A quantitative evaluation system of soil productivity for intensive agriculture in China

Geoderma 123 (2004) 319 – 331 www.elsevier.com/locate/geoderma

A quantitative evaluation system of soil productivity for intensive agriculture in China B. Zhang a,b,1, Y. Zhang a,1, D. Chen b,2, R.E. White b,*, Y. Li b,2 b

a Yangzhou Soil and Fertilizer Station, Yangzhou 225002, PR China School of Resource Management, Institute of Land and Food Resources, The University of Melbourne, Victoria 3010, Australia

Received 12 June 2003; received in revised form 12 January 2004; accepted 16 February 2004 Available online 24 March 2004

Abstract A system for the quantitative evaluation of soil productivity was developed and deployed in Gaoyou County, China. The study area, comprising 81,600 ha of cultivated land, was divided into 7367 evaluation units, and 19 soil properties were selected as factors for evaluation. Fuzzy analysis and expert score ranking combined with the Delphi method were used to quantify the membership functions of the evaluation factors selected. The weight contributions of individual factors to soil productivity were determined using the Delphi method and an analytic hierarchy process (AHP). A geographic information system (GIS) was used to manipulate the spatial database of the study area. This evaluation system, which differentiates between the concepts of land productivity and soil productivity, has several advantages compared with the China Agriculture Ministry Land Evaluation System (CAMLES), and can deliver detailed soil information to help decision makers and farmers identify the optimal agricultural management practices for achieving higher soil productivity and sustainable soil use. The proposed system has been accepted as the standard method for evaluating soil productivity in China. D 2004 Elsevier B.V. All rights reserved. Keywords: Soil productivity; Land productivity; Fuzzy analysis; Analytic hierarchy process; Delphi method; GIS

1. Introduction Land evaluation is an integrated process for evaluating potential land productivity and land suitability for varied purposes. Many systems of land evaluation have been developed since the USDA Soil Conservation Service released its land capability classification

* Corresponding author. Fax: +61-3-83444665. E-mail addresses: [email protected] (Y. Zhang), [email protected] (D. Chen), [email protected] (R.E. White), [email protected] (Y. Li). 1 Fax: +86-514-7346579. 2 Fax: +61-3-83444665. 0016-7061/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2004.02.015

system in 1961 (Klingebiel and Montgomery, 1961). Following the publication of the FAO Framework for Land Evaluation (FAO, 1976), many countries started to apply this system or developed their own, according to the theory and methodology of the FAO system (Koreleski, 1986; Dumanski and Onofrei, 1989; Bdliya, 1991; Shields et al., 1996; Voltr, 1998; Ano et al., 1999). In 1996, the China Agriculture Ministry released its first classification system for cultivated land in China (China Agriculture Ministry, 1996). Land productivity, usually represented by crop yield or animal product per ha, depends on soil productivity, climate and agricultural management practices (FAO, 1985). Although soil productivity

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may not change in an individual evaluation unit through time, climate and agricultural management practices always change with time. The management of an evaluation unit may be altered by a landholder because of an improvement in his knowledge and education, or his financial resources; it may also be altered by a change in government policy. Therefore, in a given area, soil productivity, which is a function of inherent factors such as parent material, topography, soil physical and chemical properties, and the infrastructure for irrigation and drainage, is relatively more stable than land productivity. Within the photosynthetic capacity limits set by climate, soil productivity can represent the potential productivity of land. A good understanding of soil productivity can therefore assist decision makers and farmers to apply more rational agricultural management to achieve higher land productivity and maximise land use. In the evaluation of soil productivity at a given spatial scale, there are several general principles for choosing evaluation factors (Pieri et al., 1995; Zhu et al., 1996). First, the chosen factors must have a significant effect on soil productivity, which normally can be identified from the relationships between these factors and crop yield. Second, the value of a chosen factor should have a considerable range among soil types and for different land uses. Third, the stability of a factor for any one soil type or kind of land use is important. For example, topography and parent material are considered the most stable evaluation factors; soil depth, texture, and soil horizon composition are also stable, but soil nutrients and salt content are not stable. However, in some cases of a small to medium-scale evaluation or purpose-specified evaluation, consideration of low stability factors may be necessary. For example, the content of available silicon in the soil is essential for determining its suitability for rice growth. Lastly, the chosen factors should fully meet the evaluation objectives, and the classification of factors needs to be quantitative and standardized. The evaluation method, a core issue in soil productivity evaluation, can be either a direct approach using field experiments to measure crop yield, or an indirect approach based on an integrated assessment of evaluation factors. The latter approach is widely used because of its advantages in identifying the systematic complexity of soil productivity under

