Water Requirements and Irrigation Scheduling of Spring Maize Using ...

Chinese Geographical Science 2007 17(1) 056–063 DOI: 10.1007/s11769-007-0056-3 www.springerlink.com

Water Requirements and Irrigation Scheduling of Spring Maize Using GIS and CropWat Model in Beijing-Tianjin-Hebei Region FENG Zhiming1, 2, LIU Dengwei1, 2, ZHANG Yuehong1, 2 (1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China) Abstract: Due to the over use of available water resources, it has become very important to define appropriate strategies for planning and management of irrigated farmland. In this paper, Beijing-Tianjin-Hebei (Jing-Jin-Ji) region was chosen as the case study area for its special political and economic status and its severe water problem. To achieve effective planning, the information about crop water requirements, irrigation withdrawals, soil types and climatic conditions were obtained in the study area. In the meantime, a GIS method was adopted, which extends the capabilities of the crop models to a regional level. The main objectives of the study are: 1) to estimate the spatial distribution of the evapotranspiration of spring maize; 2) to estimate climatic water deficit; 3) to estimate the yield reduction of spring maize under different rainfed and irrigated conditions. Based on the water deficit analysis, recommended supplemental irrigation schedule was developed using CropWat model. Compared to the rainfed control, the two or three times of supplemental water irrigated to spring maize at the right time reduced the loss of yield, under different scenarios. Keywords: spring maize; climatic water deficit; irrigation schedule

1 Introduction Water demand has been growing rapidly due to population growth and increasing living standards, and as a result, water shortage has become serious problems in Beijing-Tianjin-Hebei (Jing-Jin-Ji) region, which has made it necessary to improve integrated technology and multidisciplinary water resources management capabilities. Irrigation uses 80% of the available water resources of the area, of which less than 30% is effectively utilized by the crop, and the rest is consumed by deep percolation and poor management practices. This infructuously consumed water not only leads to a great waste, but also causes water logging and salinization that adversely affects the productivity of most irrigated land of the area. On the other hand, the local farmers have no appropriate plan for irrigation—they just traditionally wait for rain and only irrigate their crops when an extreme drought has occurred. They actually do not know how much crop yield can be improved through irrigation, when and how much water is needed to irrigate their crops. So, in order

to improve water use efficiency it is essential to understand how much water is being required by the crops in different periods, that is to say a practical and simplified irrigation schedule is needed for this area. After the year 2000, the grain has a surplus temporarily in China, which makes the farming products fall in price. As a result, in Jing-Jin-Ji region lots of wheat and summer maize was replaced by spring maize, which approximately occupied 20%–50% of the total sown area of crops and joined into large area in spatial distribution. In order to vividly explain this process, we called it "the spring corn planting belt phenomenon". The increment of sown area of spring maize urgently needs to perform an appropriate irrigation management plan. So in this paper, we propose a reasonable supplemental irrigation schedule for the spring maize according to the CropWat model analysis in Jing-Jin-Ji region. Several researchers have used the CropWat model for analyzing crop water and requirements in different parts of the world (Gouranga and Verma, 2005; Martyniak et al., 2006; Dechmi et al., 2003). Since spring maize is becom-

Received date: 2006-08-08; accepted date: 2006-12-20 Foundation item: Under the auspices of the Knowledge Innovation Program of Chinese Academy of Sciences (No. SU210200) Corresponding author: LIU Dengwei. E-mail: [email protected]

Water Requirements and Irrigation Scheduling of Spring Maize Using GIS and CropWat Model

57

ing the dominant crop of the Jing-Jin-Ji region, crop water and irrigation requirements of spring maize during different stages (seeding, elongation, booting and heading, milk done and mature) were computed using the CropWat model.

