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Published in Agron J 100:862-873 (2008)
DOI: 10.2134/agronj2007.0226
© 2008 American Society of Agronomy
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CORN

Model-Based Approach to Quantify Production Potentials of Summer Maize and Spring Maize in the North China Plain

Jochen Bindera,*, Simone Graeffa, Johanna Linka, Wilhelm Claupeina, Ming Liub, Minghong Daib and Pu Wangb

a Institute of Crop Production and Grassland Research (340), Fruwirthstr. 23, Univ. of Hohenheim, D-70593 Stuttgart, Germany
b Dep. of Agronomy (243), Yuan Mingyuan West Road 2, China Agricultural University, 100094 Beijing, P.R. China

* Corresponding author (binderjo{at}uni-hohenheim.de).


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
The North China Plain (NCP) belongs to the major maize (Zea mays L.) growing areas in China. Maize yields have increased steadily since the 1980s, but in recent years average yields have stabilized around 5000 kg ha–1. The objective of this study was to quantify the production potential of summer and spring maize in the NCP. For this purpose the CERES-Maize model was calibrated and validated. The variability caused by climate was considered by using up to 30 yr of weather data from 14 meteorological stations across the NCP. Simulations were carried out for five different soil texture. Results were linked to a Geographic Information System (GIS). The results of the model calibration and validation showed a good fit between simulated and measured yield. Average simulated grain yield for summer maize was 4800 kg ha–1 and for spring maize was 5700 kg ha–1. Yields of summer maize were limited by the duration of the growing period. In order to increase spring maize yields, two strategies were developed. The first approach was to sow spring maize at a time when water deficit was least likely to occur during the late vegetative, flowering, and grain-filling stages. A delay in sowing of 30 d shifted maize development closer to the rainy season and increased average yield by 13%. In a second test the use of a variety with a later flowering date as a result of a longer vegetative growth led to an average increase in yield of 15%.

Abbreviations: GIS, Geographic Information System • IDW, inverse distance weighting • IRTG, International Research Training Group • NCP, North China Plain


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Received for publication June 28, 2007.
   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
ONE OF THE MOST important regions of agricultural production in China is the NCP, also known as the Huang-Huai-Hai Plain (Fig. 1A ). The NCP is located in the north of the eastern part of China between 32° and 40° N latitude and 100° and 120° E longitude.


Figure 1
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  		Fig. 1. (A) Provinces of the North China Plain and the border of the North China Plain (black line) (Source: Bareth, 2003, adapted and modified); (B) location of the meteorological stations (Source: China Meteorological Administration, 2007).

 
Within this region, maize is grown in 17% of the sown area and is just behind wheat as the most important crop. Currently, maize is one of the most important grains in China (Meng et al., 2006). In 2003, the total cropped area for maize in China reached nearly 24.1 million ha and the production exceeded 115.8 million Mg (China Statistical Yearbook, 2004). One-third of total maize production in China was produced in the provinces of the NCP. Since the 1980s, the sown area of maize in the provinces of the NCP increased continuously from 6.4 to 8.6 million ha..In general, maize production in NCP rose from 25.7 million Mg in 1984 to 38.1 million Mg in 2003.

Winter wheat (Triticum aestivum L.) and summer maize are currently the two main crops combined in a single-year rotation also referred to as a double cropping system (Zhao et al., 2006). Winter wheat is sown at the beginning of October and harvested in mid-June. Summer maize is sown immediately following winter wheat harvest and is harvested at the end of September or beginning of October. Afterward the rotation continues with winter wheat cultivation. Due to the short time frame, the growing period of summer maize is limited to 100 to120 d. Therefore only early or medium maturity maize cultivars characterized by a rather low yield potential can be used (Sun et al., 2007). Besides the dominant double cropping of winter wheat and summer maize, there is also the possibility of monocropping maize. Spring maize is sown in April and harvested in October (Geng et al., 2001), followed by a fallow period from October until the next April. The long growing period enables the use of medium and late maturity cultivars, characterized by a high yield potential.

