1. Introduction
The scarcity of water resources is one of the major challenges in the world, particularly for the main fresh water consumer, i.e., agriculture. In the context of the increasing shortage of water resources, improving crop water productivity (producing more crop per drop) will contribute to alleviating the water crisis, especially in arid and semi-arid regions [
1]. In the Guanzhong region, water resources are limiting for crop growth and are mainly dependent on monsoon precipitation, where above 60%–70% of the precipitation comes in around June and August. This is not an appropriate time to meet water demands from a corn crop [
2]. With the variability and uneven distribution of precipitation in China [
3], the rainfall pattern is also irregular in the Guanzhong plain [
4]. This insufficient and irregular rainfall generally results in water scarcities and droughts. Therefore, water scarcities and uneven rainfall distribution are the primary limitations on the growth of agriculture in northwest China. In the arid and semi-arid region, the farmers can apply supplementary irrigation at different crop growth stages to fulfill the deficiency of rainfall and avoid the effects of water stress on the plants. Irrigation scheduling based on supplementary irrigation can increase grain yield under scarce water conditions [
5,
6,
7]. Supplementary irrigation provides the required amount of water at different crop growth stages to reduce the impacts of water shortage on plants. However, irrigation planners have no precise methodology to determine the amount of irrigation water to apply at which specific crop stage. Previous studies showed that supplementary irrigation amount was selected based on previous experiences. Currently this practice has been replaced by crop system models to improve the estimation of irrigation amount.
Many crop system models have been developed, such as FAO Aqua Crop model [
8], Crop System [
9], APSIM (Agricultural Production Systems Simulator) [
10], RZWQM (Root Zoon Water Quality Management) [
11], and DSSAT (Decision Support System for Agro-Technology Transfer) [
12]. These crop system models have been used for the simulation of agriculture practices, with DSSAT widely used in the world for the simulation of biomass and grain yield production. CERES-maize model [
13,
14,
15] included in DSSAT is a multi-purpose model that has been used to evaluate crop growth and development, such as phenology (mainly anthesis and physiology maturity dates), biomass production and yield [
12]. CERES-maize model predicts yield and soil moisture at different depths accurately under full irrigation treatments [
16,
17,
18,
19,
20]. CERES-maize model has also been used for determining optimum sowing dates using long term weather data and for yield prediction under different climate scenarios [
21,
22,
23,
24], and yield prediction response to the variability of climate [
25,
26,
27]. DSSAT has been extensively used for multiple purposes in some regions of China, such as Northeast China, [
28], in Northern China [
29] and in Northwest China [
30,
31].
There are no studies conducted in the Guanzhong region of northwest China for maize crop production using the irrigation strategy scenario and sowing date under different climatic condition. Thus, it is necessary to identify the irrigation strategy, critical crop growth stages and sowing date under the variation of precipitation amount during the growing season for securing crop productivity. In this area, the main aim is to reduce crop water stress and improve agricultural production by employing supplementary irrigation. The objectives of this study were thus (1) to calibrate and evaluate the CERES-maize model in the Guanzhong Plain. (2) to determine the optimum sowing date, crop growth stages and irrigation amount to improve maize yield under rain-fed and irrigation conditions using the CERES-maize model.
2. Materials and Methods
2.1. Field Experiment
The experiment was conducted at the Key Laboratory of Agricultural Soil and Water Engineering (
Figure 1) (34°18′ N, 108°24′ E, 506 m above sea level), Northwest A&F University, Yangling, Shaanxi Province, China. The study area is in a sub-humid to a semi-arid climate zone with a mean annual temperature of 12.9 °C and a mean annual maximum and minimum air temperatures of 40 °C and −17.4 °C, respectively. The total annual sunshine duration was 2196 h, with annual precipitation of 548 mm. Daily maximum and minimum air temperature (°C), precipitation (mm d
−1), and sunshine hours for years 2012 to 2015 were obtained from the Yangling meteorological station (
Figure 2a), which is located beside the field experimental site. Historical weather data (1961–2011) was collected from the Chinese meteorological administration [
32] against the weather station Wugong, located beside the research station (
Figure 2b).
The soil properties were determined by collecting the soil samples from the different locations of the experimental plots at five soil depths between 0 and 250 cm depth.
