Temperature and Precipitation Suitability Evaluation ... - Science Direct

Agricultural Sciences in China

February 2011

2011, 10(2): 275-288

Temperature and Precipitation Suitability Evaluation for the Winter Wheat and Summer Maize Cropping System in the Huang-Huai-Hai Plain of China

Vietnam National Museum of Nature, Vietnamese Academy of Science and Technology, Ha Noi 10000, Vietnam Key Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, P.R.China 3 Laboratory of Soil Science, Department of Geology and Soil Science (WE13), Ghent University, B-9000 Ghent, Belgium 1 2

Abstract The Huang-Huai-Hai Plain is the most important winter wheat and maize production region in China. In response to the increasing population pressure, the Chinese authorities strongly invested in improving the irrigation systems and increasing ground and surface water exploitation within the plain to increase the crop productivity. This resulted in a reduction of water resource availability and in turn indirectly affected the suitability of various agricultural ecosystems in the plain. The main purpose of this study was to review the suitability of temperature and precipitation for the winter wheat and summer maize cropping system in the Huang-Huai-Hai Plain, in order to provide a preliminary irrigation scheme. This analysis provides a first attempt to enhance the water resource management as well as to increase the water use efficiency. For this aim, a GIS-based multicriteria analysis procedure has been developed consisting of (1) definition of objectives (evaluated entities) and database building; (2) definition of evaluation criteria; (3) standardization of the criteria; (4) combination of the criteria; (5) classification of the objectives; and (6) integration of the objectives. The land suitability classification maps were transformed into maps of required irrigation amounts for each growing stage of the winter wheat and summer maize cropping system. The study also exemplified the limitations and proposed future research activities that will improve the detail and accuracy of the evaluation results. Key words: GIS, multicriteria, Huang-Huai-Hai Plain, precipitation, suitability, irrigation

INTRODUCTION The Huang-Huai-Hai Plain, one of the largest plains in China, is located in the north of the country and extends from 32 to 42°N and from 113 to 120°E, stretching over an area of about 350 000 km². Its population is densely concentrated in the northeastern edge of the plain, in the vicinity of the capital, Beijing. The plain is mainly formed by the alluvial deposits of the Yellow, Huai, and Hai rivers. Almost the entire plain is found at Received 24 June, 2010

an altitude below 50 m above sea level and the slope gradient is less than 3°. The climate is temperate, subhumid, and continental monsoon with a cumulative temperature (>0°C) of 4 200 to 5 500°C, a frost-free period of about 170 to 220 d and average annual precipitation ranging between 500 and 800 mm (Ren et al. 2008). The annual rainfall concentrates in the summer period, from July to September. Winter time, on the other hand, is characterized by a lack of water for agricultural production. Although precipitation is insufficient for cultivation in the North China Plain (including

Accepted 18 October, 2010

Correspondence QIU Jian-jun, Professor, Tel: +86-10-82106231, Fax: +86-10-82106231, E-mail: [email protected]

© 2011, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(11)60005-9

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Nguyen Thanh Tuan et al.

the Huang-Huai-Hai Plain), it is the largest agricultural production area in China, accounting for 50% of the wheat and 30% of the maize grain production in China (Wang et al. 2009). According to Li et al. (2009), the winter wheat and summer maize cropping system has been one of the most popular cropping systems in China and especially in the Huang-Huai-Hai Plain. Based on the statistical data published by the Bureau of Statistics of China in 2007, Li et al. (2009) reported that the Huang-Huai-Hai Plain provided 42.3% of the total national winter wheat and summer maize production with intensive management characterized by the application of sufficient irrigation water and fertilizers. The irrigation system, excessively using ground and surface water resources, has however severely deepened the ground water table (Hu et al. 2005) and reduced the available surface water (Xu 2002). As a while, temperature is suitable for the double cropping system in the area, but, in some places, low temperature often occurs during plant growing period and has greatly affected grain production. This study was to review the suitability of temperature and precipitation in the winter wheat and maize cropping system in the Huang-Huai-Hai Plain for providing a preliminary irrigation scheme to improve the water resources management and water use efficiency.