2 Study Area Jing-Jin-Ji region (37°30′–42°40′N, 113°30′–119°50′E), located in the Haihe River valley，was chosen as a typical study area, which includes Beijing, Tianjin, and eight neibouring cities in Hebei Province: Shijiazhuang, Tangshan, Qinhuangdao, Baoding, Zhangjiakou, Chengde, Cangzhou, and Langfang (Fig. 1). It covers an area of approximately 1.831×103km2, most of which is flat to slightly sloping lowland except the mountainous area of Chengde and the northern Zhangjiakou in the northwestern part of the study area. It lies in the temperate continental monsoon climate zone with hot-wet summer and cold-dry winter. The mean annual temperature is 11.5–12.5℃ and the accumulative air temperature of ≥ 10 ℃ is 4100–5300 ℃ , and the annual precipitation ranges from 531mm to 644mm. This area covers Greater Beijing Economy Hub and its surrounding area, and also is the core region of the Bohai Bay Trade Hub, having long been the most important political, economic and cultural center of the northern China. It supports about 10% of the nation’s population, food and GDP in the less than 2% of the nation’s land, which has exerted great pressure on water demand. Now this area is severely short of water resources due to fast economic development and the waste of water in agriculture, with a total quantity of water resources of only 37×109m3, less than 1.3% of the national total water resources. Water availability has been one of the main factors limiting economic development and agricultural productivity in this area. Uncontrolled and excessive use of groundwater has caused lowering of groundwater table and intrusion of seawater, which leads to serious salinization problems particularly in the coastal areas (Chen, 2000). Water management, especially agricultural irrigation water management has become an extremely essential measure to take in this area.

3 Methodology 3.1 Data The data we use in this study are mainly meteorological data, including monthly solar radiation, precipitation,

Fig. 1 Sketch of Jing-Jin-Ji region

relative humidity, sunshine time, average yearly air temperature, minimum air temperature, maximum air temperature and wind speed from 1961 to 2001 in 43 meteorological stations. Arc/Info grid spline method is used to interpolate the point climate data into the 1km×1km grid data. Then the grids are converted from geographic projection to Albers Conic Equal-Area projection. 3.2 Methods CropWat for Windows is a decision support system developed by the Land and Water Development Division of FAO, with the assistance of the Institute of Irrigation and Development Studies of Southampton of UK and National Water Research Center of Egypt. The model carries out calculations for reference evapotranspiration, crop water requirements and irrigation requirements in order to develop irrigation schedules under various management conditions and scheme water supply (FAO, 1992). It allows the development for improved irrigation practices, the planning of irrigation schedules and the assessment of production under rainfed conditions or deficit irrigation. CropWat for Windows uses the FAO Penman-Monteith method for calculation reference crop evapotranspiration (Allen et al., 1998). The development of irrigation schedules and evaluation of rainfed and irrigation practices are based on a daily soil-moisture balance using various options for water supply and irrigation management conditions. Scheme water supply is calculated according to the cropping pattern provided in

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FENG Zhiming, LIU Dengwei, ZHANG Yuehong

the program (Clarke et al., 1998; Smith, 1992). Studies have shown that the Penman-Monteith method is more reliable than methods that use less climatic data (Jensen et al., 1990). In this paper, the Penman-Monteith equation below was adapted as the sole means of calculating the reference evapotranspiration of spring maize. 0.408 Δ ( Rn − G ) + γ ET0 = (

900 T + 273

U 2 ( es − ea )

Δ + γ (1 + 0.34 U 2 )

)

(1) where, ET0 is the reference evapotranspiration (mm/a), Rn is the net radiation (MJ/(m2·d)), G is the soil heat flux density (MJ/(m2·d)), U2 is the wind speed at a height of 2m (m/s), es is the saturated vapor pressure (kPa). ea is the actual vapor pressure of the air at standard screen height (kPa), γ is the psychrometer constant (kPa/°C), ∆ is the slope of the saturation vapor pressure curve between the average air temperature and dew point (kPa/°C), T is the mean daily air temperature (°C). ETc is termed as the crop water requirement (CWR) (mm/a). It is defined as the depth of water needed to meet the water loss through evapotranspiration of a diseasefree crop, growing in fields under non-restricting soil conditions including soil water and fertility and achieving full production potential under the given growing environment (Doorenbos and Pruitt, 1977; Doorenbos and Kassam, 1979). ETc can be calculated by Equation (2): ETc = K c i ET0 (2) where Kc is the crop coefficient. The crop water requirement (ETc) of spring maize was computed by multiplying the crop coefficient (Kc) with ET0 at different growth stages. The Kc in various growing periods is: 0.3–0.5 in seedling stage (15–30d); 0.70–0.85 in development period (30–45d); 1.05–1.20 in intermediate stage (30–45d); 0.8–0.9 in last stage (10–30d); 0.55–0.60 in harvesting period (FAO, 1979). And the crop coefficient of spring maize was calibrated by locally available values (Han et al., 2005).

