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DIVISION S-6 - SOIL & WATER MANAGEMENT & CONSERVATION

Field Studies of Crop Response to Water and Salt Stress

^a Department of Soil and Water Sciences, Faculty of Agricultural, Food and Environmental Sciences, POB 12, Rehovot 76100, Israel
^b Department of Plants, Soils, and Biometeorology, Utah State University, Logan, UT 84322-4820

* Corresponding author (shuri{at}agri.huji.ac.il)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Studies of crop response to water and salt stress vary either salinity with a high leaching fraction or irrigation in the absence of salinity to isolate and quantify the effects of the two types of stress. Under deficit irrigation with saline water, a water conserving practice, the crop experiences simultaneous matric and osmotic stress, and it is not known if experiments designed to isolate stress effects may be used to predict crop response to simultaneous stresses. Thus, a study was conducted wherein yields were determined under varying levels of salinity and irrigation. Corn (Zea mays L.) and melon (Cucumis melo L.) were grown at the Arava Research and Development Farm in Yotvata, Israel, and alfalfa (Medicago sativa L.) at the Utah Power & Light Research Farm in Huntington, UT. Corn and melon plots were drip irrigated at six ratios of potential evapotranspiration ranging from 0.2 to1.7 in combination with four salinity levels. Alfalfa was irrigated with water of 0.2 and 4.0 dS m^-1 from a line-source sprinkler. For all three crops, the salinity treatments consisted of a control treatment with a salinity level less than published salt-tolerance thresholds. Interactive effects of salinity and water stress were not observed in these field experiments. At low irrigation levels (70% of potential evaporation), yields were unaffected by the salinity level. At the higher irrigation levels, the salinity level caused significant differences in yield. Yield data were fit to piecewise linear models that emphasized the limiting nature of the effects of salt and water stress.

Abbreviations: DAP, days after planting • EC, electrical conductivity

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
DEFICIT IRRIGATION is practiced in many arid areas of the world, and increased demand on water supplies worldwide suggests the practice must increase. Moreover, as competition for limited water resources increases, it is reasonable to assume that agriculture will have to make do with waters of poor quality. One challenge of the future will be to maintain or even increase crop production with less water that often may be of poor quality.

Quantitative understanding of crop production under deficit irrigation with saline water is generally based on three assumptions. First, an increase in salinity, above the crop tolerance level, will decrease yield (Maas and Hoffman, 1977; Letey et al., 1985; Letey and Dinar, 1986; Bresler, 1987; Maas, 1990); second, biomass production is linearly related to transpiration (deWit, 1958; Childs and Hanks, 1975; Letey and Dinar, 1986; Bresler, 1987; Shani et al., 2001); and third, the effects of salt and water stress on yields are additive (Nimah and Hanks, 1973; Letey et al., 1985; Letey and Dinar, 1986; Bresler, 1987; Cardon and Letey, 1994; Pang and Letey, 1998). The validity of the first two assumptions is well established. The linear dependence of relative dry matter production (Y_actual/Y_potential) on relative transpiration (T_actual/T_potential) under conditions of water deficit has been validated for variety of climates and crops (deWit, 1958; Childs and Hanks, 1975; Letey and Dinar, 1986; Shani et al., 2001). Under conditions of salt stress (Bresler and Hoffman, 1986; Bresler, 1987) and Na stress (Shani et al., 2001), relative yield and relative transpiration are linearly related.