natural conditions, through the use of fuzzy mathematical methods to evaluate relationships between certain soil factors and land productivity (Burrough, 1989; Fu, 1991; Tang et al., 1991; Sun et al., 1995; Dobermann and Oberthur, 1997; McBratney and Odeh, 1997). However, the effects of some factors are not easily demonstrated by numeric equations, so that expert judgement is required, and the Delphi method is often used to produce a reliable measurement of judgements by a group of experts (Richey et al., 1985; Kangas et al., 1998; Marggraf, 2003). The analytic hierarchy process (AHP) is a powerful decision-making process enabling priorities to be set, and both qualitative and quantitative criteria to be used to make the best decisions for complex, multifactor systems (Saaty, 1980). AHP has proved to be very useful in land evaluation (Fu, 1991; Ananda and Herath, 2003). The objective of this study was to develop a new quantitative method, within the framework of a GIS, which combined fuzzy analysis, the Delphi method of expert ranking, and AHP to evaluate soil productivity, using natural soil properties and information on irrigation and drainage infrastructure. The method was developed for intensive agriculture and applied in Gaoyou County, China, at a regional scale. The evaluation result was compared with the China Agriculture Ministry Land Evaluation System (CAMLES) to demonstrate its advantages in evaluating soil productivity.

2. Material and methods 2.1. Site and survey Gaoyou County is located in the northeastern part of Jiangsu Province, China. It has a moderately cool subtropical climate and four significant seasons. Annual rainfall is about 1000 mm and mostly concentrated in summer. The county has a population of 831,500, with total area of 1963 km2 in which cultivated land, under a predominantly rice –wheat rotation, occupies about 816 km2. In 1996, a soil survey was carried out in grid format with a total of 1131 sampling sites, and the results mapped at a scale of 1:50,000. Soil samples (0 – 20 cm depth) were collected at each site for analysis of chemical properties [pH, cation exchange capacity (CEC), soil organic matter, Olsen P, ex-

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changeable K, available zinc, boron and silicon], and measurements made of physical attributes (topography, water table depth, soil layer depth and depth of cultivation). Data for parent material, soil texture, bulk density, and soil horizon composition were extracted from the 2nd National Soil Survey (Gaoyou Soil Survey Office, 1982). Data on irrigation and drainage fluxes and information on the field water infrastructure supporting irrigation and drainage were provided by the local water conservation bureau (Gaoyou Soil Water Conservation Bureau, 1998). In addition, 26 sites representative of the main soil types in the county were identified for comprehensive soil analysis and crop yield monitoring. From these sites, 15 (three sites from each of the five main soil types) were selected for detailed soil physical and chemical analysis of the soil horizons to 1 m depth in 1996. From 1997 to 1998, farmers at these sites were provided with the same wheat and rice varieties, but with no prescription for fertilizer use. A 66.7 m2 control plot (without fertilizer input) was set up at each monitoring site and isolated using plastic sheeting, extending 60 cm into the soil and 20 cm above the soil surface. Grain yields from the control plots were collected and used to define the natural productivity of the soil, for comparison with grain yields recorded under normal management (with fertilizer).