MATERIALS AND METHODS Climatic data Climatic data of 33 stations distributed across the plain were collected from the National Meteorology Bureau of China. It comprised the daily maximum and minimum temperatures and precipitation records from 2005 to 2006. The geographical location of each station was stored in an ArcView 3.2 shape file using the geographic coordinate system.

Crop data Winter wheat (Triticum aestivum L.) was sown in October 2005 and harvested in early June of 2006. Summer maize (Zea mays L.) was planted immediately after wheat harvesting and was harvested in October 2006. This is in agreement with the cropping calendar published by Zhang et al. (2006a, 2008) and where the winter wheat and summer maize cropping system was cultivated in the plain from October 1, 2005 to October 5, 2006. The sowing and harvesting dates, as well as the start and end dates of the different stages in the growing cycle of winter wheat and maize, are shown in Table 1.

Table 1 Average start and end dates of the crop growth stages of winter wheat and summer maize in the Huang-Huai-Hai Plain, China (d/mon) Winter wheat Date

Seedling

Start End

01/10 15/10

Early

Vegetative Over wintering period

Late

16/10 10/11

11/11 06/03

07/03 06/04

Flowering

Yield formation

Ripening

07/04 27/04

28/04 30/05

31/05 10/06

Summer maize Date

Seedling

Vegetative

Flowering

Yield formation

Ripening

Start End

10/06 30/06

01/07 31/07

01/08 20/08

21/08 25/09

26/09 05/10

Methods Interpolation methods In order to generate maps of the relevant climatic criteria, both deterministic and geostatistical interpolators were considered. The former methods are based on either the extent of similarity or the degree of smoothing of the observed data points to produce surfaces. Examples are the Inverse Distance Weighted (IDW) and Radial Basis Functions-Spline

methods. Geostatistical interpolation methods, such as Kriging, use the statistical properties of the observed data points to create surfaces. In this study, IDW, Spline, and Kriging methods were applied. The Spline and IDW methods are exact interpolators, producing a surface that must go through each measured value. Consequently, they are appropriate for surfaces without large and/or sudden changes at short distances. Among the various existing Kriging techniques, both

© 2011, CAAS. All rights reserved. Published by Elsevier Ltd.

Temperature and Precipitation Suitability Evaluation for the Winter Wheat and Summer Maize Cropping System

exact and inexact methods could be selected, depending on the measurement error model (ESRI 2009). Selection of the most accurate interpolation method for the interpolation of each criterion relied on the root mean square error (RMSE) and the index of agreement (d). Willmott (1984) reported the following formulas, (1), (2), and (3), to calculate these indices: RMSE =

(1) (2)

(3) With N being the sample number, is the observed mean; Pi is the ith predicted value and Oi representing the ith observed value. The index of agreement ranges from 0 to 1. Value 1 expresses perfect agreement between observed and predicted values while 0 expresses disagreement (Willmott 1984). The RMSE indicates how closely the predicted values correspond with the measured values: the smaller the RMSE, the better the method is (Luo et al. 2008). Therefore, the RMSE was considered as a more important criterion than the index of agreement criterion to select the most accurate methods.

GIS-based multi-criteria analysis The method consists of the following steps: (1) definition of objectives (evaluated entities) and database building; (2) definition of evaluation criteria; (3) standardization of the criteria; (4) combination of the criteria; (5) classification of the objectives; and (6) integration of the objectives. In this case, the database was built by the above mentioned interpolation methods. Definition of objectives In general, the specific objectives of a multi-criteria analysis, being statements of the desired state of the system under consideration, depend on the development needs and land use planning goals within the area of interest. Selection and design of evaluation criteria According to Malczewski (1999), each objective is functionally related to a set of attributes, indicating the degree to which the objective can be achieved. As such, for each objective and attribute, evaluation criteria need to