4 Results and Discussion 4.1 Crop water requirements From the water requirement results computed by the evapotranspiration model, the peak period of corn water use was from early June to late July. The average daily ETc was usually more than 4mm. The calculated total

corn ETc varied between 325mm and 629mm in 1961–2001, with an average of 445.3mm, which was less in wet years and more in dry years in Jing-Jin-Ji region (Fig. 2). The variation trend of the ETc has two obvious phases: in the first phase (1961–1973), the ETc curve is highly fluctuating; in the second phase (1974–2000), the variation of ETc is comparatively gentle. 4.2 Spatial distribution of ETc and climate water deficits in Jing-Jin-Ji region The spatial distribution of spring maize evapotranspiration (ETc) in different stages of growing season was estimated by two steps. First, DEM-based and GIS-assisted methods were employed to estimate the spatial distribution of reference crop evapotranspiration (ET0) according to Penman-Monteith model. Then, spring maize evapotranspiration (ETc) of different stages of growing season was calculated by ET0 and crop coefficient (Kc) (Fig. 3). Figure 3 shows that higher ETc values are registered in the northwest (Zhangjiakou) and southeast (Cangzhou), the lower ETc value appears in the north (Chengde). Figure 3 also shows that ETc value has temporal variation during growing seasons. Highest value of ETc appears in the fourth stage (21 July–31 August, milk done), which is varied from 109mm to 182mm, and lowest ETc is seen in the last stage (1 September–10 September, mature stage), which is varied from 14mm to 23mm. Figure 4 shows the spatial distribution characteristic of water deficit in different growing stages. In the first stage (Fig. 4a, seeding stage) and the second stage (Fig. 4b, elongation stage), the average water deficit of rainfed spring maize was very high in the whole area, since there is little rainfall in the two stages. In order to reduce the loss of production, farmers should irrigate once or twice in this period. In the third and the fourth stages (Fig. 4c, booting and heading stage; Fig. 4d, milk done stage), the water deficit was different in different areas: in the northwest, the water deficit ratio (WDR), is very high, which ranges from 20% to 50%; in the southeast, the WDR is lower, which is from –20% to –119%. In the last stage (Fig. 4e, mature stage), in the southwest and the centre of the area the WDR is lower, while the rest has higher WDR value. During the whole growing stage (Fig. 4f), the WDR is relatively high in most of the study area, except for the east and southwest. So in Jing-Jin-Ji region, the rainfall is not enough to feed the spring maize, most farmlands of which need to be irrigated. The WDR can be expressed by:


59

-

Fig. 2 Total ETc of spring corn in Jing-Jin-Ji region in 1961–2001

WDR= ( ETc P )/ETc × 100%

(3)

where WDR is the water deficit ratio (%), ETc is the crop water requirement (mm), P is the difference between rainfall and runoff. 4.3 Long-term crop water deficits The average water deficit of spring maize from natural precipitation was 17.5–60.5mm in the first stage in 1961–2001 in Jing-Jin-Ji region, while its ETc is 70.7–94.7mm (Table1).

During 1961–2001, there are 35 times of water deficit occurring in the seeding stage, there are 32 times of deficit in the elongation stage, 8 times of deficit in the booting and heading stages, and 5 times of deficit in the milk stage in most parts of the Jing-Jin-Ji region. The serious water deficit in the seeding stage is the primary reason for low corn yield in this area, since the water deficit is from 24.7% to 66.2% of ETc. So the crop yield may obviously increase if irrigation water is supplied during the critical growth stage.

