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Quantifying the impact of irrigation on groundwater reserve and crop production – A case study in the North China Plain
Europ. J. Agronomy 70 (2015) 48–56
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Quantifying the impact of irrigation on groundwater reserve and crop production – A case study in the North China Plain Hongyong Sun a , Xiying Zhang a,∗ , Enli Wang b , Suying Chen a , Liwei Shao a a Key Laboratory of Agricultural Water Resources, The Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, The Chinese Academy of Sciences, Shijiazhuang 050021, China b CSIRO Agriculture Flagship, Canberra, ACT 2601, Australia
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 1 July 2015 Accepted 7 July 2015 Keywords: Evapotranspiration Crop yield Groundwater table Wheat Maize APSIM
a b s t r a c t The North China Plain (NCP) is one of the main grain production regions in China. However, the annual double cropping system of winter wheat and maize consumed a large amount of groundwater, has led to decline in groundwater table. For conservation of groundwater resource, deficit irrigation is promoted to reduce irrigation water use in recent years. This study combined process-based modeling and experimental data together to evaluate the effects of different irrigation strategies on crop production and groundwater table change for the past three decades in NCP. Data from a six year field trial (2006–2012) under four irrigation schedules and 28 year (1984–2012) field experiment under full irrigation was used to test the agricultural production systems simulator (APSIM) to simulate the responses of winter wheat and maize to different irrigation management. The results showed that APSIM model could well simulate the grain yield and water consumption of the double cropping system under the changing climate and management practices. Simulation results with four irrigation scenarios (critical stage irrigation (CI), minimum irrigation (MI), rainfed (RF) and full irrigation (FI)) from 1984 to 2012 showed that irrigation water use efficiency (IWUE) was highest under CI, although changing from FI would lead to reduction in average annual grain yield by 19.5%, 33.7%, and 58.8% under CI, MI and RF, respectively. Even the minimum irrigation strategy (MI—one irrigation for each crop) will result in continuous decline in the groundwater table, implying an inevitable future shift to alternative cropping systems like dryland wheat-maize system, single wheat or maize. Such change will likely result in lower crop productivity and increased inter-annual variability in crop yields, which will demand improved risk management strategies to minimize loss in bad year while maximize return in good years. © 2015 Elsevier B.V. All rights reserved.
1. Introduction The North China Plain (NCP) covers an area of 320,000 km2 , 17.98 million ha of which is used for agriculture. It is one of the main agricultural regions in China. The annual double cropping rotation of winter wheat and summer maize has been the prevailing system in the past three decades. It has heavily relied on irrigation from groundwater, resulting in a rapid decline of the regional groundwater table (Moiwo et al., 2010; Sun et al., 2010). There is an urgent need to reduce irrigation water use in order to
Abbreviations: NCP, the North China Plain; APSIM, agricultural production systems simulator; CI, critical stage irrigation; MI, minimum irrigation; RF, rainfed; FI, full irrigation; ET, evapotranspiration; BD, bulk density; SAT, saturated volumetric water content; DUL, drained upper limit; LL15, lower limit; GY, grain yield. ∗ Corresponding author. Fax: +86 311 85815093. E-mail address: [email protected] (X. Zhang). http://dx.doi.org/10.1016/j.eja.2015.07.001 1161-0301/© 2015 Elsevier B.V. All rights reserved.
sustain or restore the groundwater resources in this region (Chen et al., 2010b; Liu et al., 2002). While many previous studies had been carried out to investigate the effects of different irrigation scheduling on the performance of winter wheat and maize (Sun et al., 2006, 2014; Zhang et al., 2006; Fang et al., 2010; Chen et al., 2010c; Liu et al., 2013), deficit irrigation has been promoted in recent years with the attempt to minimize both irrigation water use and crop yield reduction. Zhang et al. (2006) and Sun et al., (2014) found that irrigation water use could be minimized to stabilize the groundwater table decline with yield penalties of 14% for winter wheat and 13% for maize, as compared to full irrigation (fully meet crop water demand) under normal rainfall seasons. If irrigation is better targeted to critical crop growth stages, crop yield decline could be further reduced (Zhang et al., 2013). However, all these studies only lasted several seasons. The long-term effects of deficit irrigation strategies on crop production, irrigation water use and regional groundwater balance need to be evaluated.
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Systematic studies to capture crop productivity and water use, as they respond to climate variability, cultivars renewal, irrigation supply and other management practices, require long-term observation of crop yield and water use under different irrigation strategies (Tao et al., 2003; Wang et al., 2008; Liu et al., 2010; Chen et al., 2011; Zhao et al., 2014). Long-term field observational data are usually limited due to the high costs to run the experiments, and process-based modeling is therefore frequently used to assess the impact of different climate scenarios, management practices on crop production and crop water use (Wang et al., 2012, 2013; Zhang et al., 2013). The agricultural production system simulator (APSIM) contains a suite of modules which enable the simulation of soil and plant processes in cropping systems and allow flexible specification of management options (Keating et al., 2003; Wang et al., 2002, 2004; Holzworth et al., 2014). The APSIM model has been successfully applied in NCP to investigate wheat and maize productivity under the climatic change and various irrigation supplies (Chen et al., 2010a,b,c), the contributions of climatic and crop varietal changes to crop productivity (Liu et al., 2010; Xiao and Tao, 2014). In those previous studies, APSIM model validation was done using only short-term (1–2 years) experimental data. Nevertheless, the results indicate that the APSIM model was able to simulate the crop yield and water use in NCP in response to climate variations and management changes. The objectives of this study were to: (1) further test the performance of APSIM model for simulating crop yield and water use of the wheat-maize rotation and the associated change in groundwater table using both short-term and long-term field observational data at a typical site (Luncheng) in NCP, (2) systematically evaluate the effects of different irrigation strategies on crop yield and groundwater conservation, and (3) discuss the possible strategies for sustainable groundwater management.
2. Material and methods 2.1. Study site, climate data and groundwater table Climatic and crop data were collected at Luancheng Agroecological Experimental Station, which is located in the piedmont of Taihang Mountain (37◦ 53 N, 114◦ 40 E; 50 m above sea level) in the northern part of NCP. The site is characterized with monsoon climate with concentrated summer rainfall. Rainfall during winter wheat season (October–June) was around 80–120 mm and 250–350 mm during maize season (June–September). The total evapotranspiration (ET) during the wheat and maize seasons is 400–450 mm and 350–400 mm, respectively (Zhang et al., 2011a,b). Irrigation is essential to reduce crop water stress, especially for winter wheat. The soil is loam with a pH around 8. The average water holding capacity is 38% (v v−1 ), and the wilting point is 13% (v v−1 ) for the top 2 m of the soil profile (Table 1). In area around the study site, winter wheat is usually planted in early October, and harvested during the first 10 days of June. Maize is planted immediately after wheat harvest and harvested at the end of September. Cultivars of winter wheat and maize have frequently changed over time. From 1984 to 1990, nitrogen (N) fertilizer input to winter wheat was around 100–125 N kg/ha. It was increased to 220 N kg/ha in the 1990s and 250 N kg/ha after 2000. The phosphorus fertilizer input was around 100 kg/ha (P2 O5 ) in 1980s, 150 kg/ha in 1990s and 180 kg/ha recently. For maize, N input was also increased from 100 kg/ha in 1980s to 200 kg/ha in recent years. Crop straw was retained and incorporated into soil from 1990 onward, which has resulted in significant improvement of soil organic matter. At the study site, the surface soil (0–0.2 m) contained 1.2% organic matter and 50 mg/kg of available N in 1980s and these were increased to 2.0% and 98 mg/kg, respectively. Avail-
49
able P and K were maintained relatively constant and they were around 20 mg/kg and 110 mg/kg, respectively. Historical daily whether data of maximum and minimum temperatures, sunshine hours and rainfall from 1984 to 2012 were obtained from a meteorological station at the experimental site. Daily solar radiation was estimated from daily sunshine hours using the Angstrom equation (Jones, 1992). The depth of the groundwater table was measured every five days on site from 1984 to 2012.
