Modeling of Different Irrigation Methods for Maize Using AquaCrop Model: Case Study

Modeling of irrigation methods is one of 
the most important techniques that contribute to the future of modern 
agriculture. This will conserve water as water scarcity is a major threat for 
agriculture. In this study, AquaCrop model was used to model different 
irrigation methods of maize in field trails in Al-Yousifya, 15 km Southwest of 
Baghdad. Field experiments were conducted for two seasons during 2016 and 2017 
using five irrigation methods including furrow, surface drip and subsurface 
drip with three patterns of emitter depth (10, 20 and 30 cm) irrigation. AquaCrop 
simulations of biomass, grain yield, harvest index and water productivity were 
validated using different statistical parameters under the natural conditions 
obtained in the study area. For 2016 and 2017 seasons, results of R2 were 0.98 and 0.99, 0.99 and 0.99, 0.99 and 0.97, and 0.8 and 0.73 for biomass, 
grain yield, harvest index and water productivity, respectively. The study has 
conducted that simulation using AquaCrop is considered very efficient tool for 
modeling of different irrigation applications for maize production under the existing conditions in the central region 
of Iraq.

due to the lack of irrigation water supplies as a result of climatic change and increased water demand for industrial and civil utilization [1]. Thus, food production will be effected either by the decrease in the areas currently cultivated or the inability to expand horizontally, to bridge the gap between the supply and demand for agricultural products [2].
Improving irrigation water management and increasing water use efficiency through prudent practices up to one drop is one of the best management irrigation techniques. Maize is the most important cereals planted in Iraq. The cultivated area of maize, in Iraq, for the last nine years reached around (781,322) hectares, with a production amount of (2,916,928) tons [3]. It's used for human and animals' consumption, especially poultry feeding. Maize is also one of crops most involved in several industrial products such as biofuels production [4] [5]. Maize is a summer crop that growth coincides with the hottest and driest months of the year in Iraq (July, August, and September) when it is completely lacks of precipitation [6]. Maize is a C4 crop that has high efficiency to produce much biomass rapidly with high water consumption compared to other crops. High yield of maize requires approximately 750 -900 mm water per-season; this high-water requirement due to poor water management such as the use of the traditional irrigation methods will cause a massive waste in irrigation water and lower water use efficiency [7] [8] [9] [10]. Modern irrigation methods provide more water use efficiency through water in the root zone.
Moreover, good irrigation scheduling system could be achieved by the use of sprinkler, drip, and subsurface drip irrigation systems. Recent researches [11] [12]: Documented that required water for irrigation could be decreased by 35% -55% under the sprinkler or drip irrigation with high water use efficiency compared to traditional irrigation methods. Thus, efforts should always focus on improving water management to meet maximum yield with high water use efficiency, which is the main aim of irrigation management in arid and semi-arid regions [13]. The Food and Agriculture Organization (FAO) contributed to these efforts by the development of a Crop Simulation Model (AquaCrop). This model is characterized by its simplicity, accuracy and robustness. AquaCrop model emphasizes water as a key limiting factor in crop production, which is the difference between the actual and potential yield that can be known and determine the water use efficiency under field conditions [14] [15].
In addition the advantage of AquaCrop requires minimum data which is easy to obtain or assume. Although, these standards may not be sufficient so that data should be calibrated and adjusted to local conditions, genotypes, and crop managements practice. On the other hand, input data such as plant density, irrigation schedule, and weather data are necessary to be provided by the user of this model. The engine of plant growth in this model is driven-water from the soil that has been transpired by the plant [16] [17].
AquaCrop model converts the daily crop transpiration coefficient (Tr) directly into daily biomass production by conservative crop-specific parameters. Biomass production response to water application represents the atmosphere evaporation and the CO 2 . Therefore, the reference evapotranspiration (ETo) has been adopted in this model. As a result, this model used the plant canopy instead of leave area index to calculate transpiration and separate transpiration from soil evaporation, crop production calculated based on Biomass and harvest index.
In AquaCrop model, water deficit, which ranged between field capacity [18]. It responds to the daily water equilibrium that included all influxes, infiltration, deep percolation, evaporation, transportation, runoff, and any changes in soil water content. The effect of water deficit on crop production due to poor management of crop or water could be represented in equivalents according to relative water depletion of available water in roots' zone. These equivalents are: leaves growth, sustainability of stomata conductance plant canopy aging and failure of pollination, this activities are the most sensitive to water stress. AquaCrop should calibrate according to a geographical location under different climatic conditions, soil type, phenotype, irrigation method, and crop management to improve model simulation [19] [20].
The results of several researches [21] [22] indicated that the use of AquaCrop model to manage irrigation of the maize was satisfactory and efficient, so that these studies [23] [24] [25] recommended the use of this model to simulate maize yield response to different environmental conditions and irrigation systems. The input data from field experiment used different irrigation method that contested five irrigation methods (i.e. furrow irrigation (I 0 ) surface drip irrigation (I 1 ) and subsurface drip irrigation with three patterns of emitters depth, 10 cm (I 2 ), 20 cm (I 3 ) and 30 cm (I 4 ) were used to test of AquaCrop.
The input data standard was obtained from previous studies [26] [27] was used to add test of AcuaCrop performance validity compared to the simulation of the biomass accumulation, grain yield, harvest index, and water productivity. Data that obtained from field experiment was carried out over two consecutive sea-