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be designed. These criteria can be developed using experimental expertise and/or literature data. The temperature and rainfall criteria for summer maize and winter wheat, used in this study, were developed on the basis of their climatic requirements published by Sys et al. (1993) and adapted to the local environmental conditions and cultivars applied in the Huang-Huai-Hai. Different criteria have been developed for different development stages of both crops. Each criterion was evaluated using 4 classes, being very suitable (S1), moderately suitable (S2), marginally suitable (S3), and unsuitable (N). Standardization of the criteria There exist different approaches to standardize the criteria, such as linear transformation and membership functions (MF). Selection of the most relevant method for standardization depends on the research objectives, scale of input data, as well as on the envisaged accuracy. In this study, MFs were used to define the grade of membership (suitability) of members in a finite set (space) of attributes. Standardized results represent a zone of gradual transitions, and as such the accuracy of output could be improved. The grade value ranges between 0 and 1. A grade of 0 indicates that an attribute has a complete non-membership (unsuitable) in a fuzzy set. On the other hand, a grade of 1 indicates that an attribute has a complete membership (very suitable) in a fuzzy set. Grades between 0 and 1 imply that an attribute has a partial membership in a fuzzy set. The MFs were selected based on the criteria-specific behavior with respect to crop yield, the considered objective, as well as the MF characteristics. More specifically, the trapezoidal membership function (MF), being continuous and symmetric, with values ranging between 0 and 1, is especially relevant for criteria having a non-linear but symmetric relationship with crop yield. It can be applied when the threshold values defining the marginal and unsuitable classes can be estimated (Nguyen 2009). Therefore, this function has been selected to standardize the criteria of the precipitation during the growing cycle and the monthly precipitation during the vegetative and flowering stages for winter wheat, as well as the maize’s criteria related to precipitation. This function is expressed in the following formula (4):


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(4)

Where a, b, c, and d are threshold values defining suitability levels. The Kandel extension MF has been used to standardize the winter wheat’s criterion of monthly precipitation during the ripening stage. This MF is relevant for criteria with continuous and symmetric values, but without having threshold values defining the unsuitable and/or marginal suitability class (Nguyen 2009) [eq. (5)]:

(5)

Where, b1 and b2 are threshold values determining the optimal class; d is the difference between b1 or b2 and the value at the cross point. Combination of the criteria (a) Distinction of limiting and non-limiting criteria: Before the criteria were combined, a distinction was made between limiting and non-limiting criteria, having an impact on the selection of the most appropriate combination approach. Limiting criteria for crop cultivation have been defined as criteria whose unsuitable attribute values can not be improved by man. In this case, the temperature-related criteria have all been considered as limiting. Nonlimiting criteria, on the other hand, are criteria whose unsuitable attribute values can be improved or compensated through adapted management. Irrigation for instance is a way to solve problems related to unsuitable rainfall amounts (Nguyen 2009). (b) Definition of weights for the non-limiting criteria: Determination of the weighting factor assigned to each

criterion is one of the most difficult steps in the multicriteria analysis. In this study, a ranking method based on the relative importance of the criteria was applied. More specifically, the straight ranking procedure has been used, i.e., the most important criterion received a rank of 1, the second important a rank of 2, etc. The weights were then calculated using the rank reciprocal approach [eq. (6)] (Malczewski 1999). (6) Where, wj, the normalized weight for the jth criterion; k=1, 2, …, n; n, the number of the criteria under consideration; rj, the rank position of the criterion. The relative importance of the criteria was determined by the expert taking into account the potential for improvement, the number of suitable classes reported in the study area, and the aerial extent of these classes. (c) Combination of the criteria: Additive and multiplicative combination methods often have been used to combine the criteria. The additive method [eq. (7)] is used to integrate non-limiting criteria. It entails compensation of less favourable criteria by favourable ones. The multiplicative method [eq. (8)] is used to combine the overall score layer of the non-limiting criteria with the limiting criteria layers. In this study we only applied the additive combination method since only the non-limiting criteria (related to rainfall) turned out to be relevant and were retained in the final analysis. (7) Where, y i is the overall score for ith crop (ith alternative); wj is the weight of jth criterion, = 1; xij, the score of jth criterion for ith crop (8) Si = A × B × C × D × … Where, S i is the overall score for ith crop (ith alternative); A is the score of criterion A; B is the score of criterion B; C is the score of criterion C; D is the score of criterion D; … Classification of the objectives This classification allows determining the suitability of temperature and precipitation for maize and winter wheat cultivation in



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the study area. Equally spaced classes between 0 and 1 were used to classify the suitability, as shown in Table 2. Integration of the objectives The resulting land suitability data for maize and winter wheat were combined to identify those areas that are suitable for the winter wheat and summer maize cropping system in the study area.

precipitation thresholds that achieved 95% of maximum attainable yield were therefore considered for optimal wheat production. These threshold rainfall amounts of both crops were compared with the precipitation records in the study area and the required irrigation applications were calculated.

