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DIVISION S-6-SOIL & WATER MANAGEMENT & CONSERVATION

Salinity–Grain Yield Response Functions of Barley Cultivars Assessed with a Drip-Injection Irrigation System

^a Unidad de Suelos y Riegos, Servicio de Investigación Agroalimentaria (Diputación General de Aragón, DGA), and Laboratorio Asociado de Agronomía y Medio Ambiente (DGA–CSIC), Apartado 727, 50080 Zaragoza, Spain
^b Departamento de Genética y Producción Vegetal, Estación Experimental de Aula Dei (Consejo Superior de Investigaciones Científicas, CSIC), and Laboratorio Asociado de Agronomía y Medio Ambiente (DGA–CSIC), Apartado 202, 50080 Zaragoza, Spain

aragues{at}mizar.csic.es

	ABSTRACT

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion Conclusions REFERENCES

 
Success in breeding crops for yield and other quantitative traits depends on the use of convenient methods to accurately evaluate genotypes under field conditions. We report the evaluation of a drip-injection irrigation system (DIS) for assessing the salt tolerance of barley genotypes. Ten barley cultivars were randomized within each of nine salinities imposed by a DIS in an experiment with two replications. Grain yields were regressed against soil salinity (EC_e) using a sigmoidal growth response model to obtain the statistics Y_m, EC_e50, and p. The data fitted the model well; the average correlation coefficient was 0.89 (P < 0.001) when the observations for each cultivar in both replications were pooled, and the average SEs were <12% of the mean Y_m and EC_e50 estimates. We concluded that the DIS is a reliable system for estimating the salinity response functions of barley. The grain yields obtained in the control and intermediate soil salinity were highly correlated ( , P < 0.01), indicating that the highest-yielding cultivars under nonsaline conditions were also most productive under intermediate saline conditions. However, yields at high soil salinity were not correlated with the control yields. For the same set of genotypes, the estimates of EC_e50 obtained with the DIS and with a triple-line source system (TLS) were strongly correlated ( , P < 0.01), even though the direct absorption of salts by the leaves, which is a feature of the TLS, had a deleterious effect on grain yields. Results from the DIS trial suggest that the salt tolerance of barley quoted in the literature for similar climatic conditions could be overestimated by 40%.

Abbreviations: DIS, drip-injection irrigation system • EC, electrical conductivity • ET_c, evapotranspiration • EC_e, saturation extract EC • EC_e50, the EC_e that reduces yield by 50% • EC_et, threshold EC_e or the EC_e that reduces Y_m by 5% • EC_iw, irrigation water EC • LF, leaching fraction • N, number of emitters • TLS, triple-line source sprinkler system • V_iw, volume of irrigation water • Y, grain yield obtained for a given EC_e • Y_m, grain yield under nonsaline conditions

	INTRODUCTION

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion Conclusions REFERENCES

 
THE SALINIZATION OF SOILS AND WATERS places an increasing constraint on crop production in the arid and semiarid regions of the world. Plant breeding focused on increasing the salt tolerance of crops and, in particular, that of a tolerant crop such as barley (Hordeum vulgare L.) (Maas and Hoffman, 1977), could improve the profitability of many of the world's one billion salt-affected hectares (Szabolcs, 1989).

The plant-breeding approach requires methods for the efficient screening and identification of salt-tolerant genotypes. Based on current knowledge of the physiological mechanisms of salt tolerance, identifying traits (other than yield) for screening in early generations has not been possible. Recently, Royo and Aragüés (1999) concluded that the barley genotypes with the highest yield in nonsaline conditions were also the most productive at medium and high salinities. However, unless these results can be confirmed and generalized in further trials and for other crops, we still need to measure grain yield under saline conditions to reliably identify salt-tolerant barley genotypes (Isla et al., 1998). These measurements require methods for establishing and controlling salinities in the field. Lack of such convenient and effective methods has been an important limitation to breeding crops for increased salinity tolerance (Blum, 1988; Flowers and Yeo, 1995; Shannon, 1997).

We developed the triple-line source sprinkler (TLS) system (Royo et al., 1987; Aragüés et al., 1992), which allows the imposition of any desired linear gradient of soil salinity, enabling the salinity–yield response functions of genotypes to be determined with reasonable accuracy. Although this system has been proven to be simple, robust, and useful for salt-tolerance studies (Frenkel et al., 1990; Royo and Aragüés, 1993, 1999), wetting the crops with the sprinkled saline irrigation water allows salts to be absorbed through the leaves. Such absorption of ions through leaves could distort the assessment of salt tolerance.