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each of the evaluation factors, following the methods suggested by Burrough (1989), Fu (1991), Sun et al. (1995), McBratney and Odeh (1997) and Tang (1997). The factors were processed using the following five types of fuzzy membership functions: an upper limit function, lower limit function, peak limit function, linear function and descriptive function, in terms of their relationship with soil productivity. 1. An upper limit function was chosen for factors A2, A3, A4, A9, A10, A12, A13, A14, A15 and A16, as follows 8 0; ui Vuli > > > > < yi ¼ 1=ð1 þ ai ðui  uoi Þ2 Þ; uli < ui > > > : 1; ui zuoi

ð1Þ where yi is the value of the membership function, ai is a constant, ui is the measured value of the evaluation factor, uoi is the optimum (or upper) value of the evaluation factor, uli is the lower limit value of the evaluation factor, and the subscript i denotes the ith value of the factor in the range from 1 to m. 2. A lower limit function was chosen for A8 according to

2.2. Evaluation units and evaluation factors By overlaying maps of soil type, land use and local administrative boundaries at a scale of 1:50,000, a total of 7367 evaluation units were generated. Using principal component analysis of 25 soil factors considered to be relevant to soil productivity in the region of the 26 monitoring sites, 19 factors (A1 – A19) were selected as the evaluation factors, according to the orthogonal factorial score values (Fig. 1). The spatial database of soil factors was managed by ARC/INFO (Environmental Systems Research Institute, 1995). 2.3. Parameters of the membership functions of evaluation factors Fuzzy analysis and the Delphi method were used to calculate the membership functions for

8 0; ui zuli > > > > < 2 yi ¼ 1=ð1 þ ai ðui  uoi Þ Þ; uoi < ui < uli ; ði ¼ 1; 2; : : : : : : ; mÞ > > > > : 1; ui Vuoi

ð2Þ

where the terms are as defined for Eq. (1). 3. A peak function was chosen for A11 according to 8 0; ui Vul1 or ui zul2 > > > > < 2 yi ¼ 1=ð1 þ ai ðui  uoi Þ Þ; ul1 < ui < ul2 ; ði ¼ 1; 2; : : : : : : ; mÞ > > > > : 1; ui ¼ uoi

ð3Þ

where ul1 and ul2 are the lower and upper limits for ui .

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Fig. 1. Hierarchical structure for the soil productivity evaluation.

4. A linear function was chosen for A18 and A19 according to 8 0; ui Vuli > > > > < yi ¼ ai ui uli < ui < uoi ; ði ¼ 1; 2; : : :: : : ; mÞ > > > > : 1; ui zuoi ð4Þ

The parameter values for the above four membership functions for the 14 quantitative factors are listed in Table 1. 5. An expert score ranking was used for the five descriptive factors A1 (soil horizon composition), A5 (landform), A6 (parent material), A7 (texture) and A17 (field infrastructure). Field infrastructure refers to the infrastructure in place to provide irrigation and drainage.

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Table 1 Membership functions and parameters for quantitative soil factors Function type

Factor

Parameters of membership functions ai

Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Upper limited Lower limited Lower limited Peak Linear Linear a

A2—soil depth (cm) A3—cultivated layer depth (cm) A4—water table depth (cm) A9—soil organic matter of sandy soil (g kg1) A9—soil organic matter of clay soil (g kg1) A10—CEC of sandy soil (cmolc kg1) A10—CEC of clay soil (cmolc kg1) A12—exchangeable potassium of sandy soil (mg kg1) A12—exchangeable potassium of clay soil (mg kg1) A13—Olsen phosphorus of sandy soil (mg kg1) A13—Olsen phosphorus of clay soil (mg kg1) A14—available zinc (mg kg1) A15—available boron (mg kg1) A16—effective silicon (mg kg1) A8—bulk density of sandy soil (g cm3) A8—bulk density of clay soil (g cm3) A11—pH A18—drainage modulus (m3 s1 hm2) A19—irrigation modulus (m3 s1 hm2)

2.4. Calculation of the contribution of evaluation factors to soil productivity The weight contribution of each factor to soil productivity was determined using AHP and the Table 2 Membership values for the main soil profiles Membership value