The climate in the NCP (Tables 1 and 2 ) is warm temperate with cold winters and hot summers. Of the total rainfall, 50 to 75% occurs from July to September during the summer monsoon and overlaps with the growing season of maize (Zhang et al., 1999; Yang and Zehnder, 2001). Precipitation during the winter wheat growing season is very low and necessitates supplemental irrigation (Liu et al., 2001; Zhang et al., 2006). Due to the summer-dominant rainfall, normally no irrigation is required for summer maize and only a slight amount is required for spring maize. High amounts of irrigation water are essential for wheat production, but water has become more and more scarce in the NCP (Geng et al., 2001). Therefore, some studies have suggested reducing or even stopping the wheat production (Yang and Zehnder, 2001; Spyra and Jakob, 2004; Zhang et al., 2004). Alternatively plants with a higher efficiency of water utilization such as maize should be planted (Yang and Zehnder, 2001). Böning-Zilkens (2004) conducted field experiments with winter wheat and summer maize in Dongbeiwang located in the northwest of Beijing (40.0° N, 116.3° E) during 1999–2002. Different water and N-fertilizer scenarios and their effects on grain yield and water use efficiency (WUE) were tested. Average WUE values were 9.8 kg mm–1 ha–1 for winter wheat and 19.8 kg mm–1 ha–1 for summer maize.


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  		Table 1. Station key, name, province, longitude, latitude, elevation, and time period for the 14 weather stations used in the analysis.

 

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  		Table 2. Average main weather variables and their variance (brackets) for the weather stations used in the analysis.

 
In the provinces of the NCP average maize yields including both summer and spring maize showed a steady increase between 1984 and 1995, but recently have stabilized around 5000 kg ha–1 (China Statistical Yearbook, 1985–2004). The increase in yield can be attributed to increased amounts of N fertilizer, improved use of pesticides, better cultivars, new machinery and other enhanced management techniques. However, the potential for further increases in maize yields seems to be high, as the average yields are currently only half of the average of industrial countries (Yang and Zehnder, 2001).

In this paper we explore the production potential of summer and spring maize in the NCP using the CERES-Maize model linked to a GIS. The model results were analyzed to possibilities of increasing production in the NCP. The study contributes to the identification of limiting factors for improving maize yield in the NCP.

The specific objectives of this study were to (i) quantify the production potential of summer and spring maize at different locations in the NCP using a crop modeling and GIS method, (ii) identify the spatial and temporal variability of summer and spring maize yields due to climatic differences, (iii) classify the variability of summer and spring maize yields due to different soil texture classes and (iv) contribute to the identification of agronomic and cultivar parameters for yield improvements in spring maize.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Model Description
CERES-Maize is a process-oriented model that uses a daily time step simulation and has been integrated as a part of the Decision Support System for Agrotechnology Transfer (DSSAT v. 4.0) (Jones et al., 2003). The model simulates daily growth, development, and production of maize under given climatic and cultural conditions. Biomass and yield production is calculated as a function of radiation, leaf area index and reduction factors for temperature and moisture stress. Crop development is primarily based on growing degree-days, whereas leaf and stem growth rates are calculated depending on phenological stages. To run the model, a minimum dataset of management practices (cultivar, row spacing, plant population, fertilizer, and irrigation application amounts and dates) and environmental conditions (soil texture, daily maximum and minimum temperature, rainfall, and solar radiation) is required. The model was designed to predict how grain yield is affected under alternative management strategies, different environments or by crop variety, soil water and applied N. The CERES-Maize model is well documented and has been successfully tested in numerous studies to estimate maize yield (Hodges et al., 1987; Wu et al., 1989; Kovacs et al., 1995). The model has the potential for large area yield estimation where daily maximum and minimum temperatures, precipitation, and solar radiation data are available (Hodges et al., 1987).

