Limitation of water resources and decline in the quality of soil and water have led to the use of saline water and application of management systems for reducing irrigation water. The subject of this study was to determine the effect of salinity and water stress on sugarcane yield in Iran with an operational and planning distribution model (OPDM) for 7 years (2002–2008). Irrigation scenarios consisted of the full irrigation (I1), 85% (I2) and 70% (I3) of the sugarcane water requirement, and salinity scenarios were the average salinity of the Karun River, S1 (dS m−1), S2 = S1 +2 and S3 = S1 +4. The root mean square error and mean bias error (0.04 and 0.02, respectively) show the low error percentages and the values of EF = 0.65 and d = 0.71 indicated the high accuracy of the yield simulation with OPDM. Significant differences were observed among the different irrigation levels and this difference in I3 was more than in I2. The effect of different salinity levels on yield reduction was not significant. Overall, results showed that there was an individual and combined effect of salinity and water deficit on sugarcane yield; however, the effect of different irrigation levels on the yield was more than the salinity.

INTRODUCTION

Plants are under water stress when transpiration becomes intense, or when the water supply to their roots becomes limiting. Water stress is primarily caused by a water deficit, such as a drought or high soil salinity. Saline water can be used for irrigation and then there are reductions in crop yields. The reductions vary according to the quantity and quality of applied water, salt tolerance of the crop, cropping pattern and the climatic conditions. Clearly, the losses in production and productivity are area specific. Climate changes have directly affected water availability globally, due to variations in precipitation levels, which are estimated to have serious consequences for the water balance in plants (Ryan 2011). Correspondingly, drought is among the greatest limits to productivity and geographic distribution of cultures. The future of irrigated agriculture will need to include the use of water containing higher levels of soluble salts (Malash et al. 2008). As Hassanli et al. (2010) showed, irrigation with effluent for the calcareous clay loam soil in Southern Iran led to a significant increase in sugar beet root yield, and irrigation water use efficiency (IWUE) for root and sugar content were compared with irrigation with fresh water. Amer (2010) studied corn response to irrigation amount, salinity hazard, and their interactions and showed that irrigation and salinity individually or together decrease the corn yield and growth. He also concluded that full irrigation without salinity had the highest corn yields and saline water with a 0.6 evapotranspiration (ET) achieved the lowest. Katerji et al. (2004) indicated that the corn yield response to water stress does not differ according to the cause, whether salinity or drought. Chen et al. (2009) studied the effect of drip irrigation with saline water on emergence, vegetative growth, yield, and IWUE of oleic sunflower when electrical conductivity (EC) was more than 10.9 dS m−1. They showed that the plant height declined by 0.6–1% and the yield decreased 1.8% for every one dS m−1 increase in EC. Wan et al. (2010) studied the responses of cucumber to saline water irrigation (EC ≤4.9 dS m−1) and concluded that in a 3-year open field experiment in North China the highest cucumber yield loss was about 25% compared to fresh water irrigation. Kang et al. (2010) investigated the effects of drip irrigation with saline water of different salinity levels on seedling emergence, growth, yield, and IWUE of waxy maize and concluded that the yield decreased about 0.4–3.3% for every 1 dS m−1 increase in EC and that seedling emergence rate of waxy maize was not affected by drip irrigation with saline water.

Sugarcane has great economic importance due to its application in the food industry (Rodrigues et al. 2009). Ethanol from sugarcane is the most competitive in terms of energy use and net carbon balance, and the energy use projections from the International Energy Agency foresee that by 2050, sugarcane is the only first-generation biofuel that will keep expanding (IEA 2011). Sugarcane is extremely sensitive to water deficit and moderately sensitive to salinity with a threshold yield reduction of 1.7 dS m−1 (Maas & Hoffman 1977).

Wiedenfeld (1995) evaluated the effect of reduced water availability on sugarcane growth, quality and yield. He concluded that irrigation level had a very significant effect on sugarcane yield and juice quality, so that cane yield, sugar content and sugar yields all increased dramatically as the irrigation level increased. Lingle et al. (2000) examined the effect of irrigation water salinity on yield and juice quality of sugarcane and showed that yield and most quality components were not significantly reduced by 1.25 dS m−1 water, while the 2.93 and 4.70 dS m−1 treatments reduced stalk height and weight but not stalk numbers. Choudhary et al. (2004) studied the effect of irrigation with sodic and saline–sodic waters on soil properties and their influence on growth, yield and quality of sugarcane. They concluded that cane yield and yield-contributing parameters decreased significantly over the years under sodic and saline–sodic irrigations compared with good quality water; it further decreased by 0.29 ton ha−1 and 0.18 ton ha−1 per year under sodic and saline–sodic irrigation, respectively.