2.2. Field experiments and measurements Field data collected from two experiments at Luancheng station were used to calibrate and validate the APSIM. The first experiment was conducted from 2006 to 2012, consisting of four irrigation treatments: full irrigation (FI), critical stage irrigation (CI), minimum irrigation (MI) and rain-fed (RF) for winter wheat and summer maize. The results from this experiment were used to calibrate and validate the APSIM model for simulation of crop growth and water use under different irrigation strategies (see Section 2.4). The experiment was set up with plots (5 m × 8 m) arranged in a complete randomized design. Each treatment was repeated 4 times and the plots were separated by a 2-m zone without irrigation to minimize the interactions between plots. FI treatment was irrigated depending on the precipitation with three to five irrigations for wheat and two to four irrigations for maize to ensure no water stress of both crops (average soil water contents was always maintained above 65% of field capacity in the main root zone 0-1.2 m). MI treatment was irrigated before sowing of winter wheat if the soil water content was lower than 70% of the field capacity in the top 50 cm soil layer. Maize was irrigated immediately after sowing to ensure seed germination. No other irrigation was applied. In the CI treatment, one more irrigation was added at jointing stage for winter wheat and at shooting stage for maize, in addition to the irrigation around sowing under MI. The amount of all the irrigations was around 70–80 mm. Water was applied with a plastic tube connected to the outlet of a low-pressure water transportation system to irrigate each plot with a water meter to record the water used. RF treatment was managed without any irrigation for both winter wheat and maize. The second experiment is a long-term field experiment that monitored the yield and evapotranspiration (ET) of winter wheat and maize under sufficient water supply from 1984 to 2012. The water supply was managed similarly as the FI treatment described above. The field management practices such as tillage, fertilizer and cultivars were similar to the local farmers’ practices for the past three decades. Detailed description about this experiment can be found in Zhang et al. (2011a,b). The data was used to assess APSIM performance in simulation of crop yield and crop water use during the past three decades. Due to cultivar changes of both crops, observed flowering and maturity dates were used to derive the crop parameters related to phenology and grain growth. In both experiments, the dates of sowing and key phenological stages of winter wheat including full recovery after winter dormancy, flowering, and physiological maturity were recorded when 50% of the plants reached the corresponding stages. For summer maize, the dates of sowing, seven leaves, flowering, milking, and physiological maturity were also recorded when 50% of the plant reached the stages. Grain yield (GY) was measured from the plots manually. Heads were cut and threshed using a stationary thresher. Grains were air-dried to a constant water content of 13% prior to weighing. Soil volumetric water content was monitored regularly using a neutron probe with an interval depth of 0.2 m from the soil surface to a depth of 2 m every 10 to 15 days during all the growing seasons.
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Table 1 Soil characteristics at the experimental site. Depth (cm)
Texture
Bulk density (g cm−3 )
Porosity (%)
Field capacity (v v−1 )
Wilting point (v v−1 )
Saturated hydraulic conductivity (m d−1 )
0–25 25–40 40–60 60–85 85–120 120–165 165–200
Loam Loam Loam Loam Silty clay loam Clay loam Silty Clay loam
1.39 1.50 1.46 1.49 1.54 1.63 1.55
49 46 46 46 46 42 44
0.36 0.35 0.33 0.34 0.34 0.39 0.38
0.096 0.114 0.139 0.139 0.129 0.139 0.164
1.100 0.430 0.730 0.710 0.020 0.003 0.016
2.3. Evapotranspiration and change in groundwater table Seasonal evapotranspiration (ET) was calculated based on the water balance (Zhang et al., 2008): ET = P + I + SWD – R – D + CR
(1)
where P is precipitation, I is irrigation, SWD is soil water depletion (soil water content at sowing minus soil water content at harvest for the 2 m soil depth), R is runoff, D is drainage from the root zone and CR is capillary rise to the root zone. Due to the limited rainfall runoff was zero. Capillary rise was negligible because of the deep groundwater table (around 20–40 m below soil surface during the experimental period). Drainage beyond root zone was determined using: D = −k(
h ) z
(2)
where k is the hydraulic conductivity and h is the difference in hydraulic potential over the depth interval z at the bottom of the root zone. Hydraulic conductivity was calculated using an exponential relationship between k and :
k = ks exp
−˛ s −
−˛ s − d
(3)
where ks is the saturated hydraulic conductivity, ␣ is a dimensionless constant (here ˛ = 14.5 was used, Kendy et al., 2003), s is the saturated soil water contents, is the soil water contents, and d is the moisture content of dry soil. Soil matric water potential was calculated from based on soil water retention curves developed at the same site (Zhang et al., 2001). The annual change in groundwater table was estimated using a simple relationship following Mao et al., (2005): h =
q
(4)
where h is the annual changes of groundwater table (m), q is the net water quantity replenished to groundwater, which was defined as the difference between the drainage from the root zone profile and the irrigation water drawn. is the specific yield for groundwater, which is 0.15 at the experimental site (Mao et al., 2005). In this study, the groundwater pumping for purposes other than irrigation was not considered, due to the fact that most of groundwater consumption was by irrigation in the study area.
APSIM model calibration involved derivation of wheat and maize cultivar parameters for phenology and grain growth for the cultivars used in both experiments (Table 2). The data from the first experiments under FI treatment from 2009 to 2012 were used to calibrate the modern cultivar and other parameters. The data from the second experiments under FI treatment were used to calibrate the previous cultivar parameters. Cultivar parameters were derived by matching the simulated flowering and maturity dates to the observed ones. Parameters determining grain number for wheat and grain growth rate for wheat and maize were adjusted to match the simulated and observed grain yields. A trial and error method was used to do the model calibration. After model calibration, the model was tested against data from both experiments for grain yield and ET. The validations were conducted using the grain yield and ET under different irrigation strategies from 2006 to 2012 and under the FI treatment from 1984 to 2012. Management practices including irrigation and N applications were specified according to field records. Model performance was evaluated using the slope and the coefficient of determination (r2 ) of the regression lines between simulated and observed values. The root mean square error (RMSE) was used to quantify the deviation of the modeling results from the observed data as:
N 1 (Pi − Oi )2 RMSE =
(5)
N
i=z
2.5. Modelling the impact of irrigation on crop yield, water use and groundwater table The calibrated APSIM model was then used to simulate the impact of different irrigation scenarios on crop yield, water use and changes in groundwater table. Four irrigation scenarios were simulated, i.e., FI, CI, MI and RF, the same as defined previously. Because most of the irrigation water was applied to wheat, the incremental change in irrigation water use efficiency for winter wheat was calculated and analyzed:
WUEi =
Yield(i+1) − Yieldi
ET(i+1) − ETi
(6)
where i represents the irrigation scenarios, i.e., RF, MI, CI and FI. 3. Results
2.4. Calibration and testing of APSIM model 3.1. Model performance APSIM version 7.0 was used in this study. APSIM requires daily weather data as input, including radiation, maximum and minimum temperature and precipitation. It also needs the soil hydraulic parameters, including bulk density (BD), water content at saturation (SAT), drained upper limit (DUL) and lower limit (LL15). DUL was set to measured field capacity, and LL15 to wilting point (Table 1).
The calibrated APSIM model closely simulated the flowering and maturity dates, final biomass and grain yield for both wheat and maize crop in the FI treatment (Fig. 1). The model could explain >90% of the variation in both biomass and grain yield of both wheat and maize. It explained 87% of the variation in changes of total soil water in the 0–2 m soil profile (Fig. 1g).
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Table 2 Parameter values of the main winter wheat and maize cultivars used during the two experiments. Winter wheat Main winter wheat cultivars during different periods Startgf to mat (thermal time from beginning of grain-filling to maturity (◦ Cd)) Potential grain filling rate (potential grain-filling rate (g per kernel per day)) Grains per gram stem (coefficient of kernel number per stem weight at the beginning of grain-filling (g per stem)) Vern sens (sensitivity to vernalization) Photop sens (sensitivity to photoperiod) Summer maize Main maize cultivars during different periods Head grain no max (maximum grain numbers per head) Grain gth rate (grain-filling rate (mg/grain/day)) tt emerg to end juv (thermal time required from emergence to end of juvenile (◦ Cd)) tt flower to maturity (thermal time required from flowering to maturity (◦ Cd)) tt flower to start grain (thermal time required from flowering to starting grain-filling (◦ Cd)) Photoperiod crit1 Photoperiod crit2 Photoperiod slope
Across the four irrigation treatments, APSIM was able to explain >83% of the variation in measured grain yields of both wheat and maize (Fig. 2a and b). The RMSE values for winter wheat yield were 330, 567, 923, and 762 kg ha−1 under FI, CI, MI, and RF treatments, respectively. The corresponding RMSE values for maize yield were 591, 997, 1237, and 852 kg ha−1 under the four irrigation treatments. For crop water use, the simulated total seasonal ET explained >80% of the variation in measured ETs for both the wheat and maize seasons (Fig. 2c and d). The calculated RMSE values for ET during winter wheat growing seasons were 16, 10, 30, and 15 mm under FI, CI, MI, and RF irrigation treatments, respectively. The corresponding RMSE values for ET during the maize growing seasons were 18, 45, 26, and 50 mm. These results demonstrate that the calibrated APSIM model can be used to simulate the responses of crop yield and seasonal ET to different irrigation strategies. For the long-term experiment, the calibrated APSIM model was able to capture the inter-annual variation in both crop water use and grain yields (Fig. 3). The model simulated grain yield with a RMSE of 590 kg ha−1 and 696 kg ha−1 for winter wheat and summer maize, respectively. It simulated the total growing season ET with a RMSE of 45 mm and 52 mm for wheat and maize seasons, respectively. These results demonstrate the ability of APSIM to simulate the responses of wheat and maize yield and seasonal ET to the inter-annual changes in weather conditions and management practices. Combining the simulated drainage and irrigation water use (pumping) and using the simple Eq. (4), we were able to closely match the dynamic changes in groundwater depth from 1984 to 2012 (Fig. 4). The calculated and observed average annual decline in groundwater table in the past three decades was −1.10 m/a and −1.04 m/a, respectively. This result was consistent with the finding of Wu et al., (2001).