The validity of the third assumption is less certain. Plants respond to drought by attempting to both decrease transpiration and increase water uptake. Deleterious effects of salinity on crop growth have been attributed to an osmotic effect or a specific-ion effect. Osmotic stress inhibits water uptake from the soil and requires the plant to use energy and carbohydrate in synthesizing organic solutes to adjust its internal osmotic potential (Läuchli and Epstein, 1990; Jacoby, 1994). To a lesser degree, plants may adjust their internal osmotic potential by accumulating some salt from the surrounding solution (Läuchli and Epstein, 1990). Yield loss results from reduced photosynthesis associated with closing stomata (Grill and Ziegler, 1998), from energy and carbohydrate use in osmoregulation, and from sequestered salt interfering with cell function (see e.g., Läuchli and Epstein, 1990). The specific-ion effect results from ion interference with a physiological process in the plant (see e.g., Läuchli and Epstein, 1990; Munns, 1993; Marschner, 1995). Because plants respond to drought induced by limited water or elevated salinity by a similar mechanism, the sum of the matric and osmotic components of the water potential has been used to estimate yield (Nimah and Hanks, 1973; van Genuchten, 1987; Cardon and Lety, 1994). However, the complex nature of the plant response to salt and water stress may result in a response that is not necessarily equal or additive when the two stress factors are imposed simultaneously.

Most studies of crop response varied either salinity with a high leaching fraction or varied irrigation in the absence of salt (Hoffman et al., 1983), but under deficit irrigation with saline water the crop experiences simultaneous matric and osmotic stress. Shalhevet (1994) reviewed five studies that examined the effect water and salt stress had on crop yield and found that the effects of the two appeared to be additive but not equal. The linear regression coefficient for yield as a function of water stress was about two to three times greater than the salt stress coefficient (Meiri, 1984). In a similar study, Bermuda grass [Cynodon dactylon (L.) Pers.] responded to leaching fraction, but not to soil salinity except in a sandy soil (Devitt, 1989). Shalhevet and Hsiao (1986) studied the leaf extension rate at soil water potentials from -0.16 to -0.30 MPa. At equal potential, the effect of water stress was more than 10 times greater than salt stress when either the matric or osmotic potential was varied in the treatments. When the two potentials were varied simultaneously, the crop-response curves were similar. These studies used matric or osmotic potentials in the range common for irrigated agriculture (minimized stress) and have not clearly indicated whether the decrease in transpiration associated with salt stress causes the yield loss or results from it (Shalhevet, 1994). Russo and Bakker (1987) developed crop-water production functions for sweet corn (cv. Jubilee) and cotton (Gossypium hirsutum L.) under varying levels of salinity. They found that the amount of irrigation water could not compensate for salinity and that some yield loss occurred at the highest irrigation amounts. At low irrigation, the crop production functions for the salinity levels converged suggesting that the effects of salinity were dissimilar to water. Moreover, the effect of salinity appeared greater at irrigation near potential evaporation and diminished as irrigation decreased.

Russo and Bakker (1987) focused their analysis on irrigation amounts in excess of potential evaporation to address the issue of water compensating for salinity. Thus, they did not address the relative effects of salt and water stress over a range of irrigation appropriate to deficit irrigation. Because the relative effects of osmotic and matric stress are important to crop production under deficit irrigation, and a paucity of data exists on the subject, a study was conducted wherein yields were determined under varying levels of salinity and irrigation. Our objective was to quantify the relative effects of water and salt stress and to develop a crop production model that could be used for deficit irrigation with poor quality water. Salt and irrigation levels were varied simultaneously in a field study so that the relative effects of the two stresses could be examined and any interaction might be elucidated. The responses of corn, melon, and alfalfa to water and salt stress for a range of irrigation amounts sufficient to produce leaching through severe deficit are reported.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Corn (Jubilee) and melon (cv. Galia) were grown at the Arava Research and Development Farm in Yotvata, Israel, and a study of alfalfa (cv. Pioneer 532) response to salt and water stress was conducted at the Utah Power & Light Research Farm in Huntington, UT. The threshold values for salt tolerance are 1.8 dS m^-1 for corn (Maas and Hoffman, 1977), 2.0 dS m^-1 for alfalfa (Maas, 1990), and 2.5 dS m^-1 for melon (Bresler et al., 1982). Potential evaporation (evaporation from a class A pan, E₀) at the Arava site was approximately 3200 mm yr^-1, much greater than at the Huntington site, where E₀ was approximately 1300 mm yr^-1. The Arava site receives <25 mm of precipitation each year, and the Huntington site received approximately 120 mm yr^-1 (falling primarily during the growing season). The chemical and physical properties of the Arava sandy loam soil (Typic Torrifluvent) were given by Shani et al. (1987). The soil at the Huntington research site was Penoyer loam (coarse, silty, mixed, mesic, Typic Torriorthent). Wells used for irrigation in the area around the Arava plots indicted that the ground water surface was 120 m below the soil surface. The water table was approximately 12 m below the surface of the Huntington plots during the irrigation season (Hanks et al., 1984). Thus, irrigation and a small amount of precipitation at the Huntington site (Table 1) were the only sources of water in the two experiments.