Computer models are now increasingly used for a wide range of applications in the research and management of natural systems. Using computer models reduces the limitations of field studies. Domínguez et al. (2012) used the MOPECO model to simulate yields of maize crop under deficit irrigation and salinity and they concluded that irrigation water in an area with EC = 0.85 dS m−1 does not significantly affect the final yield of this crop (4.7% decrease in yield).

The operational and planning distribution model (OPDM) was developed by the Department of Biological and Irrigation Engineering, Utah State University, to be used in the planning for water resources and training (USU 1996). The model was developed to perform simulation of water distribution and crop yield response for irrigation and other uses in complex canal and drainage networks. Crop water requirements are calculated based on specified cropping patterns and weather information, and simulated flows are routed through the systems from main supply sources and open drains. The model can estimate the relative crop yield reduction due to the root zone water deficit, soil water salinity, and waterlogging. Mowafy et al. (2010) used the OPDM model to assess the impact of the allocation of irrigation water and to investigate the effect of water quality on gross revenue. The results of this study showed that the effect of increasing salinity on the gross revenue is less than that of decreasing water quantity in the simulation model. Preeyaphorn & Kobkiat (2001) used the OPDM model for agricultural production forecasting and concluded that in the wet season the highest efficiency was achieved when 60% of the command area was planted with rice, because of high production compared with the planted area and lowest insufficient water area. Also, in the dry season, the command area could be increased about 100% compared to that in the past for rice, field crop, and farm crop area because of sufficient water.

The objective of this study was to determine the ability of the OPDM model to define the effect of salinity and water deficit on sugarcane yield in different scenarios. Different approaches for combining the effect of water stress and salinity on sugarcane yield can be selected in the model and were compared. So far, no studies have been done with our aims using the OPDM model.

METHODS

Case study

Sugarcane is cultivated in the southwest of Iran (Khuzestan province, Figure 1) over an area of more than 9 × 108m2. The Amir-Kabir irrigation and drainage network district lies between 31°15′–31°40′ N latitude and 48°12′–48°30′ E longitude. The total cultivated area in this network is 14 × 107 m2.

Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

The physical and chemical properties of the soil in the Amir-Kabir network (Sugarcane Research Center 2008) are shown in Table 1. The soil texture is mostly heavy with high clay and silt and is poor in organic matter (<1%).

Table 1

Physical and chemical characteristics of soil

Depth (cm)TextureBulk density (g cm−3)Organic matter (%)pHTotal N (ppm)Total P (ppm)Total K (ppm)
0–60 Silty clay loam 1.5 <1 7.5–8.2 400–500 <10 150–250 
Depth (cm)TextureBulk density (g cm−3)Organic matter (%)pHTotal N (ppm)Total P (ppm)Total K (ppm)
0–60 Silty clay loam 1.5 <1 7.5–8.2 400–500 <10 150–250 

Climatological data for the period of this study 2002–2008 including wind speed, maximum and minimum air temperature, pan evaporation and rainfall were available from the weather station located at the site (Sugarcane Research Center 2008). According to the measured data from stations in Amir-Kabir, in the growth season, the mean of maximum and minimum temperatures were 35.7 °C and 16.8 °C, respectively, and the meteorological parameters for Khuzestan are as shown in Figure 2. The amount of rainfall was lower than the evaporation rate during the 7 years.

Figure 2

Long-term distribution of the average climatic parameters at the Amir-Kabir stations (2002–2008).

Figure 2

Long-term distribution of the average climatic parameters at the Amir-Kabir stations (2002–2008).

Table 2 shows the details of input data needed for simulation with OPDM. These data have been gathered from the Amir-Kabir sugarcane fields (Sugarcane Research Center 2008).

Table 2

Input data for the OPDM model

Soil data
Leaching fractionRunoff coefficientCapacity (mm m−1)Initial water (mm m−1)Initial salinity (ds m−1)Porosity %Specific yield %Rise percolation (mm day−1)
0.21 0.01 100 35 2.25 43 39 84 
Crop data
Area (m2)Root depth (mm)Crop coefficient, KcDeficit yield threshold %Salinity yield threshold (dS m1)
78 × 105 1,200 1.2 65 1.7 
Aquifer data
Min. depth (m)Max. depth (m)Initial depth (m)
1.2 1.9 1.17 
Drainage data
Salinity (dS m1)Max. flow (m3s1)
18 0.78 
Soil data
Leaching fractionRunoff coefficientCapacity (mm m−1)Initial water (mm m−1)Initial salinity (ds m−1)Porosity %Specific yield %Rise percolation (mm day−1)
0.21 0.01 100 35 2.25 43 39 84 
Crop data
Area (m2)Root depth (mm)Crop coefficient, KcDeficit yield threshold %Salinity yield threshold (dS m1)
78 × 105 1,200 1.2 65 1.7 
Aquifer data
Min. depth (m)Max. depth (m)Initial depth (m)
1.2 1.9 1.17 
Drainage data
Salinity (dS m1)Max. flow (m3s1)
18 0.78 