The soil was a brown loess loam, with on an average 26% sand, 51% silt and 23% clay content. Average soil bulk density was 1.36 g cm
−3, average phosphorus (P) was 0.016%, average potassium (K) was 1.46%, and average nitrogen (N) was 0.056%. The average field capacity (FC) and permanent wilting point (PWP) of the root zone soil profile were 27.9% and 12.7%, respectively. Further detail of soil properties information is mentioned in
Table 1.
2.2. Crop Management and Irrigation
The Maize cultivar Wuke-02 that is commercially cultivated in the area was used in the experiments. Crop sowing during 2012, 2013, 2014 and 2015 growing season were carried out on 19th, 23rd, 20th, and 15th of June respectively. The seeding density was six plants m−2 with row spacing of 50 cm. The dimensions (length × width × depth) of the experimental plot are 3.0 m × 2.2 m × 3.0 m. According to the local agricultural management, 180 kg N ha−1 and 120 kg P2O5 ha−1 were applied during crop planting.
The experiment was performed under moveable rainfall out shelter. Large weighing lysimeter (3 m × 2.2 m ×3 m) fitted with data loggers were installed in experimental plot to measure crop evapotranspiration (ETc) (with precision of 0.021 mm) (
Figure 3). Soil and crop management conditions in the lysimeter were similar to other experimental plots. The ETc from the lysimeter was measured on hourly basis and then added to get the daily value. The irrigation was scheduled when soil moisture content of the lysimeter dropped to 65% of field capacity. The lysimeters received the full irrigation (CK), whereas other irrigation treatments received, i.e., 80% and 60% of the CK full irrigation which represents a moderate and severe soil moisture deficit condition. The flood irrigation method was used in this study. However, irrigation application was carried out with pump fitted with flow meter to ensure the same quantity of irrigation application applied to each experimental plot. Nine deficit irrigation treatments were designed with three replicates for experiments in 2012 and 2013 using the partial orthogonal experimental design method. The design scheme and irrigation amount are specified in
Table 2a,b.
2.3. Water Use Efficiency
Water use efficiency calculated by a given formula
where,
WUE is defined as (
Y) grain yield (kg ha
−1) per unit seasonal crop evapotranspiration
ETc (mm). Seasonal crop evapotranspiration calculated for every irrigation treatment using water balance approach [
33], which was analyzed seasonally and annually using the following equation
where
I is the irrigation amount (mm),
P is precipitation (mm),
R is the surface runoff (mm), which was considered negligible, due to the cemented boundary constructed on each side of plot for the remote rainfall shelter experiment,
D is the downward flux below the crop root zone (mm), which was ignored because the bottom of each plot was waterproofed in the rain-out shelter, and Δ
S is the change in soil water storage (mm).
2.4. Field Measurements
Plant leaf area index was determined by using the SunScan-SS1 canopy analyzer (Delta-T Company, Burwell, Britain). Leaf area measurements were made eight times during the growing season at different growth stages. For each irrigation treatment phenology was recorded by visiting the fields four times a week. The emergence phase was observed by the visual number of plant leaf in the field. Emergence, anthesis, and maturity stages were noted in the form of the day of the year. Data on grain yield and aboveground biomass at maturity was also collected. All the cobs from each treatment plot were harvested at maturity, air dried and threshed to obtain the grain yield. Finally, yield was converted kg ha−1. For aboveground biomass, all the plants were harvested closer to the ground and fresh weight was measured. Sub samples was taken and dried at 75 °C for 48 h to get the dry aboveground biomass. Soil moisture content was determined by Theta Probe ML2x (Delta-T Devices Ltd., Cambridge, UK) installed at different soil depths from 0–250 cm.
2.5. CERES-Maize Model Description and Calibration
In the present study, the CERES-maize crop simulation model (CSM) was used, which is part of the Decision Support System for Agro Technology Transfer (DSSAT) Version 4.6 [
34]. DSSAT models can simulate the growth of 30 different crops [
12,
35,
36]. The model takes the input, which includes cultivar type with specific coefficients, [
12], weather data on a daily basis, soil property information, initial soil conditions, agronomic practices, including planting density and planting dates among others. The basic crop data, emergence, anthesis and physiological maturity dates, leaf area index, final grain yield and aboveground biomass, were selected from the full irrigation treatment (CK) during the four growing seasons of 2012, 2013, 2014 and 2015 for model calibration and the estimation of cultivar coefficients of the maize crop. DSSAT-GLUE (generalized likelihood uncertainty estimation) [
37] package was used to determine the genetic coefficient for summer maize Wuke-02. GLUE tool was run 3000 times to obtain the best cultivar coefficient. If these coefficients are not satisfied with the result of simulated and observed values, then trial and error method [
38] was used to improve the simulation results, based on statistical indices (
R2, RMSE, nRMSE and
d-index).