At the Arava site, drippers were installed 0.33 m apart to promote one-dimensional water flow conditions. Irrigation water was applied through the drippers placed 10 cm below the soil surface. A polyethylene sheet was placed over the drippers to eliminate evaporation and covered with 10 cm of soil. Preliminary measurements of the temperature profile under the polyethylene sheet covered with 10 cm of soil and at 10 cm beneath the soil surface showed no differences. Plants were planted through holes in the cover. Irrigation levels I as a ratio of potential evapotranspiration E₀ were I = 0.2, 0.4, 0.7, 1.0, 1.3, and 1.7, and E₀ was estimated from a class A evaporation pan. The melon and corn plots were irrigated with waters to which CaCl₂ and NaCl (ratio of Na/Ca = 1.0 on an equivalent basis) were added to give electrical conductivity (EC) values of 3.0, 6.0, and 9.0 dS m^-1 and the corn plots were irrigated with waters that had EC values of 3.3, 6.3, 8.3, and 10.3 dS m^-1. For all three crops, the salinity treatments consisted of a control treatment with a salinity level less than the salt-tolerance threshold (1.2 dS m^-1) (Maas and Hoffman, 1977) and treatments that exceeded the threshold value. The soils were preleached with the control (1.2 dS m^-1) irrigation water so that all of the treatments started with the same conditions. The Na/Ca ratio was selected to maintain soil exchangeable Na concentrations within crop tolerance values preventing specific-ion toxicity. Alfalfa is a Na tolerant crop, corn is Na sensitive (Ayers and Westcot, 1985), and no data were available for melon.

The research design at the Huntington site consisted of two line-source sprinklers (Hanks et al., 1984) 36 m apart. One line supplied saline and the other nonsaline water to plots planted in strips 3 m wide and 72 m long. The strips were perpendicular to the sprinkler lines such that one-half of each strip was irrigated with saline water (EC 4 dS m^-1) and the other one-half with non-saline water (EC 0.2 dS m^-1). The line source-sprinkler experiment (design described by Hanks et al., 1976) duplicated each water and salt treatment on opposite sides of the line. The irrigation water contained the following ions (in mmol L^-1): Ca²⁺ = 12.6, Mg²⁺ = 9.6, Na⁺ = 10.7, SO^2-₄ = 21.7, and Cl^- = 10.7. There were three replicate alfalfa plots planted in 1987 and treatments were initiated in the middle of the 1987 season when the stand was established. Data from 1989 and 1991 are reported in this study. (Data from 1990 were omitted because the irrigation system failed mid growing season and two critical irrigation events were missed.) The radius of water application from each sprinkler line was arbitrarily partitioned into six increments, 3 m in length, for measurement of irrigation and plant yields. There was a 6-m buffer zone between the farthest reaches of the two line-source sprinklers. Irrigation was measured with a catch can placed at the center of each 3-m interval. A Bowen ratio system was used to estimate E₀ at the Huntington site. The system measures air temperature and relative humidity at 1 and 2 m, solar and net radiation, wind speed and direction at 3 m, soil heat flux, and precipitation every 5 s (Malek and Bingham, 1993). The irrigation schedule and E₀ for the two seasons are given in Table 1.