ET methods

OPDM calculates crop water requirements by one of five evapotranspiration methods (Hargreaves (Hargreaves & Samani 1985), pan evaporation (Doorenbos & Pruitt 1977), Penman–Monteith (Chiew et al. 1995), actual crop ET, mean monthly ET0) according to the user's choice. In this paper, we used the pan evaporation method for which the equation is a simple linear relationship between ETp and Epan and is written as 
formula
1
where ETp is the potential evapotranspiration (mm day−1), Epan is the evaporation rate from a small, pounded pool of water in a standardized metal tank (mm day−1), and Kp is an empirically determined coefficient.

Yield response

Crop yield response due to deficit, salinity and waterlogging can be estimated by the model. The equation of deficit yield reduction was adapted from Jensen (1968) and is written as follows: 
formula
2
where Yrel is the relative yield (%), ETa is actual transpiration (mm day−1), ETm is the maximum potential transpiration, λ is a fitted exponent (a calibrated value), and the subscript i refers to the growth stage (OPDM uses five growth stages, so, n = 5). The relationship of salinity yield effects to crop type, crop growth stage, soil fertility, soil, water and salinity concentration profiles, and many other factors, is complex, but for modeling purposes, the following simple approximation (Maas & Hoffman 1977) is used: 
formula
3
where Yrel is the relative yield (%), As is the threshold salinity (dS m−1), Bs is the rate at which relative crop yield declines with increasing salinity beyond As (% per dS m−1), and ECe is the salinity of the soil water extract (dS m−1).

The OPDM model uses the layout of the area in order to show the graphical arrangement of nodes, their connections, and linkages between the supply and drainage systems. The area irrigates through a network of irrigation canals, and excess water drains through a network of drains. The basic data of the project area that includes the canal and drain networks and all command areas are shown in Figure 3. Nine different node types can be used in OPDM to build an irrigation and drainage system. Each node has from one to three connections to other nodes in the same system (supply or drainage), and up to two linkages that allow the simulation of water movement from supply system nodes to drain system nodes, and vice versa. One type of linkage between two nodes within the supply system is also allowed. The possible node connections and linkages are dependent on the node types. A legend listing the node types is given in Figure 3.

Figure 3

The layout of the Amir-Kabir irrigation and drainage network designed with OPDM.

Figure 3

The layout of the Amir-Kabir irrigation and drainage network designed with OPDM.

Scenarios

Irrigation treatments were changed in the volume of water applied; the first treatment was full irrigation based on pan evaporation (I1), and the second and third treatments were 85% (I2) and 70% (I3) of the sugarcane water requirement. The salinity treatments were the average salinity of the Karun River (S1 dS m−1), S2 = S1 +2 and S3 = S1 +4, respectively. Table 3 shows the values of salinity treatments used for determining the yield of sugarcane in the OPDM model.

Table 3

Salinity treatments used in simulation

YearS1 (dS m−1)S2 (dS m−1)S3 (dS m−1)
2002 1.54 3.54 5.54 
2003 1.68 3.68 5.68 
2004 1.8 3.8 5.8 
2005 1.53 3.53 5.53 
2006 1.37 3.37 5.37 
2007 1.47 3.47 5.47 
2008 2.74 4.74 6.74 
YearS1 (dS m−1)S2 (dS m−1)S3 (dS m−1)
2002 1.54 3.54 5.54 
2003 1.68 3.68 5.68 
2004 1.8 3.8 5.8 
2005 1.53 3.53 5.53 
2006 1.37 3.37 5.37 
2007 1.47 3.47 5.47 
2008 2.74 4.74 6.74 

RESULTS AND DISCUSSION

The running of OPDM with initial data indicated a large difference between actual and simulated yield. In order to increase the model's accuracy for simulation we then calibrated it with sensitive parameters.