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FENG Zhiming, LIU Dengwei, ZHANG Yuehong

a. 25 April–31 May, seeding stage; b. 1 June–31 June, elongation stage; c. 1 July–20 July, booting and heading stage; d. 21 July–31August, milk done; e. 1 September–10 September, mature stage; f. 25 April–10 September, whole stage

Fig. 3 Spatial distribution of ETc in Jing-Jin-Ji region in 1961–2001

a. 25 April–31 May, seeding stage; b. 1 June–31 June, elongation stage; c.1 July–20 July, booting and heading stage; d. 21 July–31 August, milk done; e. 1 September–10 September, mature stage; f. 25 April–10 September, whole stage

Fig. 4 Spatial distribution of WDR in Jing-Jin-Ji region in 1961–2001


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Table 1 Average water deficit of spring maize in 1961–2001 Growth stage 25 April–31 May (Seeding stage)

ETc (mm) P (mm)

Shijiazhuang 79.0 39.4

Zhangjiakou 94.7 34.3

Chengde

Beijing

77.1 51.6

Qinhuangdao 70.7 53.3

Langfang

Tianjin

Tangshan

Baoding

82.4 35.0

85.8 29.0

81.3 37.9

79.8 42.4

81.0 34.7

Cangzhou 92.0 37.9 54.1

ETc–P (mm)

39.6

60.4

25.5

17.5

47.4

56.9

43.4

37.4

46.3

WDR (%)

50.1

63.8

33.1

24.7

57.5

66.2

53.4

46.9

57.2

58.8

1 June–30 June

ETc (mm)

118.6

128.0

109.5

95.8

117.3

125.7

113.3

109.7

121.9

130.0

(Elongation stage)

P (mm)

49.4

56.6

87.6

91.6

70.3

65.9

68.4

84.1

58.5

75.3

ETc –P (mm)

69.3

71.4

21.9

4.2

47.1

59.8

45.0

25.6

63.4

54.7

WDR (%)

0.6

0.6

0.2

0.0

0.4

0.5

0.4

0.2

0.5

0.4

1 July–20 July

ETc (mm)

92.5

100.8

90.0

77.1

89.3

92.3

87.3

83.1

90.9

92.2

(Booting and

P (mm)

91.1

70.3

97.0

129.6

116.8

107.0

114.2

127.7

106.5

137.7

heading stage)

ETc –P (mm)

1.3

30.5

–7.1

–52.4

–27.5

–14.7

–26.9

–44.6

–15.6

–45.5

WDR (%)

1.4

30.3

–7.9

–68.0

–30.8

–16.0

–30.8

–53.7

–17.2

–49.4

21July–31August

ETc (mm)

139.1

153.1

138.8

127.6

136.7

140.0

136.4

130.6

137.5

142.9

(Milk done stage)

P (mm)

206.6

140.7

181.2

238.3

233.3

201.9

211.7

244.1

214.0

229.5

ETc –P (mm)

–67.5

12.4

–42.4

–110.8

–96.6

–61.9

–75.2

–113.5

–76.5

–86.6

WDR (%)

–48.5

8.1

–30.5

–86.8

–70.7

–44.2

–55.1

–86.9

–55.6

–60.6

1 September–

ETc (mm)

17.4

19.5

16.3

17.9

17.9

18.3

17.8

17.6

17.7

21.1

10 September

P (mm)

16.5

13.8

15.8

17.5

14.8

13.2

13.6

15.4

14.2

14.6

(Mature stage)

ETc –P (mm)

1.0

5.6

0.5

0.4

3.1

5.1

4.2

2.2

3.5

6.5

WDR (%)

5.5

28.9

2.9

2.1

17.3

28.0

23.5

12.2

19.7

30.8

25 April–

ETc (mm)

446.6

496.0

431.7

389.2

443.6

462.2

436.1

420.8

449.0

478.2

10 September

P (mm)

403.0

315.6

433.2

530.3

470.2

417.0

445.7

513.8

427.9

495.0

(Whole stage)

ETc –P (mm)

43.6

180.4

–1.5

–141.1

–26.6

45.2

–9.6

–92.9

21.1

–16.8

9.8

36.4

–0.4

–36.3

–6.0

9.8

–2.2

–22.1

4.7

–3.5

WDR (%)