The melon fruit and canopy were harvested 57 days after planting (DAP), and the corn was harvested 62 DAP. Alfalfa was harvested by collecting plant material from a 0.9-m strip down the center of each plot three times each year. The plant material collected from each water level was weighed in the field and a subsample from one of the duplicate water levels was collected. The subsample was oven dried at 60°C to constant weight, and the ratio of dry/wet sample weight was used to correct the other irrigation-level duplicate.

A linear, piecewise regression model was used to describe the relationship between relative dry matter, Y_r = Y/Y_max (where Y_max is the greatest yield measured for all treatments) and I/E₀, or relative fruit yield and I/E₀, (Seginer, 1978; Shani et al., 2001),

(1)

where B is the intercept, Y_m is maximum yield for each salinity–irrigation treatment, with respect to salinity, and c denotes I/E₀ value at the intercept of the two line segments (where yield changed in response to limited irrigation). The piecewise regression curves parameters were estimated using the Ordinary Least Square model (Quantitative Micro Software, 1997). A Wald test (Kennedy, 1992; Quantitative Micro Software, 1997) was performed for the null hypotheses that Y_m of the various salinity treatments are equal. The same test was conducted on I_c and on B. A value of = 0.05 was used for statistical significance. The rational for selection of the piecewise linear model is given in the following section.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Letey and Dinar (1986) and Russo and Bakker (1987) used polynomials to quantify the effects of water and salt stress on yield. While facilitating a statistical analysis of treatment effects, generalization about crop production functions were limited by such an approach. Thus, we sought a formulation of the crop production function that might be generalized for management-level prediction of yield under conditions of deficit irrigation with poor quality water. Visual inspection of the melon and corn dry matter yield data suggested that at low irrigation salinity had no observable effect (Fig. 1a and 2a) . Thus, a test of the independence of the data was performed by linear regression of the data corresponding to I/E₀ < 0.7. The slopes and intercepts of the lines were compared and the models were found to be not statistically different (Table 2). A similar test was performed for the other crops with the same result. Because the slopes and intercepts of the individual lines were not different, the model for the control (1.2 dS m^-1) for each crop was used in creating a regression model for the higher salinity levels (Fig. 1b and 2b). At higher irrigation amount, visual inspection suggested that yields were limited by salinity and might be represented by a horizontal line yielding the piecewise model. Regression equations for all of the salinity levels were simultaneously solved and the resulting critical irrigation levels for each salinity level and other model components describing the crop response to irrigation and salinity are given in Table 3.

View larger version (20K): [in this window] [in a new window]   Fig. 1. The relative dry matter yield of melon as affected by irrigation and salinity treatments (a) with piecewise linear models fit to the irrigation response data for each salinity treatment and (b) with piecewise linear models fit to the irrigation response data for the lowest salinity treatment and irrigation response data for each salinity treatment above the critical irrigation level (the intercept of the two line segments).

View larger version (21K): [in this window] [in a new window]   Fig. 2. The relative dry matter yield of corn as affected by irrigation and salinity treatments (a) with piecewise linear models fit to the irrigation response data for each salinity treatment and (b) with piecewise linear models fit to the irrigation response data for the lowest salinity treatment and irrigation response data for each salinity treatment above the critical irrigation level (the intercept of the two line segments).

View this table: [in this window] [in a new window]   Table 2. Regression parameters for piecewise-linear models of melon, corn and alfalfa dry-matter yield response to irrigation and salinity treatments. A and B represent the slope and intercept, respectively, of the segment representing the yield response to irrigation, Ym is the greatest achievable yield for each irrigation-salinity treatment and Ymax is the maximum yield for all treatments. The letter in the group column represents a difference at the 0.95 probability level ( = 0.05) for the parameter in the preceding column.

