Results Top

Model calibration and validation

The input crop parameters used for calibrating the CropSyst model are shown in Table 4. The measured and simulated ‘Askari’ and ‘Yaghooti’ yields for calibration (2012) and validation (2013 and 2014) processes in the experimental areas are presented in Table 5. The measured yields show that SI in May (T4A & T4Y) increased yields for all amounts of SI treatments compared to those obtained from the single SI application treatments (T1A & T1Y), (T2A & T2Y), and (T5A & T5Y). This was due to the fact that application of SI in May coincides to the flowering and fruit set stage of grapevine (Table 1), when berries begin to enlarge in size.As it is shown in Table 5, treatment T3A (single SI in March + single SI in April) for all amounts of SI treatments, resulted in a positive effect on the measured ‘Askari’ grape yields (late ripening cultivar) in comparison with other treatments. However, the effect of treatment T3Y was negative on the measured ‘Yaghooti’ grape yields (early ripening cultivar).

A comparison of trends between the measured and simulated yields (with the exception of the T3Y treatment) indicated that the CropSyst model is able to accurately simulate the behavior of SI application on grape yield (see Table 5). However, this model fails to correctly simulate the negative effect of treatment T3Y on the ‘Yaghooti’ grapevine yield. As shown in Table 5, the measured ‘Yaghooti’ grape yield in this treatment was lower; however, the simulated yield was higher than other treatments.

Figs. 1a to 1f show the relationship between the measured and simulated yields in the calibration and validation processes compared with line 1:1. The scattering of the points around the 1:1 line, the straight-line equation, and the high coefficient of determination (R₂), shows that the model simulated grain yield with a high degree of reliability. In the calibration stage, as illustrated in Figs. 1a and 1d, the NRMSE and d values between the measured and simulated yields for all treatments in ‘Askari’ were 0.12 and 0.97, respectively, and for single SI treatments in ‘Yaghooti’ were 0.11 and 0.97, respectively. In the validation process, Fig. 1b and 1c in ‘Askari’ for all treatments, and Figs. 1e and 1f in ‘Yaghooti’ for single SI treatments, showed that the simulated and measured data in validation stage agreed very well for the two cultivars with the acceptable values of NRMSE and d.

The measured and simulated data related to T3Y in ‘Yaghooti’ grapevine are not shown in Figs. 1d, 1e and 1f and neither were taken into account to NRMSE and d calculations due to the fact that the Cropsyst model was not able to simulate the adverse effect of applying the large amounts of SI during the vegetation stage of this early ripening cultivar and consequently it was not able to accurately simulate T3Y yield.

 

Table 3. Annual cost function for ‘Askari’ and ‘Yaghooti’ grapevines at the different values of precipitation to reference evapotranspiration ratios (P/ETO)

 

Table 4. Crop parameters for ‘Askari’ and ‘Yaghooti’ grapevines

 

Table 5. Measured and simulated ‘Askari’ and ‘Yaghooti’ grape yields for a three-year period in the experimental area

 

Table 5. (Continued)

 

 

Table 6. Summary of statistical analysis for model test in rainfed conditions (no supplemental irrigation) in the study regions, 2008-2016

 

 

Simulation of the rainfed grapevine yield in different P/ETO groups

The values of the NRMSE and d between the measured and simulated yield are shown in Table 6 in the validation stage for the study regions. The minimum and maximum values of the NRMSE and d are 0.08 to 0.17 and 0.9 to 0.99, respectively, and it is clear that the measured and simulated yields are in good agreement each other.

Fig. 2 shows the simulated ‘Askari’ and ‘Yaghooti’ yields, respectively, in different P/ETo groups. Comparing the grapevine yield between treatments by one-time SI application indicated that the SI in May (treatments T4A&T4Y) significantly increased the grape yield in comparison with those obtained in the T1A & T1Y, T2A & T2Y, and T5A & T5Y for all amounts of SI application treatments. As shown in Figs. 2a to 2c, the mean grapevine yield for ‘Askari’ for all amounts of SI application treatments obtained from treatment T4A was about 14% at P/ ETo>0.6, 27% at 0.2<P/ETO<0.6 and 17% at P/ETo<0.2, which was higher than the average grapevine yields obtained from treatments T1A, T2A and T5A. Furthermore, the two-time applications of SI for ‘Askari’ in both March and April (treatment T3A) significantly increased the average simulated grape yield for all amounts of SI applications, approximately 12% at P/ETo>0.6, 14% at 0.2<P/ ETo<0.6 and 21% at P/ETo<0.2 compared with the T4A treatment (see Fig. 2). The results in Figs. 2a to 2c show that, in the case of ‘Askari’, for all SI application times and amounts, there was a mean yield increase of about 25%, 31% and 45% for the P/ETo>0.6, 0.2<P/ETo<0.6 and P/ETo<0.2 areas, respectively, compared with the I4A (no SI) treatment. The results also indicate that in most cases the difference between the grapevine yield in T1A, T2A and T5A in each amount of SI treatment and also the difference between the grapevine yield in treatment I4A and the treatments T1A, I1A and T5A I1A were not significant in all P/ETo group. Figs. 2d to 2f shows that for the treatment T4Y, there was a significant mean yield increase of about 14% at P/ETo>0.6, 19% at 0.2<P/ETo<0.6 and 21% at P/ETo<0.2 compared with average yields obtained from treatments T1Y, T2Y and T5Y. However, the differences between the ‘Yaghooti’ yield in T3Y and T4Y treatments were not significant. As the results indicated, the mean percentage of increase in the ‘Yaghooti’ yield for treatment T3Y for all amounts of SI application treatments was only about 2% compared with treatment T4Y. The results also indicate that in the study regions with 0.2<P/ETo<0.6 and P/ETo<0.2 the differences between the yield in I4Y and T1Y I1Y and T5Y I1Y were not significant. The results from Figs. 2d to 2f show that, in the case of ‘Yaghooti’, there was a mean yield increase for all SI times and amount treatments of about 22%, 26% and 35% for the P/ETo>0.6, 0.2<P/ETo<0.6 and P/ ETo<0.2 areas, respectively, compared with the I4Y (no SI) treatment. Additionally, by increasing the amount of SI application in each treatment, the yields for the two grapevine cultivars were increased.