Data for Model Calibration and Validation
The CERES-Maize model was calibrated and validated using data of experiments conducted at the experimental site Dongbeiwang in the northwest of Beijing (40.0° N and 116.3° E). The soil type was a Calcaric Cambisol (FAO Classification) formed from silt loam. Weather data including daily solar radiation, daily maximum and minimum air temperatures, and daily precipitation were from a local weather station. Information on crop management relative to cultivar, sowing date, sowing density, row spacing, irrigation, and N fertilization in the different field experiments used for model calibration and validation (Table 3 ).


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  		Table 3. Management data for model calibration and validation.

 
For summer maize, calibration was performed using 3 yr of field data from 1999 to 2002 (Böning-Zilkens, 2004). A double cropping system consisting of winter wheat and summer maize was tested relative to improving N and irrigation investments. The experiment was designed as a three factorial split-split-plot design with four replications. Three different irrigation regimes, three N-fertilization rates as well as a treatment with and without straw were tested. For spring maize the CERES-Maize model was calibrated using 2 yr of data collected from trials of the International Research Training Group (IRTG) (Binder et al., 2007). The chosen experiment was set up to compare different cropping systems in the NCP. The double cropping of winter wheat and summer maize was compared with a single rotation of spring maize. The field experiment was designed as a completely randomized single factorial block design with four replications.

Besides weather and site characteristics, maize yield in CERES-Maize depends on genetic coefficients (O'Neal et al., 2002). The required cultivar coefficients are: P1 (degree-days from seedling emergence to end of juvenile phase), P2 (degree-days from silking to maturity), P5 (delay in development per hour increase in photoperiod above 12.5 h), G2 (kernels per plant) and G3 (kernel filling rate). The calibration of these parameters followed a sequence of variables suggested by the DSSAT manual (Boote, 1999). Coefficients P1, P2, and P5 were calibrated against timing of phenological events and G2 and G3 against yield (Table 4 ).


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  		Table 4. Cultivar coefficients for summer maize (cv. Jingkeng 114, respectively, CF024) and spring maize (cv. CF1505) used for model calibration and validation.

 
The validation for summer maize and spring maize was performed using the genotype coefficients obtained by calibration (Table 4). For the two similar summer maize cv. Jingkeng 114 and CF024, the same cultivar coefficients were used. For summer maize the model was validated using 2 yr of field data collected in the IRTG-project. An independent experiment was used for spring maize. The chosen experiment for spring maize investigated the effects of three N-fertilization rates on yield in 2005. Fertilization treatments varied between 0 and 240 kg N ha–1yr–1. The field experiment was designed as a completely randomized single factorial block design with four replications.

Simulations
Settings
In general, model settings were based on data from the field experiments used for model calibration and validation. Simulations throughout the study area were performed with the same summer maize (Jingkeng 114 and CF024) and spring maize (CF1505) cultivars. The sowing density was seven plants m–2 and row spacing was 0.7 m for both summer and spring maize. Due to climate conditions across the NCP, sowing dates were delayed from south to north (Wu et al., 1989) and varied between 6 to 24 June for summer maize and 5 April to 5 May for spring maize (Fig. 2 ).


Figure 2
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  		Fig. 2. Sowing date variation for spring and summer maize across the North China Plain.

 
In a normal year, precipitation can generally meet water requirements of summer maize, but problems occur in dry years. Therefore, summer maize was irrigated after sowing with 50 mm to guarantee seedling emergence and establishment. Spring maize was irrigated with 90 mm at sowing and 50 mm 50 d after sowing. As irrigation is performed by furrow irrigation, irrigation efficiency was set to 0.50, according to the China Internet Information Center (2001). Summer maize was fertilized with 50 kg N ha–1 20 d after sowing and 90 kg N ha–1 50 d after sowing. Nitrogen fertilization for spring maize was 70 kg N ha–1 applied 50 d after sowing. Nitrogen fertilization for summer maize was higher than for spring maize due to the fact that two crops (winter wheat and summer maize) are grown on the same plot each year and to accelerate growth in the shorter vegetation period. Spring and summer maize harvests occurred at physiological maturity at the end of September.