3.2. Impact of irrigation on crop yield and water use In the 28 years (1984–2012), the simulated impact of the four irrigation scenarios on the grain yield and water use of winter wheat and summer maize varied from year to year, reflecting impact of climate variability (Fig. 5a and c). Switching from FI to CI, MI, and RF would result in 39.7%, 43.8%, and 75.4% reduction in wheat yield, respectively. The corresponding reduction in maize yield would be 23.2%, 24.1%, and 43.2%, respectively. The reduction
Jimai7 (1984–1992) 550 0.0025 28.5
Jimai36 (1993–2002) 550 0.0025 29.5
Shi733 (2003–2012) 550 0.0025 29.5
1.4 2.0
1.3 2.0
1.3 2.0
Jidan3 (1985–1993) 480 8.0 240
Yedan12 (1994–1999) 550 10.5 240
ZD958 (2000–2012) 600 13.5 200
700 140
730 120
730 120
12.5 24 19
12.5 24 19
12.5 24 21
in maize yield was much less than that in wheat yield, due to the more precipitation during the maize growing seasons. The simulated average seasonal ET during winter wheat growing seasons under FI, CI, MI, and RF irrigation scenarios were 412 mm, 290 mm, 278 mm, and 174 mm, respectively. The corresponding seasonal ET during maize season was 399 mm, 391 mm, 391 mm, and 344 mm, respectively (Fig. 5b and d). The seasonal ET for wheat under FI was slightly higher than that for maize, but was lower than maize ET under the deficit irrigation treatments of CI, MI and RF. The higher inter-annual variation in wheat ET between irrigation scenarios highlights the importance of irrigation to reduce water stress of winter wheat. Changing from FI to CI, MI and RF would result in a reduction in water use in the wheat season by 29.5%, 32.4%, and 57.8%, and in the maize season by 2.2%, 2.16%, and 13.8%, respectively.
3.3. Impact of irrigation on drainage and groundwater table The simulated annual drainage varied significantly between years and under the different irrigation scenarios (Fig. 6a). For all the treatments, there was a significant increase in drainage in 1996, due to the 770 mm heavy rainfall during the summer season. The average annual drainage was 140.0 mm, 26.3 mm, 25.7 mm and 15.8 mm under FI, CI, MI and RF, respectively. CI and MI had similar drainage; although CI used more irrigation than MI. The results showed that under deficit irrigation scenarios such as CI and MI, the increase in irrigation was all changed into crop water use. Only under full irrigation, the increase in irrigation application would result in more drainage from the root zone. Average annual drainage in 1980s and 1990s was 165.5 mm and it was declined to 105.9 mm in 2000s. This was partly related to the increase in grain yield and crop water use, especially for winter wheat under FI (Fig. 3a and b). The rainfall in summer season when the drainage usually occurs was also in a declining trend (data not shown), leading to zero drainage under CI, MI and RF since 1997 to present. Our results show that the total decline in groundwater table under FI, CI, MI, and RF from 1984 to 2012 would be 32.0 m, 26.5 m, 19.2 m, and −2.38 m, respectively (Fig. 6b). The corresponding annual decline rate in groundwater table under FI, CI, MI, and RF was 1.14 m/a, 0.95 m/a, 0.69 m/a, and −0.08 m/a, respectively. The simulated results indicate that all irrigation scenarios (FI, CI, MI) would lead to decline in groundwater table, although the rate
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H. Sun et al. / Europ. J. Agronomy 70 (2015) 48–56
Fig. 1. Comparison of simulated and observed final biomass, grain yield of what and maize, and the soil water change in the 0–2 m soil profile under the treatment of full irrigation (FI) at Luancheng station. (a and b is the simulated and observed flowering and maturity day of year for wheat and maize, c and d is the simulated and observed final biomass for wheat and maize, e and f is the simulated and observed grain yield for wheat and maize, g is the comparison of simulated and observed soil water storage).
of decline would be different. The estimated decline in groundwater table followed closely the observed changes (Fig. 6b). The observed decline in groundwater table was slightly smaller than that of simulated one under FI, especially during the recent decade. One of the possible reasons might be that farmers may have used less water than FI for winter wheat due to difficulties to pump the deeper groundwater or possible adoption of deficit irrigation in some areas. The changes in groundwater table during the winter wheat growing season and summer maize growing season were different. The average contribution to the decline in groundwater table was 92.2%, 70.3% and 66.6% during winter wheat season under FI, CI and MI, respectively. The corresponding contribution was around 7.8%, 29.7% and 33.4% during maize season, respectively (Table 3). The results indicated that under full irrigation (FI scenario) the ground-
water decline was mainly controlled by the irrigation water use during winter wheat season. The results from Table 3 also show that the contribution of water use by maize to groundwater table decline varied in the last three decades, reflecting impact of rainfall variability.
3.4. Irrigation water use efficiency of wheat For wheat the incremental change in WUE of irrigation water was greatest when irrigation was increased from MI to CI scenario, followed by the change from RF to MI and the lowest from CI to FI (Fig. 7a). The results indicate that adding irrigation water at critical stages of wheat crop would lead to the greatest gain in yield per mm of irrigation water. Fig. 7 also shows that the WUEi (calculated between RF to FI) was gradually increased over time (Fig. 7b),
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Fig. 2. Comparison of simulated and observed crop yield and evapotranspiration (ET) of winter wheat and maize under different irrigation treatments from 2006 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation and RF: rain-fed).
Fig. 3. Simulated and measured crop yield and seasonal ET of wheat and maize under full irrigation from 1984 to 2012 at Luancheng. (a and b is the comparison for the simulated and observed wheat yield and maize yield, c and d is the comparison for the simulated and observed ET for the wheat and maize growing seasons, respectively).
Table 3 Contributions of water use in winter wheat and summer maize seasons to ground waer decline under different irrigation scenarios.
1980s 1990s 2000s Average
Winter wheat Summer maize Winter wheat Summer maize Winter wheat Summer maize Winter wheat Summer maize
Full irrigation
Critical irrigation
Minimum irrigation
85.20% 14.80% 108.60% −8.06% 88.00% 12.00% 92.2% 7.8%
62.00% 38.00% 86.90% 13.10% 52.60% 47.40% 70.3% 29.7%
51.50% 48.50% 111.00% −11.00% 59.60% 40.40% 66.6% 33.4%
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Fig. 4. Comparison between calculated and observed groundwater table change under full irrigation strategy. (a is the calculated and observed groundwater table change at different years, b is the comparison for the calculated and observed annual change for the groundwater table).
Fig. 5. Simulated grain yield and evapotranspiration (ET) under different irrigation strategies for winter wheat and summer maize from 1984 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation and RF: rain-fed. a and b is the grain yield for wheat and maize under different irrigation strategies, c and d is the ET during the wheat and maize growing seasons under different irrigation strategies).
reflecting that the yield increase in the past decades also improved the irrigation water use efficiency of the crop. 4. Discussion and conclusions Our results show that under the variable climate background, ciritical irrigation (CI) strategy will result in the maximum irrigation water use efficiency (WUE), i.e., producing the maximum amount of grain per unit of irrigation water. Full irrigation to meet the crop demand would reduce the irrigation WUE, though the total grain yield would be 26.5% higher compared to that under CI. Due to the rapid decline in groundwater table, switching from FI to CI would significantly reduce the depletion of groundwater resources. However, our results also show that the irrigated wheat-maize double rotation is not sustainable in term to the groundwater use. Even the minimum irrigation strategy (MI—one irrigation for each crop) will result in continuous decline in the groundwater table.