Table 2 Relationship between land suitability classes and overall scores

RESULTS

Suitability class

Overallscores

Very suitable Moderately suitable Marginally suitable Unsuitable

0.75-1.00 0.50-0.75 0.25-0.50 0.00-0.25

Estimating irrigation water requirements in different crop growth stages The amount of surplus (irrigation) water that would be required to cover the crop water demands in each crop development stage has been estimated. The specific temperature and precipitation threshold values in development stage of winter wheat and maize, indicating very suitable conditions, were derived from the requirement tables published by Sys et al. (1993). According to Table 3, the optimal water supply during the first month of the crop cycle of maize amounts to 175 mm. A monthly water supply of 100 mm is the lower threshold value of this very suitable class. For winter wheat, however, the threshold rainfall amounts derived from Table 4 increased as this crop is grown during the dry season with a larger likelihood for drought stress. The

Selected temperature and precipitation criteria Although the growing cycle of maize was shorter than that of winter wheat, maize required the highest precipitation (Tables 3 and 4). On the other hand, winter wheat could grow in low temperature conditions during its early growth stages, being capable to survive at -20oC (Sys et al. 1993). However, compared to maize, wheat required higher temperatures during its flowering and ripening stages.

Selection of appropriate interpolation methods and final criteria The RMSE and the index of agreement of the interpolation methods for each selected criterion were shown in Table 5. The RMSE and index of agreement of the best interpolators for each criterion have been indicated in bold. In most cases, the differences in accuracy obtained by the different interpolators were small and insignificant.

Table 3 Temperature and precipitation criteria for maize cultivation in the Huang-Huai-Hai Plain, China (Sys et al. 1993) Criteria11) Precipitation of GC (mm) Precipitation of the 1st mon (mm) Precipitation of the 2nd mon (mm) Precipitation of the 3rd mon (mm) Precipitation of the 4th mon (mm) Mean temperature of the GC (°C) Mean min. temperature of GC (°C) 1)

Suitable classes S1

S2

S3

N

750-1 200 750-500 175-295 175-100 200-310 200-150 200-310 200-150 165-285 165-100 24-18 24-32 17-12 17-24

1 200-1 600 500-400 295-400 100-75 310-400 150-120 310-400 150-120 285-400 100-80 18-16 32-35 12-9 24-28

> 1 600 400-300 400-475 75-60 400-475 120-70 400-475 120-70 400-475 80-60 16-14 35-40 9-7 28-30

< 300 > 475 < 60 > 475 < 70 > 475 < 70 > 475 < 60 < 14 > 40 30

GC, growing cycle. The same as below.


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Table 4 Temperature and precipitation criteria for winter wheat cultivation in the Huang-Huai-Hai Plain, China (Sys et al. 1993) Suitable class

Criteria1) Precipitation of GC (mm) Monthly precipitation of VS (mm) Monthly precipitation of FS (mm) Monthly precipitation of RS (mm) Mean temperature of the GC (°C) Mean temperature of the VS (°C) Mean temperature of the FS (°C) Mean temperature of the RS (°C) 1)

S1

S2

S3

N

700-350 700-1 250 65-20 65-120 75-30 75-120 60-30 60-100 18-23 18-12 10-6 10-18 18-12 18-26 20-14 20-30

350-250 1 250-1 500 20-12 > 120 30-15 > 120 30-10 100-200 23-25 12-10 6-4 18-24 12-10 26-23 14-12 30-36

250-200 1 500-1 750 12-8 15-10 < 10 > 120 25-30 10-8 4-2 24-28 10-8 32-36 12-10 36-42

< 200 > 1 750 30 42

VS, vegetative stage; FS, flowering stage; RS, ripening stage. The same as below.