2.6. Statistical Model Evaluation
The performance and evaluation of the DSSAT model was evaluated using the remaining irrigation treatments (T2–T9) during the 2012–2013 growing season. In this study the evaluation of the model was generally determined by different statistically analysis
R2,
d-index value [
39], and (nRMSE) normalized root mean square error between simulated and observed data.
where,
n = number of observations,
= predicted value for the ith measurement,
= observed value for the ith measurement,
= the overall mean of observed values,
=
−
and
=
−
. The normalized root mean square error (nRMSE) calculated by using the following equation
where
RMSE = root mean square error, which was calculated by using the following equation:
Higher d-index value and the lower nRMSE value indicated a good fit between the simulated and observed data.
Generally criteria of nRMSE are categorized in four standards for understanding relationship between simulation and observed data: nRMSE < 10% was considered excellent, 10% < nRMSE< 20% was considered good, 20% < nRMSE < 30% was considered fair, and nRMSE > 30% was considered poor [
40].
2.7. Crop Cultivar Coefficient
In this study, the calibration of six cultivar coefficients obtained from the GLUE program for maize cultivar Wuke-02 are described in
Table 3. P1: Degree days (base: 8 °C) from emergence to end of the juvenile phase, P2: Photoperiod sensitivity coefficient (0–1.0), P5: Degree days (base 8 °C) from silking to physiological maturity, G2: Potential kernel number, G3: Potential kernel growth rate mg/(kernel day), PHINT: Degree days required for a leaf tip to emerge (phyllochron interval) (degree days). The cultivar coefficient values are in the ranges found in different studies of maize crop [
29,
41].
2.8. Scenario Simulation
2.8.1. Optimum Sowing Date Treatments
The seasonal program was used to simulate the grain for different sowing dates [
42]. The seasonal analysis was conducted using the long-term 55-year climatic data (1961 to 2015), with different crop parameters, such as crop management data, cultivar, fertilizer application rates and others to analyze the grain yield for multiple sowing dates. In this study, the purpose of seasonal analysis is to determine the best sowing date in response to different irrigation strategy scenario in Guanzhong plain climatic condition. Sowing dates were selected from 19 May to 28 July with 10 days intervals for the analysis of suitable sowing date. Box plots were used for the representation of simulation results.
2.8.2. Irrigation Strategy
The adjustment of irrigation treatments was based on the different crop growth stages emergence, flowering, grain filling, and physiology maturity stage with different irrigation treatments. Six irrigation scenarios with different combinations of crop growth stages were carried out in the simulation: Rain-fed, single irrigation, double irrigation, triple irrigation, quadruple irrigation and automatic irrigation. Each irrigation contained 100 mm of water, and automatic irrigation was 80% of the deficit level (
Table 4). Crop management practices in the simulation option were kept as standard practice in the local field.
4. Conclusions
CERES-maize model proposed that early and delayed sowing date from 24 June is not beneficial for maximum yield production. The model simulation for optimum time and irrigation amount was carried out with different irrigation scenarios at different growth stages resulting in the optimum timing of irrigation application. This is during the flowering and grain filling period with optimum irrigation amount of 200 mm during the crop growing season. It is important for the crop to have access to water during these two stages as lower irrigation or precipitation leads to crop stress. Two irrigation applications for these crop growth stages are essential and could lead to the similar yield obtained from 3 or 4 irrigation applications that have no crop stress. The long-term simulation of water use efficiency is higher in a single irrigation of 100 mm at the flowering stage and also higher in two irrigation application when applied 100 mm at the flowering stage and 100 mm at grain filling stage.
Overall, the application of the CERES-maize model demonstrated that the negative effects of less rainfall or water availability on the agricultural production can be controlled by the systematic consideration of critical crop growth stages, sowing date and amount of irrigation water. Furthermore, this study serves to improve our understanding of how different irrigation strategies can be used to optimize sowing date, crop growth stages and maize yield within the region.