This implies an inevitable future shift to alternative cropping systems that use less water. Possible options are dryland wheat–maize system, single cropping of wheat or maize. Such change will likely result in lower crop productivity in the region. This is already happening with the new government policy to seal the wells in many of the NCP regions. This is in response to the wide public concern and a five-year plan has been implemented to solve the groundwater overdraft problems. Due to the large inter-annual variability of rainfall, reducing or stopping irrigation does not only reduce crop yield, it also increases the variability in crop yield potential between years, particularly for wheat. Such change will demand matching management practices such as N application to maximize WUE and yield, while reducing the potential negative impact on the environment (e.g., N losses). As pointed out by Wang et al. (2008), under such situations, opportunity cropping (what and when to crop depending on climate and soil conditions rather than a set annual cycle) and better use of cli-
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bined with experimental data, is a very effective means to assess the long-term impacts of different irrigation management strategies on groundwater resources and crop productivy. Our simulation results provided scientific basis to formulate new policies for irrigation management and cropping systems design considering water resource supply and climate conditions. Acknowledgements This study was financially supported by the Chinese Academy of Sciences (CAS) and CSIRO through the CAS-CSIRO joint project ‘Advancing crop yield while reducing the use of water and nitrogen’, and the project of public sector agricultural special fund of China (Grant No. 201203077). References
Fig. 6. The simulated annual drainage (a) and groundwater table change (b) under different irrigation scenarios from 1984 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation; RF: rain-fed; OB: observed).
mate forecast information to direct decision making, are required to in order to achieve maximum return in good years while minimizing cost in bad years. The results from this study also demonstrate that APSIM could well capture the impact of management and climatic factors on crop yield and crop water use in NCP. Process-based modeling, com-
Fig. 7. Irrigation water use efficiency (WUE) of wheat due to changes in irrigation scenario from CI to FI, MI to CI and RF to MI (a), and the irrigation WUE due to change from RF to FI as compared with the WUE under rainfed condition (RF) (b) for winter wheat from 1984 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation; RF: rain-fed).
Chen, C., Wakter, E.B., Wang, E.L., Yu, Q., 2011. Characterizing spatial and temporal variability of crop yield caused by climate and irrigation in the North China Plain. Theor. Appl. Climatol. 106, 365–381. Chen, C., Wang, E.L., Yu, Q., 2010c. Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric. Water Manage. 97, 1175–1184. Chen, C., Wang, E.L., Yu, Q., Zhang, Y.Q., 2010a. Quantifying the effects of climate trends in the past 43 years (1961–2003) on crop growth and water demand in the North China Plain. Climatic Change 100, 559–578. Chen, C., Wang, E.L., Yu, Q., Zhang, Y.Q., 2010b. Modeling wheat and maize productivity as affected by climate variation and irrigation supply in North China Plain. Agron. J. 102, 1037–1049. Fang, Q.X., Ma, L.W., Yu, Q., Wang, J., Chen, Y.H., Ahuja, L.R., Hoogenboom, G., 2010. Irrigation strategies to improve water use efficiency in the wheat-maize double cropping systems in China. Agric. Water Manage. 97, 1165–1174. Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D., Brown, H., Whish, J.P.M., Verrall, S., Fainges, J., Bell, L.W., Peake, A.S., Poulton, P.L., Hochman, Z., Thorburn, P.J., Gaydon, D.S., Dalgliesh, N.P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F.Y., Wang, E., Hammer, G.L., Robertson, M.J., Dimes, J.P., Whitbread, A.M., Hunt, J., van Rees, H., McClelland, T., Carberry, P.S., Hargreaves, J.N.G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., Keating, B.A., 2014. APSIM-evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62 (0), 327–350. Jones, H.G., 1992. Plant and Microclimate: A Quantitative Approach to Environmental Plant Physiology, 2 ed. Cambridge University Press, Cambridge. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., SilburnM, Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288. Kendy, E., Marchant, P.G., Walter, M.T., Zhang, Y.Q., Liu, C.M., Steenhuis, T.S., 2003. A soil-water-balance approach to quantify groundwater recharge from irrigated cropland in the North China Plain. Hydrol. Process 17, 2011–2031. Liu, C.M., Zhang, X.Y., Zhang, Y.Q., 2002. Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter. Agric. For. Meteorol. 111, 109–120. Liu, J.G., Zang, C.F., Tian, S.Y., Liu, J.G., Yang, H., Jia, S.F., You, L.Z., Liu, B., Zhang, M., 2013. Water conservancy projects in China: achievements, challenges and way forward. Global Environ. Change 23, 633–643. Liu, Y., Wang, E.L., Yang, X.G., Wang, J., 2010. Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980. Global Change Biol. 16, 2287–2299. Mao, X.S., Jia, J.S., Liu, C.M., Hou, Z.M., 2005. A simulation and prediction of agricultural irrigation on groundwater in well irrigation area of the piedmont of Mt. Taihang, North China. Hydrol. Process 19, 2071–2084. Moiwo, J.P., Yang, Y.H., Li, H.L., Han, S.M., Yang, Y.M., 2010. Impact of water resource exploitation on the hydrology and water storage in Baiyangdian Lake. Hydrol. Process 24, 3026–3039. Sun, H.Y., Liu, C.M., Zhang, X.Y., Shen, Y.J., Zhang, Y.Q., 2006. Effects of irrigation on water balance, yield and WUE of winter wheat in the North China Plain. Agric. Water Manage. 85, 211–218. Sun, H.Y., Shen, Y.J., Yu, Q., Flerchinger, G.N., Zhang, Y.Q., Liu, C.M., Zhang, X.Y., 2010. Effect of precipitation change on water balance and WUE of the winter wheat–summer maize rotation in the North China Plain. Agric. Water Manage 97, 1139–1145. Sun, H.Y., Zhang, X.Y., Chen, S.Y., Shao, L.W., 2014. Performance of double cropping system under a continuous minimum irrigation strategy. Agron. J. 106, 281–289. Tao, F.L., Yokozawa, M., Hayashi, Y., Lin, E.D., 2003. Changes in agricultural water demands and soil moisture in China over the last half-century and their effects on agricultural production. Agric. Meteorol. 118, 251–261.
56
H. Sun et al. / Europ. J. Agronomy 70 (2015) 48–56
Wang, E., Robertson, M.J., Hammer, G.L., Carberry, P.S., Holzworth, D., Meinke, H., Chapman, S.C., Hargreaves, J.N.G., Huth, N.I., McLean, G., 2002. Development of a generic crop model template in the cropping system model APSIM. Eur. J. Agron. 18 (1–2), 121–140. Wang, E., Smith, C.J., Bond, W.J., Verburg, K., 2004. Estimations of vapour pressure deficit and crop water demand in APSIM and their implications for prediction of crop yield, water use and deep drainage. Aust. J. Agric. Res. 55 (12), 1227–1240. Wang, E.L., Yu, Q., Wu, D.R., Xia, J., 2008. Climate, agricultural production and hydrological balance in the North China Plain. Int. J. climatol. 28, 1959–1970. Wang, J., Wang, E.L., Feng, L.P., Yin, H., Yu, W.D., 2013. Phenological trends of winter wheat in response to varietal and temperature changes in the North China Plain. Field Crops Res. 144, 135–144. Wang, J., Wang, E.L., Yang, X.G., Zhang, F., Yin, H., 2012. Increased yield potential of wheat-maize cropping system in the North China Plain by climate change adaptation. Climatic Change 113, 825–840. Wu, K., Yu, J.J., Xie, X.Q., 2001. Changing tendency of groundwater level and its effects on agro-ecological environment in the south of Hebei Plain. Agro-Environ. Prot. 20, 401–404, In Chinese.
Xiao, D.P., Tao, F.L., 2014. Contributions of cultivars, management and climate change to winter wheat yield in the North China Plain in the past three decades. Eur. J. Agron. 52, 112–122. Zhang, X.Y., Chen, S.Y., Sun, H.Y., Shao, L.W., Wang, Y.Z., 2011a. Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades. Agric. Water Manage. 98, 1097–1104. Zhang, X.Y., Pei, D., Chen, S.Y., Sun, H.Y., Yang, Y.H., 2006. Performance of double-cropped winter wheat - summer maize under minimum irrigation in the North China Plain. Agron. J. 98, 1620–1626. Zhang, X.Y., Wang, S.F., Sun, H.Y., Chen, S.Y., Shao, L.W., Liu, X.W., 2013. Contribution of cultivar, fertilizer and weather to yield variation of winter wheat over three decades: a case study in the North China Plain. Eur. J. Agron. 50, 52–59. Zhang, X.Y., Zhang, L., Liu, C.M., 2011b. On describing the hydraulic properties of unsaturated soil in piedmont of Mt. Taihang. Acta Agric. Boreali-Sin. 16 (2), 75–82. Zhao, G., Bryan, B.A., Song, X.D., 2014. Sensitivity and uncertainy analysis of the APSIM-wheat model: interactions between cultivar, environmental, and management practices. Ecol. Model. 279, 1–11.