Multiple regressions between the simulated grape yield and the effective rainfall + SI in each P/ETo group were obtained to simplify yield estimation under different annual precipitation and SI conditions (Table 7). In all equations, a coefficient of determination R²>0.8, and a p<0.05, indicate that these multiple regressions can estimate the grape yield with a high level of accuracy. Furthermore, the second order polynomial equations indicate that the grape yield at first increases by increasing irrigation + rainfall, up to a maximum potential yield, after which it decreased by increasing irrigation + rainfall due to water logging, nutrient leaching and possible disease as a result of extra soil water content in the plant root zone. In the case of ‘Askari’, the maximum potential yields for P/ ETo>0.6, 0.2<P/ETo<0.6 and P/ETo<0.2 were about 6700 kg ha-1, 4900 kg ha-1 and 3960 kg ha-1, respectively. Similarly, for ‘Yaghooti’, the maximum yields at P/ETo>0.6, 0.2<P/ETo<0.6 and P/ETo<0.2 were about 4300 kg ha-1, 3600 kg ha-1 and 2900 kg ha-1, respectively.

 

Soil water content simulation

Variation of mean volumetric soil water content at 0–90 cm depth during three growing seasons for all times and amounts of SI are shown in Figs. 3a to 3e. The values of the NRMSE and d between the simulated and observed parameters were 7% and 0.97 respectively, which show a good agreement between the simulated and measured volumetric soil water contents. As it is shown in these Figures, the values of soil water content reached to its maximum level at the time of SI application and after that it was decreased gradually until the end of the growing season. The values of soil water content in I1 treatment (1000 L SI) in all times of SI application were higher than those obtained in other treatments (I2, I3 and I4) and decreased by decreasing the amount of SI. The soil water content in all amounts of SI at the end of the growing season had almost the same value except the I4 treatment that had the lowest value of soil water content. As it was shown in Fig. 3d, in treatment T4 the value of soil water content from May 15^th to June 15^th was higher than those obtained in other single SI treatments (Figs 3a, 3b and 3e), in which this period coincided to the flowering and fruit set stage in both grapevine cultivars (Table 1), and consequently caused a higher grapevine yield in this treatment (T4) as compared to those obtained in other single SI treatments (Table 5). In treatment T3 (single SI in March 25^th + single SI in April 25^th) the value of soil water content from March 25^th to May 15^th [vegetation stage in ‘Yaghooti’ (Table 1)] remained at high level and as it was mentioned before, it caused high vegetation growth in an early ripening cultivar like ‘Yaghooti’.

 

 

 

 

 

Table 7. ‘Askari’ and ‘Yaghooti’ grape yield functions for different amounts of annual rainfall plus SI

 

Table 8. The optimum equations of supplemental irrigation (SI) for ‘Askari’ and ‘Yaghooti’ grapevine cultivars under different precipitation to reference evapotranspiration ratios (P/ETo)

 

Economic analysis of SI for grapevines under land limiting conditions

Production and production cost functions

The annual cost and grape production functions for each group of P/ETo ratio and grapevine cultivar are presented in Tables 3 and 7, respectively. The price of water, which varies in each region depending on abundance and ease of access, includes water transportation and application. The slope of production cost functions depends on the price of water while the intercept depends on the fixed and annual costs in each area.

 

Optimal SI in land limiting conditions

For both ‘Askari’ and ‘Yaghooti’ cultivars, Table 8 presents multiple linear equations for obtaining the optimum amount of SI in various effective annual precipitation conditions, different water costs and different selling prices of the harvests. Fig. 4 shows the relationship between the SI (mm) and water cost in different annual precipitation conditions, whereas the SI is essential for benef itting from rainfed grapevines and gaining an acceptable yield. The results show that by increasing annual precipitation as well as water cost, the optimum SI decreases. For the ‘Askari’ cultivar (Fig. 4a-c) with minimum annual precipitation related to each region (100 mm), the SI is economical when the water price is up to about 0.95 USD m^-3. However, for ‘Yaghooti’ grapevines (Fig. 4e-f) under the same conditions, the water price is up to 1.95 USD m^-3 for SI to be economically feasible.