Soil horizon composition in the main soil profiles

1.0

A – P – W – B; A – B1 – B2; A1 – A2 – B (no obstacle layer) A – P – Wg – Bg (obstacle layer below 60 cm) A – P – Wg – G; A – P – W – E; A – P – W – Bca (obstacle layer below 40 cm) A – P – E – B; A – P – Wca – Bca; A – P – Dm (obstacle layer below 20 cm) Pca – Bca; A – Pg – G (obstacle layer above 20 cm) A – G; A – C (undeveloped soil profile)

0.4 0.2 0.1

5.39  10 7.80  103 7.96  104 1.04 1.04 8.05  103 1.26  103 5.87  104 5.29  104 4.14  102 4.14  102 4.14  104 21.56 4.14  104 16.57 16.57 1.04 0.01 0.10

uoi

uli

100 20 80 20 26 25 18 100 120 10 12 1.0 0.5 100 1.2 1.1 6.8 0.01 0.15

10 5 10 5 10 2 5 10 20 2 5 0.20 0.05 20 1.6 1.8 2.0, 12.0a 0.001 0.02

Value of ul2.

The membership functions for the descriptive factors were based on expert ranking combined with the Delphi method. Table 2 shows the membership functions for the main soil horizon compositions found in the study area.

0.8 0.6

4

Old organic matter accumulation horizon (Dm).

Delphi method (Zhao et al., 1986; Banai, 1993; Gao, 1993; Schmoldt and Peterson, 1997). A hierarchical structure was constructed, comprising an objective hierarchy G (soil productivity) at the top, a middle hierarchy C, and lower hierarchy A of soil factors. This system clustered the 19 evaluation factors from hierarchy A into five groups in hierarchy C (C1, C2, C3, C4 and C5), according to the natural grouping of the A factors (Fig. 1). Expert score ranking was used to assess the relative importance or contribution of hierarchy C to soil productivity (hierarchy G), as well as of hierarchy A to hierarchy C. The judgement matrices and the consistency ratio outcomes are shown in Table 3. The eigenvectors (CSPj) of the G judgement matrix were the weights of groups C1 to C5 for soil productivity. The eigenvectors (ACi) of the C judgement matrix (Cj) were the weights of Ai to Cj. Finally, the combined weights ASPi, which were the direct individual contributions of Ai to the final objective (soil productivity), were calculated using Eq. (5). ASPi ¼ ACi  CSPj

ð5Þ

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Table 3 Judgement matrices and consistency ratio outcomes

2.5. Calculation of the soil productivity index (SPI)

Judgement matrix of G C1 C2 C3 C4

C5

The soil productivity index (SPI) was calculated by Eq. (6), i.e.

C1 C2 C3 C4

1 1/7 1/3 1/5

7 1 7/3 7/5

3 3/7 1 3/5

5 5/7 5/3 1

3 3/7 1 3/5

C5

1/3

7/3

1

5/3

1

Outcome: eigenvectors: [0.50, 0.07, 0.17, 0.10, 0.17] maximum eigenvalue: 5.00 CIC – G = 7.24  106 RIC – G = 1.12 CRC – G = CIC – G/RIC – G = 0.00000646 < 0.1 satisfactory consistency

SPI ¼

19 X ðyi  ASPi Þ

ð6Þ

i¼1

2.6. Consistency test for single hierarchy sorting Judgement matrix of C1 A1 A2 A3 A4 A1 A2 A3 A4

1 1/5 1/3 1/7

Judgement A5 A5 1 A6 1/3

5 1 5/3 5/7

3 3/5 1 3/7

matrix of C2 A6 3 1

7 7/5 7/3 1

Outcome: eigenvectors: [0.60, 0.12, 0.20, 0.09] maximum eigenvalue: 4.00 CIA – C = 1.94  105 RIA – C = 0.9 CRA – C = CIA – C/RIA – C = 0.0000215 < 0.1 satisfactory consistency Outcome: eigenvector: [0.75, 0.25] maximum eigenvalue: 2.00 CIA – C =  5.00  105 RIA – C = 0