Contents lists available at ScienceDirect
European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja
Quantifying the impact of irrigation on groundwater reserve and crop production – A case study in the North China Plain Hongyong Sun a , Xiying Zhang a,∗ , Enli Wang b , Suying Chen a , Liwei Shao a a Key Laboratory of Agricultural Water Resources, The Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, The Chinese Academy of Sciences, Shijiazhuang 050021, China b CSIRO Agriculture Flagship, Canberra, ACT 2601, Australia
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 1 July 2015 Accepted 7 July 2015 Keywords: Evapotranspiration Crop yield Groundwater table Wheat Maize APSIM
a b s t r a c t The North China Plain (NCP) is one of the main grain production regions in China. However, the annual double cropping system of winter wheat and maize consumed a large amount of groundwater, has led to decline in groundwater table. For conservation of groundwater resource, deficit irrigation is promoted to reduce irrigation water use in recent years. This study combined process-based modeling and experimental data together to evaluate the effects of different irrigation strategies on crop production and groundwater table change for the past three decades in NCP. Data from a six year field trial (2006–2012) under four irrigation schedules and 28 year (1984–2012) field experiment under full irrigation was used to test the agricultural production systems simulator (APSIM) to simulate the responses of winter wheat and maize to different irrigation management. The results showed that APSIM model could well simulate the grain yield and water consumption of the double cropping system under the changing climate and management practices. Simulation results with four irrigation scenarios (critical stage irrigation (CI), minimum irrigation (MI), rainfed (RF) and full irrigation (FI)) from 1984 to 2012 showed that irrigation water use efficiency (IWUE) was highest under CI, although changing from FI would lead to reduction in average annual grain yield by 19.5%, 33.7%, and 58.8% under CI, MI and RF, respectively. Even the minimum irrigation strategy (MI—one irrigation for each crop) will result in continuous decline in the groundwater table, implying an inevitable future shift to alternative cropping systems like dryland wheat-maize system, single wheat or maize. Such change will likely result in lower crop productivity and increased inter-annual variability in crop yields, which will demand improved risk management strategies to minimize loss in bad year while maximize return in good years. © 2015 Elsevier B.V. All rights reserved.
1. Introduction The North China Plain (NCP) covers an area of 320,000 km2 , 17.98 million ha of which is used for agriculture. It is one of the main agricultural regions in China. The annual double cropping rotation of winter wheat and summer maize has been the prevailing system in the past three decades. It has heavily relied on irrigation from groundwater, resulting in a rapid decline of the regional groundwater table (Moiwo et al., 2010; Sun et al., 2010). There is an urgent need to reduce irrigation water use in order to
Abbreviations: NCP, the North China Plain; APSIM, agricultural production systems simulator; CI, critical stage irrigation; MI, minimum irrigation; RF, rainfed; FI, full irrigation; ET, evapotranspiration; BD, bulk density; SAT, saturated volumetric water content; DUL, drained upper limit; LL15, lower limit; GY, grain yield. ∗ Corresponding author. Fax: +86 311 85815093. E-mail address: [email protected] (X. Zhang). http://dx.doi.org/10.1016/j.eja.2015.07.001 1161-0301/© 2015 Elsevier B.V. All rights reserved.
sustain or restore the groundwater resources in this region (Chen et al., 2010b; Liu et al., 2002). While many previous studies had been carried out to investigate the effects of different irrigation scheduling on the performance of winter wheat and maize (Sun et al., 2006, 2014; Zhang et al., 2006; Fang et al., 2010; Chen et al., 2010c; Liu et al., 2013), deficit irrigation has been promoted in recent years with the attempt to minimize both irrigation water use and crop yield reduction. Zhang et al. (2006) and Sun et al., (2014) found that irrigation water use could be minimized to stabilize the groundwater table decline with yield penalties of 14% for winter wheat and 13% for maize, as compared to full irrigation (fully meet crop water demand) under normal rainfall seasons. If irrigation is better targeted to critical crop growth stages, crop yield decline could be further reduced (Zhang et al., 2013). However, all these studies only lasted several seasons. The long-term effects of deficit irrigation strategies on crop production, irrigation water use and regional groundwater balance need to be evaluated.
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Systematic studies to capture crop productivity and water use, as they respond to climate variability, cultivars renewal, irrigation supply and other management practices, require long-term observation of crop yield and water use under different irrigation strategies (Tao et al., 2003; Wang et al., 2008; Liu et al., 2010; Chen et al., 2011; Zhao et al., 2014). Long-term field observational data are usually limited due to the high costs to run the experiments, and process-based modeling is therefore frequently used to assess the impact of different climate scenarios, management practices on crop production and crop water use (Wang et al., 2012, 2013; Zhang et al., 2013). The agricultural production system simulator (APSIM) contains a suite of modules which enable the simulation of soil and plant processes in cropping systems and allow flexible specification of management options (Keating et al., 2003; Wang et al., 2002, 2004; Holzworth et al., 2014). The APSIM model has been successfully applied in NCP to investigate wheat and maize productivity under the climatic change and various irrigation supplies (Chen et al., 2010a,b,c), the contributions of climatic and crop varietal changes to crop productivity (Liu et al., 2010; Xiao and Tao, 2014). In those previous studies, APSIM model validation was done using only short-term (1–2 years) experimental data. Nevertheless, the results indicate that the APSIM model was able to simulate the crop yield and water use in NCP in response to climate variations and management changes. The objectives of this study were to: (1) further test the performance of APSIM model for simulating crop yield and water use of the wheat-maize rotation and the associated change in groundwater table using both short-term and long-term field observational data at a typical site (Luncheng) in NCP, (2) systematically evaluate the effects of different irrigation strategies on crop yield and groundwater conservation, and (3) discuss the possible strategies for sustainable groundwater management.
2. Material and methods 2.1. Study site, climate data and groundwater table Climatic and crop data were collected at Luancheng Agroecological Experimental Station, which is located in the piedmont of Taihang Mountain (37◦ 53 N, 114◦ 40 E; 50 m above sea level) in the northern part of NCP. The site is characterized with monsoon climate with concentrated summer rainfall. Rainfall during winter wheat season (October–June) was around 80–120 mm and 250–350 mm during maize season (June–September). The total evapotranspiration (ET) during the wheat and maize seasons is 400–450 mm and 350–400 mm, respectively (Zhang et al., 2011a,b). Irrigation is essential to reduce crop water stress, especially for winter wheat. The soil is loam with a pH around 8. The average water holding capacity is 38% (v v−1 ), and the wilting point is 13% (v v−1 ) for the top 2 m of the soil profile (Table 1). In area around the study site, winter wheat is usually planted in early October, and harvested during the first 10 days of June. Maize is planted immediately after wheat harvest and harvested at the end of September. Cultivars of winter wheat and maize have frequently changed over time. From 1984 to 1990, nitrogen (N) fertilizer input to winter wheat was around 100–125 N kg/ha. It was increased to 220 N kg/ha in the 1990s and 250 N kg/ha after 2000. The phosphorus fertilizer input was around 100 kg/ha (P2 O5 ) in 1980s, 150 kg/ha in 1990s and 180 kg/ha recently. For maize, N input was also increased from 100 kg/ha in 1980s to 200 kg/ha in recent years. Crop straw was retained and incorporated into soil from 1990 onward, which has resulted in significant improvement of soil organic matter. At the study site, the surface soil (0–0.2 m) contained 1.2% organic matter and 50 mg/kg of available N in 1980s and these were increased to 2.0% and 98 mg/kg, respectively. Avail-
49
able P and K were maintained relatively constant and they were around 20 mg/kg and 110 mg/kg, respectively. Historical daily whether data of maximum and minimum temperatures, sunshine hours and rainfall from 1984 to 2012 were obtained from a meteorological station at the experimental site. Daily solar radiation was estimated from daily sunshine hours using the Angstrom equation (Jones, 1992). The depth of the groundwater table was measured every five days on site from 1984 to 2012.