4.4 Irrigation schedule According to distribution characteristics and the standard deviation of ETc, we could classify them into 3 categories. The first type includes Langfang and Cangzhou, the standard deviation of which is more than 45; the second type is Chengde, the standard deviation of which is less than 30; the third type includes the rest, which have similar standard deviation of ETc varying from 33 to 37. So we pick out 3 samples respectively in the three categories, which are Langfang, Tangshan and Chengde, to analyze the irrigation scheduling of spring maize. In order to compute the irrigation schedule using the CropWat model, the information on soil type, such as total available moisture, readily available moisture and initial available moisture are also required. The results are as follows. In dry years, it needs irrigation to minimize the loss of production. So in the paper, we analyze two scenarios: The first is under fortnightly precipitation condition and the second is under triweekly precipitation condition. Under these weather situations if the spring maize has not obtained enough water, the production will drop heavily. The irrigation schedule for spring maize was planned for

two or three times under the two scenarios. The irrigation scheduling in Chengde, Langfang and Tangshan are presented in Tables 2–7. Table 2 Irrigation scheduling in Chengde (fortnightly precipitation) (mm) SMD (No Irr.)

SMD (Irr.)

TAM

RAM

Total rainfall

7 May

54.9

27.5

15.6

3.6

3.6

21 May

74.2

37.1

25.3

1.4

1.4

4 Jun.

93.5

46.8

39.7

2.8

2.8

18 Jun.

112.8

56.4

55.1

12.4

12.4

Date

Net Irr.

Lost Irr.

2 Jul.

128.0

64.0

67.4

39.6

39.6

40.0

0

16 Jul.

128.0

64.0

73.4

60.3

30.7

30.0

0

30 Jul.

128.0

64.0

71.5

68.2

28.3

10.0

0

13 Aug.

128.0

71.7

61.9

68.8

39.5

27 Aug.

128.0

89.6

46.8

65.4

41.9

Notes: SMD—soil moisture deficit; RAM—readily available moisture; TAM—total available moisture; Net Irr.—irrigation depth applied; Lost Irr.—irrigation water that is not stored in soil; The same in the below tables

Table 8 shows the effects of the different irrigation scheduling criteria simulated with CropWat model on estimated total maize yield reduction in the climatic conditions of the year 2000.

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FENG Zhiming, LIU Dengwei, ZHANG Yuehong Table 8 Effect of rainfed and optimal irrigation on total maize yield reduction in different weather conditions

Table 3 Irrigation scheduling in Chengde (triweekly precipitation) (mm) Date

TAM

RAM

7 May 28 May 18 Jun. 9 Jul. 30 Jul. 20 Aug.

54.9 83.9 112.8 128.0 128.0 128.0

27.5 41.9 56.4 64.0 64.0 80.6

Total rainfall 26.6 53.9 87.6 108.3 104.0 76.1

SMD (No Irr.) 1.1 2.1 28.6 69.9 80.0 79.6

SMD (Irr.) 1.1 2.1 28.6 56.4 55.8 45.0

Net Irr.

Lost Irr.

30.0 60.0 60.0

1.4 3.6 4.2

Net Irr.

Lost Irr.

60.0 30.0 10.0

15.6 0 0

Net Irr.

Lost Irr.