Soil Data. Since potential yields may be affected by location-specific soil water characteristics, different simulation scenarios were set up to account for spatial variability in soil across the NCP. Simulations were based on the soil texture classes sand, sandy loam, loam, silt loam, and silt which occur in the study area (Table 5 ). The soil data were obtained from Chinese Academy of Science (1997) and Böning-Zilkens (2004).


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  		Table 5. Physical properties of different soil texture classes used in the analysis (Source: Böning-Zilkens, 2004, and Chinese Academy of Science, 1997, adapted and modified).

 
Climate Data. Weather data from 14 stations evenly distributed in the NCP were obtained from China Meteorological Administration (2007) (Fig. 1B). Time series for the different sites ranged from 10 to 30 yr. The data included daily maximum and minimum temperature, rainfall, and sunshine duration. Sunshine duration was converted to solar radiation (MJ m–2 d–1) by the Angström equation using the recommended default coefficients 0.25 and 0.50 (Allen et al., 1998). Information on chosen locations relative to identity, longitude, latitude, elevation, and time series is given in Table 1. The NCP is a vast expanse of flatland with a slight slope from southwest to northeast. The average elevation is <100 m (Yao, 1969), so it was hypothesized that the topography does not have any influence on the climate within the plain.

Simulations were performed with the weather data described for the 14 locations shown in Fig. 1B and for the five soil texture classes sand, sandy loam, loam, silt loam, and silt (Table 5). The point data were interpolated to generate maps for the entire NCP. For interpolation the inverse distance weighting (IDW) method (Shepard, 1968) was used, which identified a neighborhood about the interpolated point. Afterward a weighted averaged was taken for the observed values within this neighborhood. The weights are a decreasing function of distance (Fisher et al., 1987).


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Model Calibration and Validation
From the calibration and validation results, the CERES-Maize model was found to simulated yields well (Table 6 ). The average RMSE between simulated and measured yield for summer maize was 316 kg ha–1 (calibration) and 356 kg ha–1 (validation). Similar results were obtained for spring maize with an average RMSE of 314 kg ha–1 (calibration) and 340 kg ha–1 (validation).


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  		Table 6. Average results of model calibration and validation for summer maize and spring maize.

 
Many researchers have demonstrated reasonably accurate simulations of grain yield because the CERES-Maize model has been in existence for many years and has been the subject of continuous evaluation. Liu et al. (1989) used the CERES-Maize model to simulate the growth and grain yield of a Brazilian maize hybrid in the years 1983 to 1987. Estimated yields were within 10% error range except for 1 yr. Another example is given by Mati (2000) who used the CERES-Maize model to simulate the crop response to changes in climate, management variables, soils, and different CO2 atmospheric levels in the semi-humid–semiarid areas of Kenya. With an error of 5 to 10% after calibration the model was found to simulate yields well within acceptable limits. The results of our simulations showed in general that the differences between the simulated and measured grain yields were within the range of differences reported in the literature. Thus, the model simulated grain yields adequately to proceed with simulating potential yield over regions of NCP.

Spatial and Temporal Variability of Climate
Spatial and temporal variability of precipitation, average daily temperature, and average daily solar radiation in the NCP during the summer maize (June–September) and the spring maize grow season (April–September) were analyzed.

Precipitation varied between 233 and 597 mm in the summer maize growing season and between 401 and 708 mm in the spring maize growing season. However, the average monthly perception for summer maize was higher than for spring maize, because the greatest amounts occurred during the summer months. Precipitation was higher in the south than in the north of the NCP and increased from west to east (Fig. 3A ). With a coefficient of variation between 0.28 (minimum) and 0.41 (maximum) for spring maize respectively 0.31 and 0.54 for summer maize precipitation indicated a high variability between years. This corresponds with results of Yao (1969) who reported an extremely high year-to-year variability of precipitation in the NCP.


Figure 3
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  		Fig. 3. (A) Precipitation amount (mm), (B) daily average temperature (°C), and (C) average solar radiation (MJ m–2 d–1) during the spring maize (April–September) and summer maize (June–September) growing season.