Fig. 5 shows the relationship between the net profit and applied SI water at different unit water prices and in the annual 200 mm rainfall, which is less than the average annual precipitation of Fars province. The results indicate that changes in the cost of water affect the profits from SI. Thus, in order to achieve higher net profit, the amounts of optimum SI at a given level of annual precipitation decrease by increasing the price of water. As shown in Fig. 5, by increasing the amounts of SI application, the net benefit increases up to a maximum point for each water price, after which it decreases. These results may be related to the fact that by increasing the amount of SI, the grape yield is also increased and the profit from this increase can compensate for the cost of water. In addition, after the maximum point, the net benefit decreases due to the increase in the water cost that compensates for the benefit attained from the production of a higher yield. The results from Fig. 5 indicate that, by increasing the water price by about 50% and 30%, the average attainable net benefits of all P/ETo groups for the ‘Askari’ grapevine cultivar decrease by about 54% and 31%, respectively, while for the ‘Yaghooti’ cultivar the net benefits decrease by about 18% and 6%, respectively. These results demonstrate that the impact of the cost of water for SI application would be lower by increasing the selling price of the grapevine yield.

The amount of net benefit obtained from ‘Askari’ and ‘Yaghooti’ without SI application at the regions by P/ ETo>0.6 was the highest (1143 and 1905 USD ha^-1 for ‘Askari’ and ‘Yaghooti’, respectively) due to the production of more grapevine yields in the regions by higher precipitation and lower evapotranspiration. The net benef it in the regions by 0.2<P/ETo<0.6 (762 and 1381 USD ha^-1) and P/ETo<0.2, (667 and 1071 USD ha-1) ranked in second and third places, respectively (see Fig. 5). The results also indicate that under the same amount of SI application and annual rainfall, for ‘Askari’ grapevine in the average optimum SI for each regional group (SI=124 mm in P/ETo>0.6, SI=102 mm in 0.2<P/ETo<0.6 and SI=68 mm in P/ETo<0.2), the average net benefit decreased about 15.6% when water cost increased from 0.47 USD m^-3 to 0.95 USD m^-3 (Fig. 5a-c). For ‘Yaghooti’, in the average optimum SI for each regional group (SI=160 mm in P/ETo>0.6, SI=112 mm in 0.2<P/ETo<0.6 and SI=71 mm for P/ETo<0.2), the average net benefit decreased about 30% when water cost increased from 0.95 USD m^-3 to 1.9 USD m^-3 (Fig. 5d-e). The results also indicated that under the same amount of SI application and annual rain-fall, the net benefit obtained from ‘Yaghooti’ was higher than ‘Askari’ due to the higher selling price of ‘Yaghooti’. This indicates that the selling price of the yield can play an important role in the determination of economically feasible SI in rainfed areas.

 

The LAI increased slowly in the initial stages and more rapidly reached its peak at about 3.4 m²/m² under DI and 4.6 m²/m² under SDI. Also, the H increased slowly at the initial stages and more rapidly at the mid-season stage, with the tallest plants (229.2 cm) under SDI at the mid-season stage. Moreover, the D also increased during the growing season and peaked during mid-season with values of 3.2 cm under DI and 3.6 cm under SDI. Between the irrigation systems, the lowest values of LAI, H and D occurred under DI (Table 4 and Fig. 9) as reported in Chuanyan & Zhongren (2007).

The highest yield (biomass) was 81.90 ton/ha under SDI compared to only 63.21 ton/ha under DI. Also, the WUE (kg/m³) of corn was 8.32 under SDI and 6.67 under DI (Table 4). This better performance under SDI could be explained by the conservation of optimal moisture status in the root zone, which favoured water and nutrient up-take by the crop (Zotarelli et al., 2008; Badr et al., 2010).

In summary, the results of this study showed that the lowest ET_c-act for corn occurred under SDI, mainly because of reduced evaporation (E) from the soil surface in comparison to the DI system. Consequently, higher T_p and lower E rates were observed under the SDI system. The T_p/ET_c-act started from 0 at sowing and peaked at the mid-season stage when the LAI was also at peak levels. The DI and SDI systems had different influences on the LAI of corn during the growing season. A better unders-tanding of these components (T_p, E, E/ET_c-act and T_p/ET_c-act) can provide important insights to water saving under irrigated corn production. The highest LAI occurred un-der the SDI system. Overall, the SDI system reduced soil evaporation loss and increased the efficiency of water consumption; it also produced superior biomass yield. Thus, the SDI system is more productive and would better increase WUE than the DI system.

 

AcknowledgmentsTop

The authors would like to acknowledge the support provided from: The Drought Research Center, Shiraz University; the Center of Excellence for on-Farm Water Management. The authors also wish to thank Professor Davar Khalili for his valuable comments and suggestions for this research.