Table 4 Irrigation scheduling in Langfang (fortnightly precipitation) (mm) Date

TAM

RAM

7 May 21 May 4 Jun. 18 Jun. 2 Jul. 16 Jul. 30 Jul. 13 Aug. 27 Aug.

76.4 103.2 130.1 156.9 178.0 178.0 178.0 178.0 178.0

38.2 51.6 65 78.5 89.0 89.0 89.0 99.7 124.6

Total rainfall 4.7 14.4 32.0 51.5 66.9 73.6 69.9 56.9 39.0

SMD (No Irr.) 22.1 27.8 30.1 44.4 73.8 94.0 101.4 102.4 100.6

SMD (Irr.) 22.1 27.8 30.1 44.4 31.3 33.2 50.3 70.2 73.3

TAM

7 May 28 May 18 Jun. 9 Jul. 30 Jul. 20 Aug.

76.4 116.7 156.9 178.0 178.0 178.0

RAM 38.2 58.3 78.5 89.0 89.0 112.1

Total rainfall 10.1 41.0 83.5 108.6 100.4 65.4

SMD (No Irr.) 16.7 11.4 45.4 98.0 115.6 118.1

SMD (Irr.) 16.7 11.4 45.4 67.1 68.6 81.7

60.0 60.0 30.0

14.6 0 0

Table 6 Irrigation scheduling in Tangshan (fortnightly precipitation) (mm) Date

TAM

7 May 21 May 4 Jun. 18 Jun. 2 Jul. 16 Jul. 30 Jul. 13 Aug. 27 Aug.

61.8 83.5 105.2 126.9 144.0 144.0 144.0 144.0 144.0

RAM 30.9 41.8 52.6 63.5 72.0 72.0 72.0 80.6 100.8

Total rainfall 8.6 20.7 41.2 63.6 81.0 88.3 83.5 68.1 47

SMD (No Irr.) 9.8 6.0 2.8 11.5 37.8 62.1 74.4 77.5 75.4

SMD (Irr.) 9.8 6.0 2.8 11.5 37.8 29.1 30.4 50.6 53.0

Net Irr.

Lost Irr.

40.0 25.0

2.2 0

Table 7 Irrigation scheduling in Tangshan (triweekly precipitation) Date

Total SMD rainfall (No Irr.)

SMD (Irr.)

Net Irr.

Lost Irr.

28.1

40.0

11.9

75.0

58.1

60.0

1.9

119.9

90.3

57.6

60.0

2.4

78.6

91.8

44.2

TAM

RAM

7 May

61.8

30.9

16.8

1.7

28 May

94.4

47.2

53.7

2.1

2.1

18 Jun.

126.9

63.5

102.4

28.1

2 Jul.

144.0

72.0

130.4

30 Jul.

144.0

72.0

20 Aug.

144.0

90.7

Option

Net Irr. Lost Irr. Yield red. (mm) (mm) (%)

Chengde

Fortnightly precipitation (rainfed) Triweekly precipitation (rainfed) Fortnightly precipitation (irrigation) Triweekly precipitation (irrigation)

– – 80 150

– – 0.4 9.1

16.2 35.9 0.2 4.1

Langfang


– – 100 150

– – 14.6 15.6

15.7 29.2 0.1 0.6

Tangshan


– – 65 160

– – 2.2 16.3

11.7 31.1 0 1.7

Note: Yield red.—estimated yield reduction due to crop stress

Table 5 Irrigation scheduling in Langfang (triweekly precipitation) (mm) Date

Site

1.7

In the rainfed conditions (fortnightly precipitation and triweekly precipitation), the calculated soil moisture deficit shows the effect of rainfall only. Due to the small amount of precipitation during maize seeding season, the soil moisture deficit reaches the limit of the readily available moisture in the first ten days of June in the area. Beginning from June the soil moisture deficit goes up to the limit of total available moisture. In this case, the maize has a yield reduction, which is estimated to be 16.2% (fortnightly precipitation), 35.9% (triweekly precipitation) for Chengde and 15.7% (fortnightly precipitation), 29.2% (triweekly precipitation) for Langfang and 11.7% (fortnightly precipitation), 31.1% (triweekly precipitation) for Tangshan (Table 8). In irrigation scheduling for maize at three sites, the daily soil moisture balance option was selected to show the status of the soil every day, the soil moisture changes in the growing season and estimated total yield reduction. First, we will analyze the irrigation scheduling under the fortnightly precipitation scenario. Table 2, Table 4 and Table 6 show soil moisture changes during the maize growing season in Chengde, Langfang and Tangshan sites using the scheduling criteria: irrigating at fixed intervals of 14 days and variable depths (the soil is returned exactly to field capacity with no or less excess irrigation), when the soil moisture deficit reaches the readily available moisture. In this case, the “negative” soil moisture deficit mean lost irrigation (0.4mm at Chengde, 14.6mm at Langfang and 2.2mm at Tangshan), which is assumed to runoff. The predicted crop stress is small and the estimated total yield reduction is not high (0.2%, 0.1%, 0) as compared with the rainfed conditions (Table 8).