Study Area
Experiments were conducted in a field of a maize farmer in the Yousifya area, 15 km southwest of Baghdad, Iraq, which is located at 33˚07'84"N Latitude, 44˚18'75"E Longitude and 34m Altitude, as shown in Figure 1. The climate of this region is Engineering characterized by high temperature, intense solar radiation, without rainfall and an increase in the evaporation rates. Figure 2 shows climate variations of maize growing during the 2016 and 2017 seasons. As for soil, some of its physical, chemical and hydraulic properties are shown in Table 1 at a depth of 0 -30 cm.

Experimental Procedure and Treatments
The experiment land was prepared in terms of tillage, cultivation and leveling; then, it was divided into plots to represent the experimental unities according to a randomized completely block design (RCBD) with three replicates. The measured values was analyzed using analysis of variance (ANOVA) and significant difference were tested by Least Significant Differences method (LSD) at (0.05) level. SAS version 2012 was used [28]. The experiment included five irrigation systems that were furrow irrigation (I 0 ) surface drip irrigation (I 1 ) and subsurface drip irrigation with three patterns of emitters depth, 10 cm (I 2 ), 20 cm (I 3 ) and 30 cm (I 4 ) ( Figure 3). Kalamaras maize hybrids were planted on 7 August for 2016 and 2017 seasons with a population (62,500) plant ha −1 . Experimental unit is fertilized with the use of 60 kg·ha −1 P of Diamonium phosphate DAP fertilizer (18:46:0) with urea 200 kg·ha −1 of (N: 46%) and kg·ha −1 120 Potassium sulphate K 2 SO 4 (0:0:50%) [29].