2.2. Field experiments and measurements Field data collected from two experiments at Luancheng station were used to calibrate and validate the APSIM. The first experiment was conducted from 2006 to 2012, consisting of four irrigation treatments: full irrigation (FI), critical stage irrigation (CI), minimum irrigation (MI) and rain-fed (RF) for winter wheat and summer maize. The results from this experiment were used to calibrate and validate the APSIM model for simulation of crop growth and water use under different irrigation strategies (see Section 2.4). The experiment was set up with plots (5 m × 8 m) arranged in a complete randomized design. Each treatment was repeated 4 times and the plots were separated by a 2-m zone without irrigation to minimize the interactions between plots. FI treatment was irrigated depending on the precipitation with three to five irrigations for wheat and two to four irrigations for maize to ensure no water stress of both crops (average soil water contents was always maintained above 65% of field capacity in the main root zone 0-1.2 m). MI treatment was irrigated before sowing of winter wheat if the soil water content was lower than 70% of the field capacity in the top 50 cm soil layer. Maize was irrigated immediately after sowing to ensure seed germination. No other irrigation was applied. In the CI treatment, one more irrigation was added at jointing stage for winter wheat and at shooting stage for maize, in addition to the irrigation around sowing under MI. The amount of all the irrigations was around 70–80 mm. Water was applied with a plastic tube connected to the outlet of a low-pressure water transportation system to irrigate each plot with a water meter to record the water used. RF treatment was managed without any irrigation for both winter wheat and maize. The second experiment is a long-term field experiment that monitored the yield and evapotranspiration (ET) of winter wheat and maize under sufficient water supply from 1984 to 2012. The water supply was managed similarly as the FI treatment described above. The field management practices such as tillage, fertilizer and cultivars were similar to the local farmers’ practices for the past three decades. Detailed description about this experiment can be found in Zhang et al. (2011a,b). The data was used to assess APSIM performance in simulation of crop yield and crop water use during the past three decades. Due to cultivar changes of both crops, observed flowering and maturity dates were used to derive the crop parameters related to phenology and grain growth. In both experiments, the dates of sowing and key phenological stages of winter wheat including full recovery after winter dormancy, flowering, and physiological maturity were recorded when 50% of the plants reached the corresponding stages. For summer maize, the dates of sowing, seven leaves, flowering, milking, and physiological maturity were also recorded when 50% of the plant reached the stages. Grain yield (GY) was measured from the plots manually. Heads were cut and threshed using a stationary thresher. Grains were air-dried to a constant water content of 13% prior to weighing. Soil volumetric water content was monitored regularly using a neutron probe with an interval depth of 0.2 m from the soil surface to a depth of 2 m every 10 to 15 days during all the growing seasons.
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Table 1 Soil characteristics at the experimental site. Depth (cm)
Texture
Bulk density (g cm−3 )
Porosity (%)
Field capacity (v v−1 )
Wilting point (v v−1 )
Saturated hydraulic conductivity (m d−1 )
0–25 25–40 40–60 60–85 85–120 120–165 165–200
Loam Loam Loam Loam Silty clay loam Clay loam Silty Clay loam
1.39 1.50 1.46 1.49 1.54 1.63 1.55
49 46 46 46 46 42 44
0.36 0.35 0.33 0.34 0.34 0.39 0.38
0.096 0.114 0.139 0.139 0.129 0.139 0.164
1.100 0.430 0.730 0.710 0.020 0.003 0.016
2.3. Evapotranspiration and change in groundwater table Seasonal evapotranspiration (ET) was calculated based on the water balance (Zhang et al., 2008): ET = P + I + SWD – R – D + CR
(1)
where P is precipitation, I is irrigation, SWD is soil water depletion (soil water content at sowing minus soil water content at harvest for the 2 m soil depth), R is runoff, D is drainage from the root zone and CR is capillary rise to the root zone. Due to the limited rainfall runoff was zero. Capillary rise was negligible because of the deep groundwater table (around 20–40 m below soil surface during the experimental period). Drainage beyond root zone was determined using: D = −k(
h ) z
(2)
where k is the hydraulic conductivity and h is the difference in hydraulic potential over the depth interval z at the bottom of the root zone. Hydraulic conductivity was calculated using an exponential relationship between k and :
k = ks exp
−˛ s −
−˛ s − d
(3)
where ks is the saturated hydraulic conductivity, ␣ is a dimensionless constant (here ˛ = 14.5 was used, Kendy et al., 2003), s is the saturated soil water contents, is the soil water contents, and d is the moisture content of dry soil. Soil matric water potential was calculated from based on soil water retention curves developed at the same site (Zhang et al., 2001). The annual change in groundwater table was estimated using a simple relationship following Mao et al., (2005): h =
q
(4)
where h is the annual changes of groundwater table (m), q is the net water quantity replenished to groundwater, which was defined as the difference between the drainage from the root zone profile and the irrigation water drawn. is the specific yield for groundwater, which is 0.15 at the experimental site (Mao et al., 2005). In this study, the groundwater pumping for purposes other than irrigation was not considered, due to the fact that most of groundwater consumption was by irrigation in the study area.
APSIM model calibration involved derivation of wheat and maize cultivar parameters for phenology and grain growth for the cultivars used in both experiments (Table 2). The data from the first experiments under FI treatment from 2009 to 2012 were used to calibrate the modern cultivar and other parameters. The data from the second experiments under FI treatment were used to calibrate the previous cultivar parameters. Cultivar parameters were derived by matching the simulated flowering and maturity dates to the observed ones. Parameters determining grain number for wheat and grain growth rate for wheat and maize were adjusted to match the simulated and observed grain yields. A trial and error method was used to do the model calibration. After model calibration, the model was tested against data from both experiments for grain yield and ET. The validations were conducted using the grain yield and ET under different irrigation strategies from 2006 to 2012 and under the FI treatment from 1984 to 2012. Management practices including irrigation and N applications were specified according to field records. Model performance was evaluated using the slope and the coefficient of determination (r2 ) of the regression lines between simulated and observed values. The root mean square error (RMSE) was used to quantify the deviation of the modeling results from the observed data as:
N 1 (Pi − Oi )2 RMSE =
(5)
N
i=z
2.5. Modelling the impact of irrigation on crop yield, water use and groundwater table The calibrated APSIM model was then used to simulate the impact of different irrigation scenarios on crop yield, water use and changes in groundwater table. Four irrigation scenarios were simulated, i.e., FI, CI, MI and RF, the same as defined previously. Because most of the irrigation water was applied to wheat, the incremental change in irrigation water use efficiency for winter wheat was calculated and analyzed:
WUEi =
Yield(i+1) − Yieldi
ET(i+1) − ETi
(6)
where i represents the irrigation scenarios, i.e., RF, MI, CI and FI. 3. Results
2.4. Calibration and testing of APSIM model 3.1. Model performance APSIM version 7.0 was used in this study. APSIM requires daily weather data as input, including radiation, maximum and minimum temperature and precipitation. It also needs the soil hydraulic parameters, including bulk density (BD), water content at saturation (SAT), drained upper limit (DUL) and lower limit (LL15). DUL was set to measured field capacity, and LL15 to wilting point (Table 1).
The calibrated APSIM model closely simulated the flowering and maturity dates, final biomass and grain yield for both wheat and maize crop in the FI treatment (Fig. 1). The model could explain >90% of the variation in both biomass and grain yield of both wheat and maize. It explained 87% of the variation in changes of total soil water in the 0–2 m soil profile (Fig. 1g).
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Table 2 Parameter values of the main winter wheat and maize cultivars used during the two experiments. Winter wheat Main winter wheat cultivars during different periods Startgf to mat (thermal time from beginning of grain-filling to maturity (◦ Cd)) Potential grain filling rate (potential grain-filling rate (g per kernel per day)) Grains per gram stem (coefficient of kernel number per stem weight at the beginning of grain-filling (g per stem)) Vern sens (sensitivity to vernalization) Photop sens (sensitivity to photoperiod) Summer maize Main maize cultivars during different periods Head grain no max (maximum grain numbers per head) Grain gth rate (grain-filling rate (mg/grain/day)) tt emerg to end juv (thermal time required from emergence to end of juvenile (◦ Cd)) tt flower to maturity (thermal time required from flowering to maturity (◦ Cd)) tt flower to start grain (thermal time required from flowering to starting grain-filling (◦ Cd)) Photoperiod crit1 Photoperiod crit2 Photoperiod slope
Across the four irrigation treatments, APSIM was able to explain >83% of the variation in measured grain yields of both wheat and maize (Fig. 2a and b). The RMSE values for winter wheat yield were 330, 567, 923, and 762 kg ha−1 under FI, CI, MI, and RF treatments, respectively. The corresponding RMSE values for maize yield were 591, 997, 1237, and 852 kg ha−1 under the four irrigation treatments. For crop water use, the simulated total seasonal ET explained >80% of the variation in measured ETs for both the wheat and maize seasons (Fig. 2c and d). The calculated RMSE values for ET during winter wheat growing seasons were 16, 10, 30, and 15 mm under FI, CI, MI, and RF irrigation treatments, respectively. The corresponding RMSE values for ET during the maize growing seasons were 18, 45, 26, and 50 mm. These results demonstrate that the calibrated APSIM model can be used to simulate the responses of crop yield and seasonal ET to different irrigation strategies. For the long-term experiment, the calibrated APSIM model was able to capture the inter-annual variation in both crop water use and grain yields (Fig. 3). The model simulated grain yield with a RMSE of 590 kg ha−1 and 696 kg ha−1 for winter wheat and summer maize, respectively. It simulated the total growing season ET with a RMSE of 45 mm and 52 mm for wheat and maize seasons, respectively. These results demonstrate the ability of APSIM to simulate the responses of wheat and maize yield and seasonal ET to the inter-annual changes in weather conditions and management practices. Combining the simulated drainage and irrigation water use (pumping) and using the simple Eq. (4), we were able to closely match the dynamic changes in groundwater depth from 1984 to 2012 (Fig. 4). The calculated and observed average annual decline in groundwater table in the past three decades was −1.10 m/a and −1.04 m/a, respectively. This result was consistent with the finding of Wu et al., (2001).