Sensitivity analysis

The presented model is robust and requires only a minimum of input data, which are readily available or can easily be collected. The sensitivity analysis illustrated the robustness of this model for yield simulation. To determine the sensitivity of OPDM, spider diagrams were used (Eschenbach & McKeague 1989) (Figure 4). This type of diagram allows easy comparison of the relative impacts of these parameters when varied over their realistic range. The parameters were leaching fraction, runoff coefficient, root depth and Kc crop coefficient and were modified by the user in the model. The results showed that the model was sensitive to changes in runoff, Kc coefficient and the leaching fraction respectively, with lowest sensitivity to changes in root depth.

Figure 4

The OPDM model's sensitivity to the parameters.

Figure 4

The OPDM model's sensitivity to the parameters.

A numerical index (sensitivity index (SI)) of the sensitivity analysis indicated similar results to those with the spider diagrams (Table 4). According to SI (Equation (4)) the maximum and minimum sensitivity of the model was related to runoff coefficient and root depth with SI = 0.19 and 0.01, respectively:

Table 4

The sensitivity analysis with SI

SIParameter rangeParameter
0.19 0–0.15 Runoff coefficient 
0.04 0.15–0.25 Leaching fraction 
0.01 800–1,600 Root depth (mm) 
0.09 0.4–1.2 Kc coefficient 
SIParameter rangeParameter
0.19 0–0.15 Runoff coefficient 
0.04 0.15–0.25 Leaching fraction 
0.01 800–1,600 Root depth (mm) 
0.09 0.4–1.2 Kc coefficient 

where SI is in the range 0 to 1, Ya is the actual yield (%) and Yi is the simulated yield (%).

Calibration and validation

The modeling efficiency EF, which is used to represent the value of a function, is desirable. RMSE is the root mean square error measure and indicates the degree of precision of the estimates, which should be minimized as much as possible. MBE is a mean bias error that shows little difference between estimated and measured data. A negative value shows a lower estimate and a positive value shows a higher estimate. The corresponding index d ranges from 0 to 1, with the value of d greater as the amount estimated is closer to the measured values (Salazar et al. 2008). To ensure the accuracy of the results, the year 2002 was selected for calibration and 2003–2005 for the validation test. Results showed that after the calibration a good response was received with regard to the RMSE index (Equation (5)). Also, RMSE = 0.03 in the validation test confirmed the results (Table 5). 
formula
3
(Willmott et al. 1985)(5)
Table 5

OPDM calibration and validation

Calibration (2002)
ParameterBefore calibrationAfter calibrationBeforeAfterValidation (2003–2005) RMSE
Leaching fraction 0.25 0.21 0.03 0.02 0.03 
Root depth (mm) 1,500 1,000 0.01 
Kc coefficient 0.8 1.2 0.08 0.05 
Runoff coefficient 0.01 0.18 0.06 
Calibration (2002)
ParameterBefore calibrationAfter calibrationBeforeAfterValidation (2003–2005) RMSE
Leaching fraction 0.25 0.21 0.03 0.02 0.03 
Root depth (mm) 1,500 1,000 0.01 
Kc coefficient 0.8 1.2 0.08 0.05 
Runoff coefficient 0.01 0.18 0.06 

In Equations (5)–(8), Ei, Mi, and n are measured value, estimated value, mean and the number of data, respectively.

Statistical analysis

Results indicated that during the 7 years of this assessment OPDM could simulate the reliable yield of sugarcane (Table 6). The model's performance and ability to simulate the sugarcane according to water quality and quantity was acceptable and it can be recommended for future planning.

Table 6

Statistical comparison between actual and simulated yield

YearRMSEMBEEFD
2002–2008 0.04 0.02 0.65 0.71 
YearRMSEMBEEFD
2002–2008 0.04 0.02 0.65 0.71 

The effect of irrigation and salinity on sugarcane yield

To show the statistical significance levels of irrigation, the average simulated sugarcane yield in irrigation treatments with OPDM was analyzed with 95% confidence intervals. Results indicated that in I2 and I3 levels the yield reduced by 19% and 42%, respectively, compared with I1. It seems when sugarcane under water stress starts to lose water, relative water content (RWC) decreases and triggers a significant reduction in the CO2 uptake rate due to the stomata. A decrease of 10–20% in RWC caused reduction in all photosynthetic apparatus of tolerant and sensitive sugarcane plants submitted to water deficit (Graça et al. 2010). Significant differences were observed between the irrigation levels (Figure 5). It can be concluded that irrigation treatments had very large effect on biomass. The results from this experiment agree with those of other studies on sugarcane (Inman-Bamber & De Jager 1986) that water stress restricts processes. Da Silva et al. (2013) examined four irrigation treatments (25%, 50%, 75% and 100%) during 2009–2010 and 2010–2011 on sugarcane yield in Brazil and reported that the highest yield was obtained by full irrigation (100% of water requirement).