Scheduling option with 3 times of irrigation of 40mm (2 July), 30mm (16 July) and 10mm (30 July) at Chengde can be applied when soil moisture deficit falls below the readily available moisture, which seems to be the most suitable option (Table 2) according to the analysis. In Langfang, scheduling with three times of irrigation of 60mm (18 June), 30mm (2 July) and 10mm (16 July) seems to be the best option (Table 4). In Tangshan, scheduling with two times of irrigation of 40mm (16 July) and 25mm (30 July) will be the best choice (Table 6). Second, we will discuss the irrigation scheduling under the triweekly precipitation sce nario. The lost irrigation (9.1mm in Chengde, 15.6mm in Langfang and 16.3mm in Tangshan) is more than that of the first scenario. The estimated total yield reduction is not so high (4.1%, 0.6%, 1.7%) compared with the rainfed conditions, however it is little higher than the fortnightly precipitation (irrigation) conditions (Table 8). Scheduling option with three times of irrigation of 30mm (18 June), 60mm (9 July) and 60mm (30 July) at Chengde seems to be the most suitable option (Table 3). In Langfang, scheduling with three times of irrigation of 60mm (18 June), 60mm (9 July) and 30mm (30 July) will be the best option (Table 5). In Tangshan, scheduling with three times of irrigation of 40mm (18 June), 60mm (9 July) and 60mm (30 July) is the best choice (Table 7).

5 Conclusions The spring maize in Jing-Jin-Ji region has seasonal water deficits, especially serious in spring (seeding stage), which is the dominating reason for the low yield per unit area in this region. To remedy the water deficits during its critical growth periods and avoid the waste of water in the mean time, precise supplemental irrigation schedules were recommended in different weather conditions (the fortnightly precipitation and the triweekly precipitation). Under the fortnightly precipitation scenario, in Chengde, irrigation was recommended three times in its growth period: 2 July, 16 July and 30 July respectively. In Langfang irrigation was also recommended three times: 18 June, 2 July and 16 July respectively. In Tangshan irrigation was recommended twice: 16 July and 30 July. Under the triweekly precipitation scenario, irrigation was recommended three times in the area during the growing season: one at elongation stage, one at booting and

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heading stage, and one in the milk done stage. These timely and limited irrigations will result in significantly higher yields compared to the rainfed control. Precise supplemental irrigation in critical growth periods is valuable for reducing the loss of spring maize production, especially during the dry years. References Allen R G, Pereira L A, Raes D et al., 1998. Crop evapotranspiration. In: FAO Irrigation and Drainage Paper 56. Rome: FAO, 293. Chen Zhikai, 2000. Sustainable development and utilization of China’s water resources in 21st century. Engineering Sciences in China, 2(3), 7–11. (in Chinese) Clarke D, Smith M, El-Askari K, 1998. CropWat for Windows: User Guide. Southampton: University of Southampton, 1–43. Dechmi F, Playan E, Faci J M et al., 2003, Analysis of an irrigation district in northeastern Spain Ⅱ. Irrigation evaluation, simulation and scheduling. Agricultural Water Management, 61: 93–109. Doorenbos J, Kassam A H, 1979. Yield response to water. In: FAO Irrigation and Drainage Paper No. 33. Rome: FAO, 193. Doorenbos J, Pruitt W O, 1977. Crop water requirements. In: FAO Irrigation and Drainage Paper No. 24. Rome: FAO, 144. FAO, 1979. Yield response to water. In: FAO Irrigation and Drainage Paper No. 33. Rome: FAO, 112–113. FAO, 1992. CROPWAT A computer program for irrigation planning and management. In: FAO Irrigation and Drainage Paper 46. Rome: FAO, 126. Gouranga Kar, Verma H N, 2005. Climatic water balance, probable rainfall, rice crop water requirements and cold period in AER 12.0 in India. Agricultural Water Management, 72: 15–32. Han Shumin, Cheng Yisong, Hu Chunsheng, 2005. Relationship between crop coefficient and precipitation pattern in the piedmont of Mt. Taihang. Agricultural Research in the Arid Areas, 23(5): 152–158. (in Chinese) Jensen M E, Burman R D, Allen R G, 1990, Evapotranspiration and irrigation water requirements. In: Manual and Reports on Engineering Practice. New York: American Society of Civil Engineers, 15–41. Martyniak L, Dabrowska-Zielinska K, Szymczyk R et al., 2006. Validation of satellite-derived soil-vegetation indices for prognosis of spring cereals yield reduction under drought conditions—Case study from central-western Poland. Advances in Space Research, 8: 1–6. Smith M, 1992. CROPWAT, A computer program for irrigation planning and management. In: FAO Irrigation and Drainage Paper 46. Rome: FAO. 1–65.