3.2. Impact of irrigation on crop yield and water use In the 28 years (1984–2012), the simulated impact of the four irrigation scenarios on the grain yield and water use of winter wheat and summer maize varied from year to year, reflecting impact of climate variability (Fig. 5a and c). Switching from FI to CI, MI, and RF would result in 39.7%, 43.8%, and 75.4% reduction in wheat yield, respectively. The corresponding reduction in maize yield would be 23.2%, 24.1%, and 43.2%, respectively. The reduction
Jimai7 (1984–1992) 550 0.0025 28.5
Jimai36 (1993–2002) 550 0.0025 29.5
Shi733 (2003–2012) 550 0.0025 29.5
1.4 2.0
1.3 2.0
1.3 2.0
Jidan3 (1985–1993) 480 8.0 240
Yedan12 (1994–1999) 550 10.5 240
ZD958 (2000–2012) 600 13.5 200
700 140
730 120
730 120
12.5 24 19
12.5 24 19
12.5 24 21
in maize yield was much less than that in wheat yield, due to the more precipitation during the maize growing seasons. The simulated average seasonal ET during winter wheat growing seasons under FI, CI, MI, and RF irrigation scenarios were 412 mm, 290 mm, 278 mm, and 174 mm, respectively. The corresponding seasonal ET during maize season was 399 mm, 391 mm, 391 mm, and 344 mm, respectively (Fig. 5b and d). The seasonal ET for wheat under FI was slightly higher than that for maize, but was lower than maize ET under the deficit irrigation treatments of CI, MI and RF. The higher inter-annual variation in wheat ET between irrigation scenarios highlights the importance of irrigation to reduce water stress of winter wheat. Changing from FI to CI, MI and RF would result in a reduction in water use in the wheat season by 29.5%, 32.4%, and 57.8%, and in the maize season by 2.2%, 2.16%, and 13.8%, respectively.
3.3. Impact of irrigation on drainage and groundwater table The simulated annual drainage varied significantly between years and under the different irrigation scenarios (Fig. 6a). For all the treatments, there was a significant increase in drainage in 1996, due to the 770 mm heavy rainfall during the summer season. The average annual drainage was 140.0 mm, 26.3 mm, 25.7 mm and 15.8 mm under FI, CI, MI and RF, respectively. CI and MI had similar drainage; although CI used more irrigation than MI. The results showed that under deficit irrigation scenarios such as CI and MI, the increase in irrigation was all changed into crop water use. Only under full irrigation, the increase in irrigation application would result in more drainage from the root zone. Average annual drainage in 1980s and 1990s was 165.5 mm and it was declined to 105.9 mm in 2000s. This was partly related to the increase in grain yield and crop water use, especially for winter wheat under FI (Fig. 3a and b). The rainfall in summer season when the drainage usually occurs was also in a declining trend (data not shown), leading to zero drainage under CI, MI and RF since 1997 to present. Our results show that the total decline in groundwater table under FI, CI, MI, and RF from 1984 to 2012 would be 32.0 m, 26.5 m, 19.2 m, and −2.38 m, respectively (Fig. 6b). The corresponding annual decline rate in groundwater table under FI, CI, MI, and RF was 1.14 m/a, 0.95 m/a, 0.69 m/a, and −0.08 m/a, respectively. The simulated results indicate that all irrigation scenarios (FI, CI, MI) would lead to decline in groundwater table, although the rate
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Fig. 1. Comparison of simulated and observed final biomass, grain yield of what and maize, and the soil water change in the 0–2 m soil profile under the treatment of full irrigation (FI) at Luancheng station. (a and b is the simulated and observed flowering and maturity day of year for wheat and maize, c and d is the simulated and observed final biomass for wheat and maize, e and f is the simulated and observed grain yield for wheat and maize, g is the comparison of simulated and observed soil water storage).
of decline would be different. The estimated decline in groundwater table followed closely the observed changes (Fig. 6b). The observed decline in groundwater table was slightly smaller than that of simulated one under FI, especially during the recent decade. One of the possible reasons might be that farmers may have used less water than FI for winter wheat due to difficulties to pump the deeper groundwater or possible adoption of deficit irrigation in some areas. The changes in groundwater table during the winter wheat growing season and summer maize growing season were different. The average contribution to the decline in groundwater table was 92.2%, 70.3% and 66.6% during winter wheat season under FI, CI and MI, respectively. The corresponding contribution was around 7.8%, 29.7% and 33.4% during maize season, respectively (Table 3). The results indicated that under full irrigation (FI scenario) the ground-
water decline was mainly controlled by the irrigation water use during winter wheat season. The results from Table 3 also show that the contribution of water use by maize to groundwater table decline varied in the last three decades, reflecting impact of rainfall variability.
3.4. Irrigation water use efficiency of wheat For wheat the incremental change in WUE of irrigation water was greatest when irrigation was increased from MI to CI scenario, followed by the change from RF to MI and the lowest from CI to FI (Fig. 7a). The results indicate that adding irrigation water at critical stages of wheat crop would lead to the greatest gain in yield per mm of irrigation water. Fig. 7 also shows that the WUEi (calculated between RF to FI) was gradually increased over time (Fig. 7b),
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Fig. 2. Comparison of simulated and observed crop yield and evapotranspiration (ET) of winter wheat and maize under different irrigation treatments from 2006 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation and RF: rain-fed).
Fig. 3. Simulated and measured crop yield and seasonal ET of wheat and maize under full irrigation from 1984 to 2012 at Luancheng. (a and b is the comparison for the simulated and observed wheat yield and maize yield, c and d is the comparison for the simulated and observed ET for the wheat and maize growing seasons, respectively).
Table 3 Contributions of water use in winter wheat and summer maize seasons to ground waer decline under different irrigation scenarios.
1980s 1990s 2000s Average
Winter wheat Summer maize Winter wheat Summer maize Winter wheat Summer maize Winter wheat Summer maize
Full irrigation
Critical irrigation
Minimum irrigation
85.20% 14.80% 108.60% −8.06% 88.00% 12.00% 92.2% 7.8%
62.00% 38.00% 86.90% 13.10% 52.60% 47.40% 70.3% 29.7%
51.50% 48.50% 111.00% −11.00% 59.60% 40.40% 66.6% 33.4%
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Fig. 4. Comparison between calculated and observed groundwater table change under full irrigation strategy. (a is the calculated and observed groundwater table change at different years, b is the comparison for the calculated and observed annual change for the groundwater table).
Fig. 5. Simulated grain yield and evapotranspiration (ET) under different irrigation strategies for winter wheat and summer maize from 1984 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation and RF: rain-fed. a and b is the grain yield for wheat and maize under different irrigation strategies, c and d is the ET during the wheat and maize growing seasons under different irrigation strategies).
reflecting that the yield increase in the past decades also improved the irrigation water use efficiency of the crop. 4. Discussion and conclusions Our results show that under the variable climate background, ciritical irrigation (CI) strategy will result in the maximum irrigation water use efficiency (WUE), i.e., producing the maximum amount of grain per unit of irrigation water. Full irrigation to meet the crop demand would reduce the irrigation WUE, though the total grain yield would be 26.5% higher compared to that under CI. Due to the rapid decline in groundwater table, switching from FI to CI would significantly reduce the depletion of groundwater resources. However, our results also show that the irrigated wheat-maize double rotation is not sustainable in term to the groundwater use. Even the minimum irrigation strategy (MI—one irrigation for each crop) will result in continuous decline in the groundwater table.