Table 5 RMSE and the index of agreement of the interpolation methods for each selected criterion in the Huang-Huai-Hai Plain, China

Precipitation of GC Monthly precipitation of VS Monthly precipitation of FS Monthly precipitation of RS Mean temperature of GC Mean temperature of VS Mean temperature of FS Mean temperature of RS

d

RMSE

D

RMSE

d

37.6900 13.8300 6.4790 2.5150 0.5786 0.6154 0.5809 0.8263

0.7520 0.9091 0.7735 0.6643 0.9118 0.9485 0.9618 0.4401

35.0000 14.1200 5.3880 2.4790 0.5477 0.5609 0.5767 0.7877

0.8106 0.9054 0.8767 0.7140 0.9246 0.9594 0.9616 0.5660

34.9400 14.4900 5.3400 2.4490 0.5493 0.5536 0.6030 0.7893

0.8089 0.9097 0.8677 0.7409 0.9282 0.9616 0.9602 0.5002

D

RMSE

d

0.7776 0.8114 0.8990 0.9306 0.5518 0.5853 0.9772

0.3923 0.6202 100.3000 64.3400 65.7900 45.7600 5.7260

0.7810 0.8193 0.9050 0.9257 0.5784 0.5824 0.9690

Maize Spline

IDW RMSE

1)

Kriging

RMSE

Criteria Mean temperature of GC Mean minimum temperature of GC Precipitation of GC Precipitation of 1st mon Precipitation of 2nd mon Precipitation of 3rd mon Precipitation of 4th mon

Winter wheat Spline

IDW

Criteria 1)

0.4086 0.6328 101.4000 55.9700 65.5000 47.7200 9.5310

d

RMSE

0.7395 0.7985 0.8850 0.9364 0.4720 0.4034 0.8899

0.3959 0.6254 100.7000 60.3300 64.3700 46.3700 5.1240

Kriging

IDW, inverse distance weighted; RMSE, root mean square error; d, index of agreement.

Using the most appropriate interpolation method, the criteria layers were interpolated for the whole Huang-HuaiHai Plain from the 33 climatic stations. The interpolated results indicated that the mean temperature during the winter wheat growing cycle and its vegetative, flowering, and ripening stage range from 13 to 19, 9 to 16, 10 to 17, and 23 to 27°C, respectively, indicating that the temperature in the Huang-Huai-Hai Plain was very suitable for winter wheat cultivation. The over wintering period during the vegetative stage of winter wheat was not considered in the current calculations. Similarly, the temperature conditions in the plain were

also very suitable for summer maize cultivation. The mean and mean minimum temperature during the maize growing cycle ranged from 24 to 27 and from 19 to 23°C, respectively. The precipitation criteria were taken into consideration in the subsequent evaluation steps.

Standardized evaluation criteria using scoring functions The approach to standardize criteria using membership functions has been illustrated using the example of the



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precipitation during the growing cycle criterion for winter wheat cultivation. In order to standardize this criterion, [eq. (9)], based on the trapezoidal MF, has been utilized. In Fig. 1, the spatial distribution of the standardized precipitation during the growing cycle criterion for winter wheat cultivation in the plain is illustrated. It shows that almost the entire plain is characterized by scores below 0.1. Consequently, the precipitation during the growing cycle of winter wheat is unsuitable; the rainfall water supply is insufficient to cover its water demands.

(9)

The other criteria for winter wheat and summer maize cultivation in the plain have been standardized based on the selected MFs following a similar procedure.

Weights of non-limiting criteria Criteria related to precipitation in the plain were considered as non-limiting criteria since they could be improved by irrigation. The weights of these criteria are shown in Table 6. Precipitation amounts recorded in the study area during the winter wheat growing cycle were generally less than 200 mm. When matched with the requirements in Table 4, this implied that the plain is not suitable for winter wheat cultivation. Since this criterion considerably affects the entire growing process and production of winter wheat across a large area, it was considered being most important, and thus it received a weight of 0.48 (Table 6). Unsuitable precipitation amounts during winter wheat’s flowering stage (