AquaCrop
Climate, soil, crop characteristics, and management data (irrigation and agronomic practices) of the site were supplied as input to
AquaCrop. In
AquaCrop, crop phenology depends on the nature of the cultivar and air temperature (
Raes et al. 2009;
Steduto et al. 2009). The calendar day option in the model was used because corn was grown in favorable temperatures in the study area. Calibration for fertility stress was not considered in the model because the corn was assumed to be grown under optimal fertility. Model default calibration data sets were used as the starting point. However, most of the cultivar and site-specific crop parameters (none conservative) were entered in the model as per the collected information from the corn experiment. Table
2 shows some important crop data used for calibrating the
AquaCrop. Canopy cover is one of the most important crop parameters in
AquaCrop because it limits the amount of water transpired, which indirectly determines the biomass (
Steduto et al. 2009). Canopy cover at emergence was adopted from Heng et al. (
2009) and Hsiao et al. (
2009). Since plant density in the study area varied with irrigation treatments, adjustments to default maximum canopy cover to match actual field conditions was made as presented in Steduto et al. (
2009). The canopy cover was estimated from LAI and extinction coefficient (
), as presented in Hsiao et al. (
2009).
However, owing to variation in planting density, a typical maximum canopy cover (estimated from LAI) that is representative of the treatments was selected to simplify complexities. The maximum canopy cover was then set to 80%.
Estimated ranges of normalized crop water productivity values for C4 crops (
) are available in the literature (
Raes et al. 2012). For this analysis, the normalized crop water productivity values that were adopted in Hsiao et al. (
2009), Heng et al. (
2009), and Paredes et al. (
2014) (
) were used. Similarly, crop evapotranspiration (ETc) at maximum canopy cover for no stress was also taken from Hsiao et al. (
2009). The cultivar grown in the experimental site had a maximum harvest index (HI) of up to 59%. However, 52% was taken as the most common HI value for good growing conditions, which is higher than the value (48%) used in Hsiao et al. (
2009). This shows that the experimental cultivar was different from those presented in Hsiao et al. (
2009). Water stress may affect HI depending on the timing, duration, and severity of the stress (
Raes et al. 2009). Model default values for corn were adopted for the effects of water stress on HI. Reports indicated that water stress could negatively affect pollination, fruit setting or abortion, or under filling of young fruits depending on the timing and extent of the stress (
Steduto et al. 2012).
The water stress coefficients (for leaf expansion, stomatal conductance, and leaf senescence) are considered to be conservative (globally applicable once calibrated), meant for use under various climate and soil conditions (
Hisiao et al. 2009). However, slight adjustments were necessary for the model to reproduce our measured data. Adjustment for these parameters was carried out after repeatedly comparing the measured against the simulated data, taking into account the available documented information. After adjusting the water stress coefficients, simulated yield, biomass, and ET were comparable to measured values. However, the upper level for leaf senescence obtained based on iteration in this study slightly deviated from the information for maize presented in Hsiao et al. (
2009) and Steduto et al. (
2012). Some of the possible reasons for this include (1) cultivar differences; (2) soil heterogeneity; (3) method used for estimation of the soil water characteristics [pedo-transfer function (e.g.,
Saxton et al. 1986) based on soil texture]; (4) limited LAI measurements together with planting density differences among the treatments; and (5) model version differences. For example, Hsiao et al. (
2009) hinted that version differences might contribute to the disparity in some of the documented conservative parameters. Overall, since our interest was to develop indicative optimal irrigation strategies that suit corn in the study area, measured biophysical data with the documented information in the literature were adequately used to characterize the actual crop in the model.
ET was estimated using the soil-water-balance method [Eq. (
1)]:
where
= irrigation (mm);
= precipitation during sampling period (mm);
= runoff or runon during sampling period (assumed negligible, mm); SW1 and SW2 = total profile soil water (mm) between first and last neutron probe readings, respectively; and
= drainage during sampling period. Drainage was estimated as presented in Miller and Aarstad (
1972) and Klocke et al. (
2011).
The sampling period refers to the period between the first and last neutron probe readings during the growing season. Data from full irrigation treatment for the 2011 cropping season were used for model calibration. Four treatments each in 2010 and 2012 were used to validate the
AquaCrop model (Table
3). Treatments that were dry (not representative of the actual corn growing conditions) were not included.
Goodness-of-fit statistics based on the percent of deviation (d) [Eq. (
2)], normalized root-mean square error (NRMSE) [Eq. (
3)], index of agreement (IAg) based on Wilmott (
1982), and coefficient of determination (
) were used when testing model performance:
where
= simulated and
= measured values.
= mean of the measured value; a
value close to zero indicates better agreement between the measured and simulated values. The root-mean square error (RMSE) is a statistical indicator of model uncertainty; a NRMSE value close to zero indicates excellent agreement and, hence, good performance of the model. IAg varies from below zero to 1. Values closer to 1 indicate better agreement between simulated and measured values.