To avoid foliar salt absorption and its consequences, we have recently developed (Aragüés et al., 1999) a drip-injection irrigation system (DIS), which does not wet the leaves with saline water. The DIS proved to be accurate and robust in that (i) the measured salinities and target irrigation water salinities were similar, (ii) the temporal and within-treatment spatial variabilities were low to moderate, and (iii) the irrigation water and soil salinity values were strongly correlated. We concluded that the DIS constitutes a practical, low-cost (in 1998 U.S. dollars, about $5000 for an automated DIS and $3000 for a manual DIS) system for imposing defined soil salinities suitable to evaluate the salt tolerance of crops in the field.

In this paper, we show that the salinity–yield response functions of barley cultivars can be assessed with good internal consistency and accuracy using the DIS. We show that the ranking of the salinity tolerance of nine barley cultivars obtained with the DIS was similar to that obtained with the TLS system, and further, that the salt tolerance of barley quoted in the literature could be a considerable overestimate.

	Materials and methods

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion Conclusions REFERENCES

 
Description of the Drip-Injection Irrigation System (DIS)
The DIS set-up comprises two tanks (one for fresh and one for saline water), a centrifugal pump, an injection pump, a conventional drip-irrigation system with various independent irrigation sectors, plus filters for removing particulates. The DIS used in this work was fully automated using a computer to control the appropriate solenoid valves (Aragüés et al., 1999). The DIS is based on the combination of two pumps in parallel: a centrifugal pump for supplying fresh water and an injection pump for injecting saline water into the fresh water flow. Fresh and saline waters are mixed in the irrigation line to produce an irrigation water whose salinity (EC_iw) depends on the discharge ratio of the pumps. The number of emitters (N) connected to the pumping system determines this discharge ratio (i.e., the discharge of the centrifugal pump that blends with the fixed discharge of the injection pump). Therefore, the EC_iw applied to a given irrigation sector depends on the total number of emitters installed in that sector. For a saline water of (made up of NaCl and CaCl₂ in a 2:1 weight ratio), a fresh water of EC 2 dS m^-1 (and the other variables fixed), we determined that (Aragüés et al., 1999).

Irrigation Network and Salinity Treatments
Figure 1 shows the schematic layout of the DIS irrigation network. Nine irrigation water salinity treatments of (dashed rectangles in the figure) were imposed in a trial of a split-plot design with two replications on the experimental farm of the Agronomic Research Service for Aragón (Servicio de Investigación Agroalimentaria, Zaragoza, Spain), located in the central part of the Ebro river basin (0°49' W, 41°44' N). The soil is a Typic xerofluvent with a silty-clay-loam texture and with a layer of polygenic gravels underneath (depth > 0.75 m).

View larger version (77K): [in this window] [in a new window]   Fig. 1 Schematic layout of the DIS irrigation network. Each of the salinity Treatments 1 through 9 is characterized by its target ECiw (irrigation water EC) value, and are split into one to three irrigation sectors. Each irrigation sector has four laterals with the appropriate number of emitters to deliver its target ECiw (see example in left dashed rectangle). The number of 1.5 m2 barley plots in each sector is indicated within the small rectangles. The location of Replications 1 and 2 is shown on the right of the figure

We applied 73 irrigations (3–4 irrigations wk^-1) during the barley irrigation season (February to the end of May 1995) to accommodate the weekly V_iw to the weekly evapotranspiration (ET_c) of barley (measured in a weighing lysimeter close to the experimental plot) plus the appropriate volume of water needed to obtain high leaching fractions (LF). These LFs were quite high (LF > 70%) during February and March in order to rapidly impose the target soil salinity values in the initially nonsaline soil, and decreased thereafter to values of about 60%. Figure 2 shows the weekly volume of irrigation water (V_iw), the barley ET_c, and the corresponding LF (calculated as ). The mean LF during the experimental period was 67%.