Figure 5

Effect of different irrigation levels on sugarcane yield (2002–2008). Error bars are 95% confidence intervals.

Figure 5

Effect of different irrigation levels on sugarcane yield (2002–2008). Error bars are 95% confidence intervals.

The salinity levels reduced the yield but this reduction was not significant (Figure 6). With increasing salinity from S1 to S2 and S3, the yield decreased 3% and 9%, respectively. Wiedenfeld (2008) compared the effect of two salinity irrigation levels (1.3 and 3.4 dS m−1) on sugarcane yield in an area with a subtropical and semi-arid climate. He concluded that the product was reduced approximately 17% at 3.4 dS m−1 compare with 1.3 dS m−1. Lingle et al. (2000) examined the saline water effect (0.01, 1.25, 2.93, and 4.70 dS m−1) on sugarcane yield and showed that with the 1.25 dS m−1 treatment, the yield and components were not decreased significantly.

Figure 6

Effect of different salinity levels on sugarcane yield (2002–2008). Error bars are 95% confidence intervals.

Figure 6

Effect of different salinity levels on sugarcane yield (2002–2008). Error bars are 95% confidence intervals.

Results show that a significant difference in measured yield with I1S1 treatment was not observed. With increasing salinity and water stress from I1S1 to I2S2 and I3S3, the biomass decreased 3.5% and 9.5%, respectively. Also, in I2S2 and I2S3, the yield reduced by 2.65% and 8.6% compared with I2S1 (Figure 7). Boumans et al. (1988) reported that marginal quality waters (EC: 4–6 dS m−1) were being directly used in several locations in Haryana and the average yield depressions for crops including cotton, millet, mustard and wheat were less than 20%.

Figure 7

Interaction effects of different levels of salinity and irrigation on simulated yield in OPDM (2002–2008).

Figure 7

Interaction effects of different levels of salinity and irrigation on simulated yield in OPDM (2002–2008).

The response to water stress in the I3 scenario was more evident. We observed the yield reduction with increasing salinity levels in this treatment. Biomass under I3S2 and I3S3 treatments decreased by 4.2% and 10.5% compared with I3S1. Overall, Figure 7 indicates that significant differences were observed between different levels of irrigation and also shows that the salinity levels do not have similar effects. Nyati (1996) reported that water stress had significant effects on sugarcane growth. Wiedenfeld & Enciso (2008) showed with the sugarcane response to three irrigation levels (20% below ETc, full ETc and 20% above ETc) that growth was decreased but there were no significant differences in cane or sugar yields.

Yield function

One of the more useful and widely accepted production function forms is based on the consumptive use, or ET, of water as a yield index. Vaux & Pruitt (1983) reviewed a reliable body of previous research in which ET production functions were derived from a variety of agricultural crops. They concluded that the yield of a given crop could generally be described as a linear function of cumulative ET, although they cited several studies in which curvilinear relationships between Y and ET were found. To evaluate yield variations for actual ET, linear regression functions were obtained for all levels of irrigation (I1, I2 and I3) (Figure 8). The results of Figure 8 show that high correlation was observed with R2 = 0.77 between ET rates and crop yield. As Istanbulluoglu et al. (2009) reported, there was a positive linear relationship between ET and the grain yield of safflower.

Figure 8

Yield function for different levels of irrigation according to amount of actual ET.

Figure 8

Yield function for different levels of irrigation according to amount of actual ET.

CONCLUSIONS

In this study, the sugarcane yield in the Amir-Kabir network was simulated with the OPDM model under different scenarios of salinity and water deficit stress during the years 2002–2008. Statistical analysis between actual and simulated yield showed the high accuracy of estimating the sugarcane yield with OPDM. The biomass estimated according to irrigation levels indicated that there was a significant difference between deficit and full irrigation. The commonly used irrigation criterion for sugarcane in Iran is safe for achieving maximum cane yields. However, it could lead to greater use of irrigation than is necessary. Results also showed that the effect of salinity on yield was not significant. The interaction effect of salinity and water stress on sugarcane yield was evaluated with OPDM and indicated that the effect of different irrigation levels on yield was more than that of the salinity. The robustness of the model and its ability to describe the effect of water stress on sugarcane yield make the model very useful for the design of deficit irrigation strategies under growing conditions. The model will be useful for irrigation strategies under water deficit and salinity conditions to guarantee an optimal response to the applied water. Therefore, other scenarios should be studied to clarify the economic and social effects due to changing crop patterns on the Amir-Kabir network.

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