View this table: [in this window] [in a new window]   Table 3. Regression parameters for piecewise-linear models (shown in the figures) of melon, corn and alfalfa dry-matter yield response to irrigation and salinity treatments. A and B represent the imposed slope and intercept, respectively, of the segment representing the yield response to irrigation, Ym is the greatest achievable yield for each irrigation-salinity treatment and Ymax is the maximum yield for all treatments. The letter in the group column represents a difference in the critical irrigation level (Ic) at the 0.95 probability level ( = 0.05).

View larger version (14K): [in this window] [in a new window]   Fig. 3. The relative dry matter yield of alfalfa as affected by irrigation and salinity treatments with piecewise linear models fit to the irrigation response data for the lowest salinity treatment below the critical irrigation level and irrigation response data for each salinity treatment above the critical irrigation level.

View larger version (15K): [in this window] [in a new window]   Fig. 4. The relative fruit yield of melon as affected by irrigation and salinity treatments with piecewise linear models fit to the irrigation response data for the lowest salinity treatment below the critical irrigation level and irrigation response data for each salinity treatment above the critical irrigation level.

For all crops, the maximum dry matter yields obtained at each salinity level were significantly different from the control (Tables 2 and 3), and the general trend was a decrease in yield with increasing salinity. In most cases, the four or five salinity treatments for melon and corn, respectively, produced three or four different values for the greatest achievable yields. The two salinity treatments imposed on the alfalfa resulted in two maximum yields. A decrease in maximum yield due to salinity was associated with decreased transpiration. The corn and melon dry matter was linearly related to transpiration and good agreement with a 1:1 relationship was found (Fig. 5) . Thus, elevated salinity reduces transpiration (or yield that in turn, reduces transpiration) and the result is that less irrigation is required to produce the greatest achievable yield for a given salinity level. Our results support the findings of Russo and Bakker (1987) in showing that increasing water cannot compensate for salt and reaffirm Liebeg's assertion that one growth factor cannot compensate for limitations of another.

View larger version (24K): [in this window] [in a new window]   Fig. 5. The relative dry matter yield of corn and melon as a function of relative transpiration for each salinity treatment. The dashed line represents the 95% confidence limits for the regression Y Y-1max = 0.134 + 0.805; r2 = 0.8833.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Our objective was to quantify the relative effects of simultaneously imposed water and salt stress on yield. Varying EC by nearly an order of magnitude produced no field-measurable differences in yield when the irrigation level was below that required to produce maximum yield. This result was consistent with other studies of crop yields under conditions of matric and salt stress where researchers reported similar yields for crops irrigated with saline and non-saline waters when the leaching fraction was decreased (Hoffman et al., 1983; Devitt, 1989). The implications for management of poor quality water where the quantity of water is limited are significant. Over a significant range of EC values, water quality may not produce field-observable differences in crop yield when the quantity of water is limiting. In a situation where water is limited, poor quality water might be used without concern for additional yield loss. The putative increase in salt tolerance with decreased irrigation was consistent with both a flux-limited condition for water uptake and a decreased contribution to the water potential from the osmotic potential relative to the matric potential of the soil. Further studies should be conducted to determine the mechanism. Regardless of the mechanism, salt tolerance appears to increase when irrigation was low. The implication for use of poor quality water under deficit irrigation is clear.

Because the slopes and intercepts of the initial portion of the piecewise linear regression models were not statistically different, the equation for the initial line from the piecewise regression model was used to develop models for other salinity treatments. Piecewise linear regression was subsequently used to find the irrigation level at which additional irrigation does not produce additional yield. The resulting models for a crop or yield component (dry matter or fruit) differ only in the critical irrigation levels. The utility of this approach is that crop production functions for simultaneous salt and water stress may be created from data of independently imposed stress factors.