View larger version (22K): [in this window] [in a new window]   Fig. 2 Measured weekly volumes of irrigation water (Viw) and barley evapotranspiration (ETc), and calculated weekly leaching fractions (LF) for the period 15 Feb. to 15 May 1995

The irrigation-season average EC_iw values measured in each irrigation sector are summarized in Table 1. Based on these values and the linear regression equation between EC_iw and EC_e (saturation extract EC of 36 soil samples taken on two dates at a 0- to 50-cm depth in each salinity treatment) shown in the legend of Table 1, we estimated the irrigation-season average root zone soil EC_e for each irrigation sector (Table 1). These EC_e values were the independent variables used for obtaining the soil salinity–grain yield response functions. We choose the 0- to 50-cm soil depth as representative of the root zone of barley irrigated with the high-frequency DIS since, in a contiguous plot sown with 13 barley cultivars under a high-frequency TLS system, 78% or more of the total average root length densities measured in various salinity treatments were contained in the first 50 cm (Isla et al., 1999).

The relationship between grain yields and EC_e for each barley cultivar was described by a three parameter sigmoidal growth response model (option 12 of the SALT program described by van Genuchten, 1983):

(1)

where Y is the grain yield obtained for a given EC_e, Y_m is the grain yield under nonsaline conditions, EC_e50 is the EC_e that reduces yield by 50%, and p is a parameter that determines the steepness of the curve.

Estimation of model parameters was performed by nonlinear least squares techniques using the maximum neighborhood method of Marquardt (1963). The goodness of fit of each salinity–yield response function was assessed from the correlation coefficient between the observed and the estimated Y values. The standard errors (SE) of Y_m, EC_e50, and p were obtained from the SALT program. The F test for differences between the regressions for the different cultivars was performed using the procedure described in Dixon (1985)(p. 245). In those cases where the F ratio was significant, the Y_m, EC_e50, and p estimates obtained for each cultivar were compared using the Duncan's Multiple Range Test. To ease comparison with published results, we calculated an additional salinity tolerance parameter, the threshold EC_e (EC_et), defined as the EC_e that reduced the Y_m by 5%.

	Results and discussion

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion Conclusions REFERENCES

 
Reliability of the Salinity Response Functions
Figure 3 shows the relationships between grain yield and EC_e for the 10 barley cultivars and the regressions of best fit to the data from Replicates 1, 2, and 1 + 2. The cultivars `Kym' (only sown in Replication 2) and `Acsad-60' had the lowest yields in all treatments, probably because of poor seed quality and lack of adaptation to our climatic conditions, respectively. The cultivars `Alpha', `Criter', and `Martin' had the highest yields. It should be noted that the DIS provided for a wide range of soil salinity ( , Table 1), uniformly distributed over the range, giving correspondingly large variations in grain yields. This feature of the data enabled the response functions to be estimated with good accuracy. In addition, visual inspection of the response functions shows that for a given cultivar, the functions were generally similar between replications.

View larger version (31K): [in this window] [in a new window]   Fig. 3 Soil salinity (ECe)–Grain yield response functions of 10 barley cultivars developed using the option 12 of the SALT program (van Genuchten, 1983) using data obtained with the drip-injection irrigation system. Replication 1: +, dashed line; Replication 2: x, solid fine line; Replications 1 + 2: solid thick line

ANOVA performed on the model statistics showed that there were significant differences among cultivars for EC_e50 and Y_m, but not for p (Table 3) . The Spearman's rank order coefficient between the EC_e50 and Y_m statistics obtained in Replications 1, 2, and 1 + 2 were higher than 0.83, indicating that the ranking of cultivars for these statistics was similar (i.e., not significantly different at P > 0.01). The most important result obtained in the ANOVA from the point of view of validation of the DIS is that there were not significant differences (P > 0.05) between replications. Therefore, we pooled together the observations of both replications (i.e., 1 + 2, with 18 observations, except for `Kym') and obtained the single response functions of each cultivar shown in Table 2. Based on Y_m, the cultivars fell into six groups, whereas based on EC_e50 they fell into three groups (Table 2). All the R² values were significant at P < 0.001, and the SE of the Y_m and EC_e50 statistics were low, with average SE < 12% of their corresponding means. Based on our results, we conclude that the DIS is a reliable system for estimating the salinity response functions of barley.

The average threshold EC_et calculated in this work for the 10 barley cultivars was 6.0 dS m^-1, with a range of 4.3 (Alpha) to 8.0 (Albacete), compared with the 8.0 dS m^-1 reported by Maas and Hoffman (1977). This value is 33% higher than ours, indicating that the salt tolerance of barley, based on the threshold EC_et reported in the literature, could also be overestimated. On the other hand, we estimated the mean value of p to be 3.8, a mean which is identical to the value given by van Genuchten and Gupta (1993). Our results therefore agree with the proposition of those authors in that the p parameter could be fixed at a value of 3.0 without affecting the accuracy of the least-square fit for most data sets.