Soil Moisture and Irrigation Management
Initial soil moisture for experimental units was measured using a gravimetric method which was converted into volumetric water content at depth 0 -90 cm and it was divided into four layers (0 -15, 15 -30, 30 -50 and 50 -90 cm) where the moisture for the four layers was calculated, this is used to represent soil water through the root zone. Moisture depletion was monitored in the root zone of the experimental units for the furrow irrigation treatments either as the experimental units for the drip irrigation (surface and subsurface). Moisture was monitored using a system of sensors (manufactured by Decagon Device Company) and    connected to the drip irrigation system that was used for the irrigation experimental unities I 1 , I 2 , I 3 and I 4 treatments irrigation frequency applied. After 50% of available water is depleted at the root zone (available water is equal to the percentage of soil moisture between the field capacity and wilting point).
The irrigation for I 0 treatment was applied by tubers with valves and flow meter to measure the amount of added water to experimental units of this treatment as in the following equation [30]: where: d = Depth of water applied (mm), FC θ = Volumetric water content at field capacity, W θ = Volumetric water content before irrigation (depletion 50% of available water), and D = Effective root depth (mm).
As for the amount of water added to the experimental units for drip irrigation treatments (I 1 , I 2 , I 3 and I 4 ), it was calculated according to the following equation [31]:  The net irrigation requirement was calculated using soil water balance as in the following equation [32]: where: P = precipitation (mm), C = capillaries (mm), I = irrigation (mm), D = deep percolation (mm), ET a = actual evapotranspiration (mm), R = runoff (mm), Δs = changes in the water storage during soil profile, C = 0 (limited contribution, water table depth = 3 m), R = 0 (no surface runoff), P = 0 (no rain), D = 0 (So irrigation at field efficiency is limited to the degradation).
Equation (3) becomes: Throughout the present study, at the beginning of the study, the soil water content was observed to be similar to its content at the end of the experiment, Δs = 0. The equation for water-consuming use becomes: Water use efficiencies were determined equation [33]: where: f WUE = field water use efficiency (kg·m 3 ), GY = total grain yield (kg·ha −1 ), WA = water applied (m 3 ·ha −1 ).

Crop Measurements
Maturity biomass and grain yield were measured on dry weight after harvesting, harvest index was calculated as the ratio of grain yield to the total above-ground dry mass of shoot. As for water productivity, it was calculated by dividing the grain yield by the amount of water given to the crop.

Model Validation and Calibration
AquaCrop model was calibrated for simulating predicting maize growth and prod-  Table 2. Then, we tested the calibrated model with two years of measured data (2016 and 2017).
The simulation was mainly focused on aboveground biomass, grain yield, harvest index and water productivity. There is a great need to calibrate the AquaCrop model, which includes the need to adjust to the original standards that apply before the model is used for simulation prediction. Calibration is done by including datasets on: climate, soil, crop and field management practices and also we need to modify some inputs such as planting date, plant population's plant growth stages duration [18] [34].

Statistical Comparison
Five Statistical measurements were applied to test the performance of the model and compare the simulated and measured results: where: Si and Mi are simulated and measured values; respectively, and n is the number of observations. 2) Coefficient of Determination (R 2 ):

