including a salinity dependent respiration factor. We believe that a systematic production level perspective may assist in further developing databases that are sufficiently complete to enable model-based extrapolation. Whereas in many situations where halophyte crops are grown, saline water may be readily available, this may not be the case in general. If water becomes more limiting, drought and associated salt stress may become an issue. Despite decades of research in this field for non-halophyte crops, it appears that there are different ways in which drought and salt stress can be combined, and it is not clear which way is more appropriate. If salt water is freely available, excess salt loads need to be removed regularly, according to the concept of leaching requirement. This implies that also halophyte crop production needs to deal properly with our groundwater and surface water reserves that receive the leached salts. Moreover, under such saline agriculture regimes, perhaps not drought and salinity, but water logging and salinity need to be combined in management. The adaptation of plants to anaerobic conditions may be equally important as that to drought, but are less extensively investigated in our disciplines. Given that – to our knowledge – relatively little has yet been done regarding halophyte crop modelling, we feel that modelling requires a sound experimental basis (ground truth), that modelling may help profoundly in optimizing experiments, obtain system understanding, and give ‘guidelines for guessing’ if environmental or management changes need to be assessed. To model halophytes,

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different new functional responses need to be taken into account in modelling, with regard to salinity, drought and anaerobicity, and their interactions, that data for non-saline agriculture cannot provide. Currently these functional responses are captured in lumped empirical functions. However, extrapolation to other conditions, whether that be climates, soils or plant species requires more explicit physiological parameters to be taken into account in crop modelling, such as root/shoot partitioning, senescence and respiration response. For conventional glycophytic crops many of the required data are available, but for halophytic crops these data and research are still required to allow for more detail in the simulations. To do this effectively implies an interdisciplinary interaction, similar as is needed for non-saline agriculture. Likewise, agriculture in general, i.e. also saline agriculture, needs to make the assessment whether its management of scarce fresh water as well as of abundant saline water is sustainable. As we emphasized in this paper, not only water and salts are an issue. With regard to saline agriculture, it is important to be aware whether its effects are reversible or not. If for instance, sodicity develops in conditions that favour soil structure deterioration, the soil may lose its production capabilities for a long time. Furthermore, saline and sodic soils may have secondary effects on crop yield, such as induced nutrient deficiencies and chemical toxicity (for crop or its consumer). For these effects both the long term sustainability and the issue of food security versus food safety should become central to the selection of the most desirable production system. Acknowledgements We gratefully acknowledge Dr. J.C. van Dam (Wageningen University) who adjusted the SWAP model to account for the salt tolerance function of Fig. 3b for S. dolichostachya. This research is part of the Dutch Knowledge for Climate program “Climate Proof Fresh Water Supply” and the IPOP program “Kust en Zee”, Wageningen University. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements – FAO Irrigation and Drainage Paper 56. FAO – Food and Agriculture Organization of the United Nations, Rome. Baule, B., 1918. Zu Mitscherlichs gesetz der physiologischen beziehungen. Land wirschaftliche Jahrbücher 57, 363–385. Bernstein, L., Francois, L.E., Clark, R.A., 1974. Interactive effects of salinity and fertility on yields of grains and vegetables. Agronomy Journal 66 (3), 412–421. Bolt, G.H., 1982. Soil Chemistry B. Physico-Chemical Models. Elsevier, Amsterdam. Bolt, G.H., Bruggenwert, M.G.M., 1976. Soil Chemistry A – Basic Elements. Elsevier, Amsterdam. Bouman, B.A.M., van Keulen, H., Van Laar, H.H., Rabbinge, R., 1996. The ‘School of de Wit’ crop growth simulation models: a pedigree and historical overview. Agricultural Systems 52 (2–3), 171–198. Breckle, S., 2009. Is sustainable agriculture with seawater irrigation realistic? In: Ashraf, M., Ozturk, M., Athar, H.R., Lieth, H., Kratochwil, A. (Eds.), Salinity and Water Stress. Springer, Netherlands, pp. 187–196. Bresler, E., Hoffman, G.J., 1986. Irrigation management for soil salinity control: theories and tests. Soil Science Society of America Journal 50, 1552–1560. Bresler, E., MacNeal, B.L., Carter, D.L., 1982. Saline and Sodic Soils: Principles—Dynamics—Modelling. Springer Verlag, New York. Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussiere, F., Cabidoche, Y.M., Cellier, P., Debaeke, P., Gaudillere, J.P., Henault, C., Maraux, F., Seguin, B., Sinoquet, H., 2003. An overview of the crop model STICS. European Journal of Agronomy 18 (3–4), 309–332. Cardon, G.E., Letey, J., 1992. Plant water uptake terms evaluated for soil water and solute movement models. Soil Science Society of America Journal 32, 1876–1880. Childs, S.W., Hanks, R.J., 1975. Model of soil salinity effects on crop growth. Soil Science Society of America Journal 39, 617–622. Colmer, T.D., Flowers, T.J., 2008. Flooding tolerance in halophytes. New Phytologist 179, 964–973. Darrah, P.R., Jones, D.L., Kirk, G.J.D., Roose, T., 2006. Modelling the rhizosphere: a review of methods for ‘upscaling’ to the whole plant scale. European Journal of Soil Science 57 (1), 13–25. De Jong van Lier, Q., van Dam, J.C., Metselaar, K., 2009. Root water extraction under combined water and osmotic stress. Soil Science Society of America Journal 73 (3), 862–875.

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