Correlations Between the Statistics of the Response Functions
We calculated the correlations between Y_m and EC_e50 (Table 2, Replications 1 + 2) and between the estimated grain yields at intermediate (Y₉ or grain yields at ) and high (Y₁₇ or grain yields at ) salinities. The cultivar `Kym' was not included in the calculations because it was sown only in one replication. On average, grain yields were decreased by 20% in the intermediate salinity treatments and by 70% in the high salinity treatments, relative to the control yields. The results of these correlations are shown in Fig. 4 . The Y_m - Y₉ regression was significant at P < 0.01, whereas the Y_m - Y₁₇ regression was not significant at P > 0.05 (Fig. 4A). These results are consistent with those of Isla et al. (1997) with a set of 18 barley cultivars grown at three salinity levels with a conventional drip-irrigation system, and indicate that the highest-yielding cultivars under nonsaline conditions were also most productive under intermediate-saline conditions, though not under high-saline conditions. The consistency of the results obtained with the DIS is also shown by the significant correlation (P < 0.01) found between Y₉ and Y₁₇ (Fig. 4B). However, Fig. 4C shows that the salinity tolerance parameter EC_e50 was not significantly correlated (P > 0.05) with Y₉, whereas it was significantly correlated (P < 0.05) with Y₁₇. This is an unexpected result and may be a consequence of the small number of barley cultivars studied. Further work with a larger set of cultivars is therefore needed to assess the correlations between EC_e50, Y₉, and Y₁₇.

View larger version (24K): [in this window] [in a new window]   Fig. 4 Relationships between (A) estimated grain yields in the control (Ym) and at ECe values of 9 (Y9) and [17 (Y17)] dS m-1, (B) Y9 and Y17, and (C) ECe50 and Y9 and Y17. The number of cultivars used in these analyses was nine (i.e., the cultivar `Kym' was deleted)

View larger version (18K): [in this window] [in a new window]   Fig. 5 Relationship between the ECe50 values obtained in the DIS and the TLS. The number of cultivars used in this analysis was nine (i.e., the cultivar `Kym' was deleted)

We also compared the grain yields estimated at EC_e values of 9 dS m^-1 in the DIS (Y₉-DIS) vs. 6 dS m^-1 in the TLS (Y₆-TLS), and at 17 dS m^-1 in the DIS (Y₁₇-DIS) vs. 12 dS m^-1 in the TLS (Y₁₂-TLS). We compared these EC_e values because when transformed to soil solution EC, they were similar in both systems. So differences in grain yields in the DIS and the TLS should be mainly attributed to the absorption of salts by leaves in the TLS. The negative effect of sprinkling with saline waters was most evident at high EC_iw values of around 15 dS m^-1, as demonstrated by the normalized grain yields, which were in the DIS but only in the TLS. On the other hand, for intermediate EC_iw values of about 8 dS m^-1, these normalized values were quite similar ( in the DIS and in the TLS), suggesting that with our TLS strategy of short pre- and post- irrigations with fresh water, the deleterious effect of saline sprinklings in barley was negligible at these intermediate levels of irrigation water salinity.

	Conclusions

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion Conclusions REFERENCES

 
In an earlier paper (Aragüés et al., 1999), we described and validated a drip-injection irrigation system (DIS) for creating controlled soil salinities suitable for evaluating the salt tolerance of crops in the field. In the present work, we validated the DIS from the point of view of its reliability in establishing the salinity–grain yield response functions of 10 barley cultivars. Our results indicated that these functions were assessed with excellent internal consistency and accuracy. We also showed that the salt tolerance of barley quoted in the literature could be overestimated by 40%. Finally, we found that the rankings in salinity tolerance obtained in the TLS and in the DIS were similar, even though the direct absorption of salts by the leaves in the TLS had an important negative effect on grain yields when sprinkler-irrigated with high salinity waters.

	ACKNOWLEDGMENTS

 
We thank J. Gaudó, M. Izquierdo, T. Molina, and L. Naval for technical assistance.

Received for publication February 12, 1999.

	REFERENCES

TOP ABSTRACT INTRODUCTION Materials and methods Results and discussion Conclusions REFERENCES

 

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