The piecewise models represent a marked departure from the conventional conceptual models of the effects of salt and water stress. The conventional model assumes that the effects of matric and osmotic stress are additive. In contrast, we modeled crop response by limiting yield with the dominant stress factor and neglecting the effect of the secondary factor. The piecewise crop production functions provide a different view of the relative effects of salt and water stress than the functions proposed by Letey et al. (1985) or Bresler (1987) that predict separation of the response curves at very low irrigation levels. Moreover, the production functions of Letey et al. (1985) or Bresler (1987) predict that the effects of salt stress may be ameliorated by additional water (Bresler et al., 1982). In this study, the critical irrigation level decreased with increasing salinity, demonstrating that additional water does not compensate for salt stress. As a practical matter, the amount of water required to produce the greatest possible yield is decreased by salt stress so that less poor quality water is necessary to produce maximum crop yield than good quality water.

View this table: [in this window] [in a new window]   Table 4. Regression parameters for piecewise-linear models (shown in the figure) of melon fruit yield response to irrigation and salinity treatments. A and B represent the imposed slope and intercept, respectively, of the segment representing the yield response to irrigation, Ym is the greatest achievable yield for each irrigation-salinity treatment and Ymax is the maximum yield for all treatments. The letter in the group column represents a difference in the critical irrigation level (Ic) at the 0.95 probability level ( = 0.05).