 
Average daily temperatures increased from north to south and decreased from west to east (Fig. 3B). During the summer maize growing season average daily temperature varied between 23.2 and 26.3°C and during the spring maize growing season between 20.4 and 23.8°C. Average daily temperature showed a low variability between years, that is, the coefficient of variation varied between 0.02 and 0.04 for spring as well as for summer maize.

Solar radiation varied between 18.3 and 19.7 MJ m–2 d–1 in the summer maize growing season and between 18.5 and 20.2 MJ m–2 d–1 in the spring maize growing season. Radiation increased from south to north due to lower rainfall and latitude, resulting in less cloudy days and more sunshine hours per day in the growing seasons (Fig. 3C). The coefficient of variation between the years varied between 0.04 and 0.07 for spring maize and between 0.05 and 0.08 for summer maize.

Precipitation, temperature, and solar radiation have direct effects on maize growth and yield. Precipitation provides soil water essential for emergence and plant establishment at sowing, for an adequate leaf area development and photosynthesis rate during the preflowering time, and for increasing ear and kernel set during the 2 wk bracketing the flowering stage. Temperature also has a big effect on maize growth as maize is very sensitive to frost, particularly in the juvenile stage. Besides, increasing temperatures accelerates the plant development. Badu-Apraku et al. (1983) showed that increasing temperature cause decreased duration of grain filling resulting in lower grain yields. Further, growth of maize is very responsive to solar radiation, because radiation is the main source of energy for biomass synthesis (Idinoba et al., 2002). The dry matter produced is directly related to the amount of intercepted radiation. Muchow et al. (1990) showed that a low temperature and a high solar radiation results in high maize yields, because lower temperature increase the length of time that the crop can intercept radiation.

However maize varieties have a wide adaptability to different climate conditions (Shaw, 1988). Therefore the right choice of varieties with a length of growing period matching well with the length of the growing season is crucial for successful cultivation (Doorenbos and Kassam, 1979).

Spatial Variation in Potential Yields of Summer Maize and Spring Maize
According to the simulation analysis, long-term average potential yields of summer maize ranged from 3900 kg ha–1 on sand to 5800 kg ha–1 on silt loam (Table 7 ). The spring maize yields were lowest for sand and sandy loam (4800 kg ha–1) and highest for silt loam (6600 kg ha–1). The overall mean grain yields for the entire NCP were 4800 kg ha–1 for summer maize and 5700 kg ha–1 for spring maize. Average total biomass production for summer maize was 10,700 kg ha–1 (8500–13,500 kg ha–1) and for spring maize was 14,200 kg ha–1 (12,500–15,900 kg ha–1).


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  		Table 7. Average summer maize and spring maize yields (kg ha–1) and yield difference (kg ha–1) for the tested soil texture classes.

 
In general, yields decreased from north to south (Fig. 4 and 5 ) even if the potential growing season was longer in the south. This was due to the lower radiation and higher temperature in the south. Along the same latitude, yields increased with longitude from west to east, because lower temperatures at the coast extend the grain-filling phase. The highest yields of summer and spring maize were realized at the most northeast meteorological station Laoting. The lowest summer maize as well as spring maize yields were obtained at Huimin station due to water shortage. As crop photosynthesis and hence biomass and grain yield production are directly associated with the light interception by the canopy (Muchow et al., 1990), yields increased in the direction from southwestern to northeastern.


Figure 4
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  		Fig. 4. Simulated potential yields (kg ha–1) of summer maize for different soil texture classes [(A) sand, (B) sandy loam, (C) loam, (D) silt loam, and (E) silt] in the North China Plain.

 

Figure 5
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  		Fig. 5. Simulated potential yields (kg ha–1) of spring maize for different soil texture classes [(A) sand, (B) sandy loam, (C) loam, (D) silt loam, and (E) silt] in the North China Plain.

 
Yield Difference between Summer Maize and Spring Maize and Possible Developments
The yield difference between summer and spring maize varied from 810 kg ha–1 on silt loam to 1030 kg ha–1 on loam (Table 7). Due to lower temperatures interrelated with higher radiation and higher rainfall, yield distinction increased from west to east (Fig. 6 ). For the entire NCP the mean yield distinction was 900 kg ha–1. However, considering the longer growing period of spring maize (165 d) in comparison to summer maize (110 d), differences in yield are quite small and sometimes negative. This is in agreement with results of Beck et al. (2002) who reported no major yield differences between summer and spring maize in the Loess Plateau.