Judgement matrix of C3 Outcome: A7 A8 A9 A10 A11 eigenvector: [0.53, 0.11, 0.18, 0.11, 0.08] A7 1 5 3 5 7 maximum eigenvalue: 5.00 A8 1/5 1 3/5 1 7/5 CIA – C = 1.06  105 A9 1/5 5/3 1 5/3 7/3 RIA – C = 1.12 A10 1/5 1 3/5 1 7/5 CRA – C = CIA – C/RIA – C = 0.00000948 < 0.1 A11 1/7 5/7 3/7 5/7 1 satisfactory consistency Judgement matrix of C4 Outcome: A12 A13 A14 A15 A16 eigenvector: [0.53,0.18,0.11,0.11,0.08] A12 1 3 5 5 7 maximum eigenvalue: 5.00 A13 1/3 1 5/3 5/3 7/3 CIA – C = 1.06  105 A14 1/5 3/5 1 1 7/5 RIA – C = 1.12 A15 1/5 3/5 1 1 7/5 CRA – C = CIA – C/RIA – C = 0.00000948 < 0.1 A16 1/7 3/7 5/7 5/7 1 satisfactory consistency Judgement matrix of C5 A17 A18 A19 A17 1 A18 1/3 A19 1/3

3 1 1

3 1 1

Outcome: eigenvector: [0.60, 0.20, 0.20] maximum eigenvalue: 3.00 CIA – C =  3.33  105 RIA – C = 0.58 CRA – C = CIA – C/RIA – C = 0.0000575 < 0.1 satisfactory consistency

The consistency index (CI) of a judgement matrix for single hierarchy sorting, such as for A factors in the C hierarchy, was calculated by Eq. (7) CI ¼ ðkmax  nÞ=ðn  1Þ

ð7Þ

where kmax is the maximum eigenvalue, and n is the number of factors in the judgement matrix. The consistency ratio (CR) of such a judgement matrix was calculated by Eq. (8) CR ¼ CI=RI

ð8Þ

where RI is the average random consistency index (Saaty, 1980).

Table 4 Classification criteria of the soil productivity index (SPI) developed for Gaoyou County Grade

SPI

Description

1

z 0.91

2

0.81 – 0.91

3

0.71 – 0.80

4

0.61 – 0.70

5

0.51 – 0.60

6 7 8 9 10

0.41 – 0.50 0.31 – 0.40 0.21 – 0.30 0.11 – 0.20 V 0.10

High soil productivity (no limitation) Medium – high soil productivity (few limitations) Medium soil productivity (low limitations) Low – medium soil productivity (some limitations) Low soil productivity (high limitations) Not suitable for crop growth Not suitable for crop growth Not suitable for crop growth Not suitable for crop growth Not suitable for crop growth

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2.7. Consistency test for general hierarchy sorting

2.8. Presentation of evaluation results

The equations for general hierarchy sorting, such as for the A factors in the G hierarchy, were as follows. 5 X CIAG ¼ Cj  CICG ð9Þ

The SPI was calculated for each evaluation unit and the corresponding soil productivity grade for each unit was determined according to the classification criteria in Table 4. The spatially distributed results are presented in GIS format in Fig 2. Village-based average crop yields for 1998 were obtained from the statistical agency of the local government (Gaoyou Statistical Bureau, 1998) and used to grade the agricultural land according to CAMLES. The performance of CAMLES and the proposed SPI system for evaluating the natural productivity of land was assessed by regression analysis. In addition, at the 15 monitoring sites, crop yields

j¼1

RIAG ¼

5 X

Cj  RICG

ð10Þ

j¼1

CRAG ¼

CIAG RIAG

ð11Þ

where the subscript A –G refers to the general sorting of C against G, subscript C – G refers to the sorting of A against G, and j is the number of C groups.