Results and Discussion
Simulation values of AquaCrop model were compared with data obtained from the field experiment which was carried out for two seasons (2016 and 2017). This included five treatments for irrigation of maize under the natural conditions of the central region of Iraq, and the cultivation of hybrid Kalimeras (F1). Table 3 show the results of the simulated and measured values of the parameters for Aqua Crop, that was used for calibration the model, shows that the range of the calibrated values is well matching within the recommended vicinity of the simulated and the measured values and illustrated that the average calibrated values of the parameters are close to the simulated value for all irrigation treatments in this study for 2016 and 2017 seasons. The values of the statistical analysis confirmed the accuracy of the calibration of AquaCrop in its simulation of the biomass, grain yield, harvest index and water productivity in the ( Table 3). The model shows high correlation (1:1) between simulated and measured values. Generally, the correlation values (R 2 ) were (0.98 and 0.99) for Biomass, (0.99 and 0.99) for grain yield, and (0.99 and 0.97) for harvest index for the two seasons of 2016 and 2017; respectively. While the (R 2 ) for water productivity was (0.8 and 0.75) for the 2016 and 2017; respectively this indicates that the model has predicted a high degree of accuracy with respect to Biomass, grain yield and harvest index and this was confirmed by the   Table 4 and Figures 4-8 show the percentage of deviation between the simulated and measured values, which ranged between 1.3% in I 2 and 2.4% in I 3 and 0.9% in I 1 and 2.0% in I 3 treatments for biomass in 2016 and 2017seasons, respectively. As for the grain yield, it ranged between 2.5% in I 2 to 5.5% in I 1         Simulating the final harvest index for all treatments are shown in Table 5 and Figures 9-13. Deviation ranged for the harvest index values between 3.4% for I 1 treatment in 2016 as the highest value and lowest values of 0.1% for I 0 in 2017 season. The deviation from the harvest index values is very low due to the matching between the values of biomass and grain yield. Biomass and grain yield were slightly underestimating for all treatments. However, it was well matching within the recommended vicinity of the default and the measured values. As for water productivity, it showed high-value deviations between dated and measured that ranged between +38.3% for I 0 treatment in 2016 season and −32.7% in 2017 season. Deviation values for water productivity were negative for some irrigation treatments I2 (−9%), I 3 (−30.22) and I 4 (−24.2) in 2016 season and I 2 (−14.6%), Table 5. Simulation values were compared with the measured value and standard deviations of harvest index and water productivity (kg·m −3 ) for maize under different irrigation methods for the 2016 and 2017 seasons.    increase in the deviation values between the simulated and measured values of water productivity may be due to the increased water requirement for I 0 treatment, and as a result of losses due runoff, deep percolation and evaporation compared to subsurface drip and the low water productivity in surface drip irrigation I 1 treatment This because the water droplets that fall on the surface of the soil are exposure to evaporation because the soil texture is heavy and does not allow the water to percolate in the depths of the soil quickly and due to the high temperature and exposure of the soil surface to direct sun radiation. Meaning that the evaporation of water is faster than its percolation to root zone, As for the furrow irrigation I 0 treatment, the soil receives a sufficient amount of moisture because the water column cause a pressure that helps to quickly Percolation the wash to the depths of the soil where the root zone. However, the disadvantage of this method are losses due to surface run off deep percolation and evaporation from the soil surface Therefore water requirements in crease which reduces the water use efficiency for this method and since the efficiency of water use is the a main goal for the irrigation process in arid and semi-and regions. Water productivity is a measure of water use efficiency, and the efficiency is determined by their two factors the amount of irrigation water used and the amount of grain yield produced according to the Equation (6), as the efficiency to water use decrease as the amount of water used increase and this is what happened with the furrow irrigation method, or the yield may decrease by a high percentage, despite the decrease in the amount of water used, which cause a decrease in the efficiency of water use, which reflects on water productivity. [41] [42]. Thus, AquaCrop model is efficient in managing the irrigation of maize and predicting the outputs that will be obtained. Figures 14-16 show that the simulated values of biomass, grain yield and harvest index had been concentrated to be close to the line 1:1 and this explains the overestimation or underestimation in yield between simulated and experimental values The low mean value of biomass, grain yield and harvest index in    the surface and subsurface drip irrigation I 1 and I 2 treatments are due to decrease of moisture, lead to disturbance such as photosynthesis, respirator, erosion, water absorption and nutrients. It also affects the cellular division that leads to a decrease in the number of divided cells and prolong the period needed to divide,

Conclusion
This study showed that the subsurface drip irrigation with a depth of 20 cm I 3 was the best among other irrigation methods in terms of yield and water use efficiency, which are the two main objectives of the irrigation process. The results of this study revealed that the AquaCrop model fits to predict biomass, grain yield and harvest index with a high degree of reliability under different irrigation methods through the agreement between simulated and measured value for biomass, grain yield and harvest index is considered satisfactorily. Then, it is concluded that the AquaCrop model is an efficient tool to help and support decision-makers for irrigation management strategies. The results indicated that the deviation of the measured values from the simulation was very low with respect to biomass, grain yield and harvest index, ranging between (1.3% to 2.4%) and (0.2% to 2%), (2.5% to 5.5%), (2.1% to 4%) and (0.7% to 3.4%), (0.1% to 2.5%) for the 2016 and 2017 seasons; respectively. This indicates that AquaCrop model simulates well the conditions in which water is the limiting factor for crop production. While the deviation value of water productivity ranged between (−30.2% to 38.3%), (−29.7% to 35.0%) for 2016 and 2017 seasons; respectively. Statistical procedure results of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Coefficient efficiency (E) and Agreement index (d) confirm that AquaCrop has a high ability to simulate biomass yield, grain yield and harvest index with high accuracy