	ACKNOWLEDGMENTS

Received for publication August 28, 2000.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Abdul-Jabbar, A.S., D.G. Lugg, T.W. Sammis, and L.W. Gray. 1984. A field study of plant resistance to water flow in alfalfa. Agron. J. 76:765–769.[Abstract/Free Full Text]
• Ayers, R.S., and D.W. Westcot. 1985. Water quality for agriculture. FAO Irrig. Drain. Paper 29. Rev. 1. FAO, Rome, Italy.
• Bresler, E. 1987. Application of conceptual model to irrigation water requirement and salt tolerance of crops. Soil Sci. Soc. Am. J. 51: 788–793.[Abstract/Free Full Text]
• Bresler, E., and G.J. Hoffman. 1986. Irrigation management for salinity control: Theories and tests. Soil Sci. Soc. Am. J. 50:1552–1560.[Abstract/Free Full Text]
• Bresler, E., B.L. McNeal, and D.L. Carter. 1982. Saline and sodic soils. Springer-Verlag. New York, NY.
• Cardon, G.E., and J. Letey. 1994. Plant water uptake terms evaluated for soil water and solute movement models. Soil Sci. Soc. Am. J. 32:1876–1880.
• Childs, S.W., and R.J. Hanks. 1975. Model of soil salinity effects in crop growth. Soil Sci. Soc. Am. Proc. 39:112–115.
• Devitt, D.A. 1989. Bermudagrass response to leaching fractions, irrigation, salinity, and soil types. Agron. J. 81:893–901.[Abstract/Free Full Text]
• deWit, C.T. 1958. Transpiration and crop yield. Versl. Landbouwk. Onderz. 64.6 Inst. of Biol. Chem. Res. Field Crops Herbage, Wageningen, the Netherlands.
• Grill, E., and H. Ziegler. 1998. A plant's dilemma. Science 282:252–253.[Free Full Text]
• Hanks, R.J., J. Keller, V.P. Rassmussen, and G.D. Wilson. 1976. Line source sprinkler for continuous variable irrigation–crop production studies. Soil Sci. Soc. Am. J. 40:426.[Abstract/Free Full Text]
• Hanks, R.J., R.F. Neilson, R.L. Cartee, L.S. Willardson, R.E. Long, and R.B. Sorenson. 1984. Use of saline wastewater from electrical power plants for irrigation. Utah State Univ. Ag. Exp. Stn. Res. Rep. 97. Utah State Univ., Logan, UT.
• Hillel, 1980. Applications of soil physics. Wiley-Interscience. New York, NY.
• Hoffman, G.J., E.V. Maas, T.L. Prichard, and J.L. Meyer. 1983. Salt tolerance of corn in the Sacramento-San Joaquin Delta of California. Irrig. Sci. 4:31–44.
• Jacoby, B. 1994. Mechanisms involved in salt tolerance by plants. In M. Pessarakli (ed.) Handbook of plant and crop stress. Marcel Deker, New York, NY.
• Kennedy, P. 1992. A guide to econometrics. The MIT Press, Cambridge, MA.
• Läuchli, A., and E. Epstein. 1990. In K.K. Tanji (ed.) Agricultural salinity assessment and management. ASCE Manuals and Reports on Engineering Practice no. 71. Am. Soc. Civil Engineers, New York, NY.
• Letey, J., and A. Dinar. 1986. Simulated crop-production functions for several crops when irrigated with saline waters. Hilgardia 54:1–32.
• Letey, J., A. Dinar, and K.C. Knapp. 1985. Crop-water production function model for saline irrigation waters. Soil Sci. Soc. Am. J. 49:1005–1009.[Abstract/Free Full Text]
• Maas, E.V. 1990. Crop salt tolerance. In K.K. Tanji (ed.) Agricultural salinity assesssment and management. ASCE Manuals and Reports on Engineering Practice no. 71. Am. Soc. Civil Engineers, New York, NY.
• Maas, E.V., and G.J. Hoffman. 1977. Crop salt tolerance—Current assessment. J. Irrig. Drain. Div. Am. Soc. Civil Eng. 103:115–134.
• Malek, E., and G.E. Bingham. 1993. Comparison of the Bowen ratio–energy balance and the water balance methods for measurement of evapotranspiration. J. Hydrol. 146:209–220.
• Marschner, H. 1995. Mineral nutrition of higher plants. Academic Press, New York, NY.
• Meiri, A. 1984. Plant response to salinity: Experimental methodology and application to the field. In I. Shainberg and J. Shalhevet (ed.) Soil salinity under irrigation: Processes and management. Ecological Studies 51. Springer-Verlag, Berlin, Germany.
• Munns, R. 1993. Physiological processes limiting growth in saline soils: Some dogma and hypotheses. Plant Cell Environ. 16:15–24.
• Nimah, M., and R.J. Hanks. 1973. Model for estimating soil, water, plant and atmospheric interrelations: I. Description and sensitivity. Soil Sci. Soc. Am. Proc. 37:522–527.
• Pang, X.P., and J. Letey. 1998. Development and evaluation of ENVIRO-GRO, an integrated water, salinity and nitrogen model. Soil Sci. Soc. Am. J. 62:1418–1427.[Abstract/Free Full Text]
• Quantitative Micro Software. 1997. Eviews-3 user's guide. Quantitative Micro Software. Irvine, CA.
• Russo, D., and D. Bakker. 1987. Crop-water production functions for sweet corn and cotton irrigated with saline waters. Soil Sci. Soc. Am. J. 51:1554–1562.[Abstract/Free Full Text]
• Seginer, I. 1978. A note on the economic significance of uniform water application. Irrig Sci. 1:19–25.
• Shalhevet, J. 1994. Using water of marginal quality for crop production: Major issues. Agric. Water Manage. 25:233–269.
• Shalhevet, J., and T.C. Hsiao. 1986. Salinity and drought, a comparison of their effect on osmotic adjustment, assimilation, transpiration and growth. Irrig. Sci. 7:249–264.
• Shani, U., R.J. Hanks, E. Bresler, and C.A.S. Oliveria. 1987. Field method for estimating hydraulic conductivity and matric potential-water content relations. Soil Sci. Soc. Am. J. 51:298–302.[Abstract/Free Full Text]
• Shani, U., H. Borochov-Neori, L.M. Dudley. 2001. Response of beans (Phaseolus vulgaris L.) to water stress, salinity, and sodium under and field conditions. Plant Soil (In press).
• van Genuchten, M.Th. 1987. A numerical model for water and solute movement below the root zone. Res. Pap. 121. USDA-ARS, U.S. Salinity Laboratory, Riverside, CA.

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