This implies an inevitable future shift to alternative cropping systems that use less water. Possible options are dryland wheat–maize system, single cropping of wheat or maize. Such change will likely result in lower crop productivity in the region. This is already happening with the new government policy to seal the wells in many of the NCP regions. This is in response to the wide public concern and a five-year plan has been implemented to solve the groundwater overdraft problems. Due to the large inter-annual variability of rainfall, reducing or stopping irrigation does not only reduce crop yield, it also increases the variability in crop yield potential between years, particularly for wheat. Such change will demand matching management practices such as N application to maximize WUE and yield, while reducing the potential negative impact on the environment (e.g., N losses). As pointed out by Wang et al. (2008), under such situations, opportunity cropping (what and when to crop depending on climate and soil conditions rather than a set annual cycle) and better use of cli-
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bined with experimental data, is a very effective means to assess the long-term impacts of different irrigation management strategies on groundwater resources and crop productivy. Our simulation results provided scientific basis to formulate new policies for irrigation management and cropping systems design considering water resource supply and climate conditions. Acknowledgements This study was financially supported by the Chinese Academy of Sciences (CAS) and CSIRO through the CAS-CSIRO joint project ‘Advancing crop yield while reducing the use of water and nitrogen’, and the project of public sector agricultural special fund of China (Grant No. 201203077). References
Fig. 6. The simulated annual drainage (a) and groundwater table change (b) under different irrigation scenarios from 1984 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation; RF: rain-fed; OB: observed).
mate forecast information to direct decision making, are required to in order to achieve maximum return in good years while minimizing cost in bad years. The results from this study also demonstrate that APSIM could well capture the impact of management and climatic factors on crop yield and crop water use in NCP. Process-based modeling, com-
Fig. 7. Irrigation water use efficiency (WUE) of wheat due to changes in irrigation scenario from CI to FI, MI to CI and RF to MI (a), and the irrigation WUE due to change from RF to FI as compared with the WUE under rainfed condition (RF) (b) for winter wheat from 1984 to 2012. (FI: full irrigation; CI: critical stage irrigation; MI: minimum irrigation; RF: rain-fed).
Chen, C., Wakter, E.B., Wang, E.L., Yu, Q., 2011. Characterizing spatial and temporal variability of crop yield caused by climate and irrigation in the North China Plain. Theor. Appl. Climatol. 106, 365–381. Chen, C., Wang, E.L., Yu, Q., 2010c. Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric. Water Manage. 97, 1175–1184. Chen, C., Wang, E.L., Yu, Q., Zhang, Y.Q., 2010a. Quantifying the effects of climate trends in the past 43 years (1961–2003) on crop growth and water demand in the North China Plain. Climatic Change 100, 559–578. Chen, C., Wang, E.L., Yu, Q., Zhang, Y.Q., 2010b. Modeling wheat and maize productivity as affected by climate variation and irrigation supply in North China Plain. Agron. J. 102, 1037–1049. Fang, Q.X., Ma, L.W., Yu, Q., Wang, J., Chen, Y.H., Ahuja, L.R., Hoogenboom, G., 2010. Irrigation strategies to improve water use efficiency in the wheat-maize double cropping systems in China. Agric. Water Manage. 97, 1165–1174. Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D., Brown, H., Whish, J.P.M., Verrall, S., Fainges, J., Bell, L.W., Peake, A.S., Poulton, P.L., Hochman, Z., Thorburn, P.J., Gaydon, D.S., Dalgliesh, N.P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F.Y., Wang, E., Hammer, G.L., Robertson, M.J., Dimes, J.P., Whitbread, A.M., Hunt, J., van Rees, H., McClelland, T., Carberry, P.S., Hargreaves, J.N.G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., Keating, B.A., 2014. APSIM-evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62 (0), 327–350. Jones, H.G., 1992. Plant and Microclimate: A Quantitative Approach to Environmental Plant Physiology, 2 ed. Cambridge University Press, Cambridge. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., SilburnM, Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288. Kendy, E., Marchant, P.G., Walter, M.T., Zhang, Y.Q., Liu, C.M., Steenhuis, T.S., 2003. A soil-water-balance approach to quantify groundwater recharge from irrigated cropland in the North China Plain. Hydrol. Process 17, 2011–2031. Liu, C.M., Zhang, X.Y., Zhang, Y.Q., 2002. Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter. Agric. For. Meteorol. 111, 109–120. Liu, J.G., Zang, C.F., Tian, S.Y., Liu, J.G., Yang, H., Jia, S.F., You, L.Z., Liu, B., Zhang, M., 2013. Water conservancy projects in China: achievements, challenges and way forward. Global Environ. Change 23, 633–643. Liu, Y., Wang, E.L., Yang, X.G., Wang, J., 2010. Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980. Global Change Biol. 16, 2287–2299. Mao, X.S., Jia, J.S., Liu, C.M., Hou, Z.M., 2005. A simulation and prediction of agricultural irrigation on groundwater in well irrigation area of the piedmont of Mt. Taihang, North China. Hydrol. Process 19, 2071–2084. Moiwo, J.P., Yang, Y.H., Li, H.L., Han, S.M., Yang, Y.M., 2010. Impact of water resource exploitation on the hydrology and water storage in Baiyangdian Lake. Hydrol. Process 24, 3026–3039. Sun, H.Y., Liu, C.M., Zhang, X.Y., Shen, Y.J., Zhang, Y.Q., 2006. Effects of irrigation on water balance, yield and WUE of winter wheat in the North China Plain. Agric. Water Manage. 85, 211–218. Sun, H.Y., Shen, Y.J., Yu, Q., Flerchinger, G.N., Zhang, Y.Q., Liu, C.M., Zhang, X.Y., 2010. Effect of precipitation change on water balance and WUE of the winter wheat–summer maize rotation in the North China Plain. Agric. Water Manage 97, 1139–1145. Sun, H.Y., Zhang, X.Y., Chen, S.Y., Shao, L.W., 2014. Performance of double cropping system under a continuous minimum irrigation strategy. Agron. J. 106, 281–289. Tao, F.L., Yokozawa, M., Hayashi, Y., Lin, E.D., 2003. Changes in agricultural water demands and soil moisture in China over the last half-century and their effects on agricultural production. Agric. Meteorol. 118, 251–261.
56
H. Sun et al. / Europ. J. Agronomy 70 (2015) 48–56
Wang, E., Robertson, M.J., Hammer, G.L., Carberry, P.S., Holzworth, D., Meinke, H., Chapman, S.C., Hargreaves, J.N.G., Huth, N.I., McLean, G., 2002. Development of a generic crop model template in the cropping system model APSIM. Eur. J. Agron. 18 (1–2), 121–140. Wang, E., Smith, C.J., Bond, W.J., Verburg, K., 2004. Estimations of vapour pressure deficit and crop water demand in APSIM and their implications for prediction of crop yield, water use and deep drainage. Aust. J. Agric. Res. 55 (12), 1227–1240. Wang, E.L., Yu, Q., Wu, D.R., Xia, J., 2008. Climate, agricultural production and hydrological balance in the North China Plain. Int. J. climatol. 28, 1959–1970. Wang, J., Wang, E.L., Feng, L.P., Yin, H., Yu, W.D., 2013. Phenological trends of winter wheat in response to varietal and temperature changes in the North China Plain. Field Crops Res. 144, 135–144. Wang, J., Wang, E.L., Yang, X.G., Zhang, F., Yin, H., 2012. Increased yield potential of wheat-maize cropping system in the North China Plain by climate change adaptation. Climatic Change 113, 825–840. Wu, K., Yu, J.J., Xie, X.Q., 2001. Changing tendency of groundwater level and its effects on agro-ecological environment in the south of Hebei Plain. Agro-Environ. Prot. 20, 401–404, In Chinese.
Xiao, D.P., Tao, F.L., 2014. Contributions of cultivars, management and climate change to winter wheat yield in the North China Plain in the past three decades. Eur. J. Agron. 52, 112–122. Zhang, X.Y., Chen, S.Y., Sun, H.Y., Shao, L.W., Wang, Y.Z., 2011a. Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades. Agric. Water Manage. 98, 1097–1104. Zhang, X.Y., Pei, D., Chen, S.Y., Sun, H.Y., Yang, Y.H., 2006. Performance of double-cropped winter wheat - summer maize under minimum irrigation in the North China Plain. Agron. J. 98, 1620–1626. Zhang, X.Y., Wang, S.F., Sun, H.Y., Chen, S.Y., Shao, L.W., Liu, X.W., 2013. Contribution of cultivar, fertilizer and weather to yield variation of winter wheat over three decades: a case study in the North China Plain. Eur. J. Agron. 50, 52–59. Zhang, X.Y., Zhang, L., Liu, C.M., 2011b. On describing the hydraulic properties of unsaturated soil in piedmont of Mt. Taihang. Acta Agric. Boreali-Sin. 16 (2), 75–82. Zhao, G., Bryan, B.A., Song, X.D., 2014. Sensitivity and uncertainy analysis of the APSIM-wheat model: interactions between cultivar, environmental, and management practices. Ecol. Model. 279, 1–11.