Figure 6
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  		Fig. 6. Mean differences (kg ha–1) between summer and spring maize yields for different soil texture classes [(A) sand, (B) sandy loam, (C) loam, (D) silt loam, and (E) silt] in the North China Plain.

 
Water stress and high temperatures during ear formation, reproduction, and grain filling may be responsible for the small differences in yield between summer and spring maize. Maize is highly sensitive to water deficit in specific growth stages. Sensitivity varies among different growth stages (Salter and Goode, 1967; Claassen and Shaw, 1970; Grant et al., 1989). Commonly grain crops are more sensitive to water stress during flowering and early seed formation than during vegetative or grain-filling phases (Doorenbos and Kassam, 1979). This is in agreement with results of Denmead and Shaw (1960) and Grant et al. (1989) who found that maize water demand peaked in the period 1 wk before until 2 wk after flowering. Similarly Dale and Daniels (1992) reported a peak in water demand in the period 4 wk before silking, ending about 20 d after silking. Drought at flowering often results in barrenness caused by a reduction in the flux of assimilates to the developing ear below some threshold level necessary to sustain grain formation and growth (Schussler and Westgate, 1995). According to Bänzinger et al. (2000), maize is more susceptible than other corps to water stress at flowering because of the large distance between male and female organs, exposing pollen and fragile stigmatic tissue to desiccating conditions during pollination.

In our study water stress at flowering was also detected as the major factor responsible for the small differences in yield between summer and spring maize. The CERES-Maize model considers stress related to water using water deficit factors. When the soils dry and the potential root water uptake decrease to a value lower than the potential transpiration rate, actual transpiration will be reduced by partially closed stomata to the potential root uptake rate. When this happens the potential biomass production rate is assumed to decline in the same proportion as the transpiration. The potential transpiration and biomass production rates are reduced by multiplying their potential rates by a soil water deficit factor calculated from the ratio of the potential uptake to the potential transpiration. This value is set to 1 when the ratio exceeds 1. A second water deficit factor is calculated to account for water deficit effects on phytophysiological processes that are more sensitive than the stomata controlled processes of transpiration and biomass reduction. Reduced turgor pressure in many crop plants will slow down processes such as leaf expansion, branching, and tillering before stomata-controlled processes are reduced. Values for the second factor are assumed to fall below 1 when potential root uptake relative to potential transpiration falls below 1.5. They are assumed to be reduced linearly from 1 to 0 in proportion to this ratio (Ritchie, 1998). In our simulation the CERES-Maize model indicated average values at flowering of 0.13 for spring maize and 0.04 for summer maize. This corresponds to the differences in the amount of precipitation during the period from flowering initiation until the beginning of grain filling (spring maize: end of May until the beginning of July; summer maize: end of June until the beginning of August). On average, precipitation during this period amounted 207 mm for summer maize and 139 mm for spring maize. Taking into account an effective irrigation amount of 25 mm (irrigation efficiency was set to 0.50) for spring maize at the beginning of flowering, there is still a water deficit for spring maize of 43 mm less water compared to summer maize. However, there is a strong variation in the occurring water deficit between the single locations. In Yanzhou or Laoting for example the water deficit for spring maize during flowering period could almost be completely compensated by irrigation at flowering initiation. Therefore, spring maize outyielded summer maize at these two locations. On the other hand, there are also locations such as Anyang or Jinan where the water deficit could not be compensated by irrigation. As a consequence, spring maize yields there were rather low.