Fig. 2. Classification map of soil productivity for Gaoyou County based on the proposed system.

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with and without fertilizer, and fertilizer costs per kg of grain produced, were recorded by the landowners who were also responsible for maintaining and monitoring the sites. The major soil limitations at each site were derived from the database.

3. Results and discussion 3.1. Weight contribution of evaluation factors As shown in Table 5, the C1 group contributed nearly 50% to soil productivity. The C3 and C5 groups were equal second contributors at about 16%. With respect to weight contributions in the individual C groups, soil horizon composition (A1) accounted for almost 60% of the weight contribution among the four factors in the C1 group, and consequently contributed almost 30% of combined weight to soil productivity. The substantial contribution of soil horizon composition reflects the fact that there are several types of obstacle layers in Gaoyou County soils, which have a strong effect on crop growth. Most experts and experienced farmers believe that soil

horizon composition is a very important factor in the county. Cultivated layer depth (A3) is the second most important factor in the C1 group, because crop roots mostly grow within the cultivated layer in a rice – wheat rotation cropping system. The weight contribution of A3 to the C1 group and to total soil productivity was 19.9% and 9.9%, respectively. In the C3 group, soil texture (A7) made the largest weight contribution, but its combined weight contribution to soil productivity was only 8.8%, because of the relatively low weight contribution of the C3 group to soil productivity (16.7%). It is well known that soil texture affects many other soil physical and chemical properties and also affects the availability of some soil nutrients for crop growth. Similar to soil texture in the C3 group, field infrastructure (A17) has the largest weight contribution (60%) to the C5 group. Its combined weight contribution to soil productivity was 10%, second only to soil horizon composition (A1) at 29.7%. Field infrastructure, referring to the irrigation and drainage facilities present, is an important factor contributing to soil productivity because Gaoyou County is a water-

Table 5 Contribution weight of soil factors to soil productivity calculated by the AHP Hierarchy G Hierarchy A

Hierarchy C C1 0.4976

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 Total

C2 0.0711

C3 0.1659

C4 0.0995

C5 0.1659

Combined weight ACi  CSPj = ASPi

0.6 0.2 0.2 1.0000

0.2968 0.0593 0.0989 0.0424 0.0533 0.0177 0.0884 0.0176 0.0294 0.0176 0.0126 0.0530 0.0176 0.0106 0.0106 0.0075 0.0995 0.0331 0.0331 1.0000

0.59659 0.11932 0.19886 0.08523 0.75 0.25 0.53299 0.10660 0.17766 0.10660 0.07615 0.53299 0.17766 0.10660 0.10660 0.07616

1.0000

1.0000

1.0000

1.0000

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networked and intensively irrigated area. The county is subject to flooding in the summer, and therefore requires an effective drainage infrastructure for good crop production. Nevertheless, due to low rainfall in winter and spring, irrigation is critical for high wheat yields. However, the irrigation and drainage modulus values were judged to be relatively unimportant contributors to the SPI (combined weights of 3.3% each). It is interesting that the C4 group (soil available nutrients) was given very little influence on soil productivity. The soil nutrients, from exchangeable potassium (A12) to available silicon (A16), are no longer regarded as limiting factors for rice and wheat growth because they can be easily compensated by fertilizer applications, according to the experience of experts chosen in this project. Nitrogen supply was not considered in this evaluation system for three reasons. First, soil organic matter (A9) can reflect the content of total soil nitrogen. Second, the content of available nitrogen in soil changes with time, which makes it difficult to set up critical values for assessment. Third, nitrogen for crop growth is mainly supplied from fertilizers under intensive agriculture in Gaoyou County. Overall, regarding the combined weight contribution of hierarchy A to soil productivity, soil horizon composition was the most important factor, the field irrigation drainage infrastructure the second, cultivated layer depth the third, and soil texture the fourth. 3.2. Spatial distribution of SPI The spatial distribution of the SPI is displayed in Fig. 2. Generally, Gaoyou County can be divided into the following four regions according to the SPI: (1) The hilly, low soil productivity region, located to the west of Gaoyou Lake. The major problem in this region is lack of organic matter and nutrients in soils. In addition, cultivated soil layer depths are very shallow, and the appearance of a bleached layer in the soil profile is very common. (2) The sandy, low soil productivity region, located in the southeastern part of the county. Soil productivity is significantly limited by a compact sand layer occurring in the soil profile. (3) The extensive, high soil productivity region along the eastern part of Gaoyou Lake, beside the