Timing and intensity of drought stress can be managed by irrigation. If water stress limits growth, irrigation at the most water-sensitive growth stages may result in higher yields per unit water compared to water applied during other growth stages (Stone and Schlegel, 2006). Therefore, a better irrigation management could help increase spring maize yields whereas for summer maize, no further yield increase can be expected. In our simulation two irrigations for spring maize (at sowing and 50 d after sowing) were applied. In wet years the first irrigation was not needed. However in dry season, it was needed to guarantee seedling emergence and establishment. The second irrigation took place in the water sensitive period reported by Dale and Daniels (1992). Therefore possibilities for improvements in irrigation scheduling can scarcely be proposed. A higher or an additional irrigation application seems also to be a problem because water in the NCP is scarce and its availability will be further reduced by the competition of nonagricultural users (Liu et al., 1998). A further way to save water might be the adoption of advanced irrigation technologies and a better maintenance of irrigation infrastructure (Wolff, 1999).

Another management practice to reduce water stress is to reduce maize plant population to maintain the amount of available water per plant above a minimum. Some other agronomic practices such as mulching or reduced tillage might also help to improve the water conditions (Scopel et al., 2001) and there should be further tested.

Another alternative could be to use cultivars with higher water-use efficiency. In general, the available cultivars differ widely in agronomic characteristics, for example, in the length of growing period. Longer growth duration is often associated with a higher yield potential (Olson and Sander, 1988). Consequently a delay in sowing especially when the moisture environment is sufficient leads to yield losses. However moisture environment is unpredictable and may vary to a large extent between years. Therefore, the object of agronomic practices must be to sow maize at a time when water deficit is least likely to occur during the late vegetative, flowering and grain-filling stage. This could be reached by shifting the sowing date closer to the rainy season. In our simulations a delay in sowing of 30 d brought an average yield increase of 13% (Fig. 7 ). However in some regions such as Laoting a yield decrease was ascertainable because the sowing date was already relatively late. Therefore a more site-specific variation in sowing seems to be required.


Figure 7
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  		Fig. 7. Possible yield levels (kg ha–1) of spring maize by a later sowing date for different soil texture classes [(A) sand, (B) sandy loam, (C) loam, (D) silt loam, and (E) silt] in the North China Plain.

 
Another strategy to improve spring maize yields could be to find maize genotypes with appropriate phenology matching growth and developmental processes with environmental conditions. For environmental stress conditions cultivars diversification is based mainly on differential phenology, primarily flowering date. Maize is relatively insensitive to water stress during early vegetative growth stages because water demand is relatively low (Shaw, 1988). In our simulations the use of a cultivar with a later flowering date as a result of a longer vegetative growth led to an average increase in yield of 15% (Fig. 8 ). Late flowering leads to a longer vegetative growing period that promotes the accumulation and allocation of more resources to seed production. Olson and Sander (1988) previously recommended the use of a cultivar that will not be in the critical flowering stage during the time when a stress period can be expected.


Figure 8
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  		Fig. 8. Possible yield levels (kg ha–1) of spring maize by the use of adapted cultivars for different soil texture classes [(A) sand, (B) sandy loam, (C) loam, (D) silt loam, and (E) silt] in the North China Plain.

 
Combining both a delay in sowing and the use of a cultivar with a later flowering date led to an average increase in yield of 32% (Fig. 9 ).


Figure 9
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  		Fig. 9. Possible yield levels (kg ha–1) of spring maize by a later sowing date in combination with the use of adapted cultivars for different soil texture classes [(A) sand, (B) sandy loam, (C) loam, (D) silt loam, and (E) silt] in the North China Plain.

 

   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
This paper presents a simulation approach to quantify the potential yields of spring and summer maize across the NCP. Climate-related temporal and spatial variability in yield performance were simulated using long-term daily weather data from various meteorological stations spread evenly throughout the NCP. Simulations were performed for five different soil texture classes. Results of the simulations indicated that inspite of a longer growing season of spring maize the simulated yield difference in comparison to summer maize was relatively small, due to water stress during flowering. However, shifts in sowing dates to a later point in time and the use of late flowering varieties may facilitate an increase in spring maize yields.


   ACKNOWLEDGMENTS
 
This work was funded by the Deutsche Forschungsgemeinschaft (GRK 1070, IRTG Sustainable Resource Use).

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   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 





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