327

Yunhe River and from the northern to southern border. (4) The lowland, medium –high soil productivity region, located in the centre part of the county. Although the soils have very high organic matter and nutrient contents, the SPI of some soils in this lowland area is less than 0.5, and classified as ‘not suitable for the crop growth’ because of poor field drainage caused by a shallow water table and frequent flooding associated with the low elevation. A summary of the results of the proposed evaluation system is given in Table 6. The soil productivity of most of the county is higher than grade 5. Grade 3 (the medium level) is the largest group in the county, accounting for 42% of the total cultivated land. The second grade (medium – high level) accounts for 31% of the area. In general, the status of soil productivity in Gaoyou County is medium, but tending towards the higher grade. 3.3. Comparison of the SPI evaluation system with CAMLES Table 6 shows the evaluation results for the proposed SPI system compared with a traditional land productivity evaluation system based on crop yield (CAMLES). The CAMLES system divides land productivity into 10 grades from 1500 to 15,000 kg ha1 year1 with 1500-kg increments. The proposed system divides soil productivity into 10 grades from 0.1 to 1.0, with 0.1 increments of the SPI. However, there are some substantial differences between these two evaluation results. According to CAMLES, 19.7% of the land is in grade 2, 62.2% in grade 3 and only 0.3% in grade 5, whereas the percentages of the area in the corresponding grades of soil productivity classified by the proposed system are 31.0%, 42.3% and 11.5%, respectively. The performance of the two evaluation systems in assessing the natural land productivity was compared using yield data from the 15 monitoring sites, selected from the 26 sites (Table 7). The crop yield without fertilizer was regressed against the soil productivity index of proposed system and the land productivity grade of CAMLES. The former regression accounted

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Table 6 Land productivity grades based on CAMLES compared with soil productivity grades based on the proposed system CAMLES

Proposed system

Land grade

Yielda (kg ha1 year1)

No. of villages

1 2 3 4 5 6 7 8 9 10 Total

>15,000 13,500 – 15,000 12,000 – 13,500 10,500 – 12,000 9000 – 10,500 < 9000

14 118 373 92 2 1

a

Area (%) 2.3 19.7 62.2 15.3 0.3 0.2

600

Soil grade

SPI

No. of evaluation units

1 2 3 4 5 6 7 8 9 10

z 0.91 0.81 – 0.90 0.71 – 0.80 0.61 – 0.70 0.51 – 0.60 < 0.50

201 2283 3116 868 845 54

100

Area (%) 2.7 31.0 42.3 11.8 11.5 0.7

7367

100

Village-based average crop yield (with fertilizer) in 1998.

for 88% of the variation in yield (significant at the 1% probability level), whereas the latter accounted for only 33% of the variation (significant at the 5% level). This result suggested that CAMLES did not reflect the natural productivity of the land, because yield in an intensive agriculture system such as Gaoyou County can be strongly influenced by fertilizer use, other management practices and climate variations, which are taken into account in CAMLES. It is well known that fertilization is probably the most important management practice influencing crop yield. For example, at sites of 10, 13 and 14 where the SPI values were

low, relatively high yields were still achieved. However, these high yields required a considerable increase in fertilizer input. The fertilizer costs at sites of 10, 13 and 14, were 0.26, 0.25 and 0.23 Yuan per kg grain, respectively, which were higher than the average value of 0.20 (Table 8). 3.4. Advantages and limitations of the new evaluation system The proposed system identifies specific soil limitations, and therefore can be used to assist decision

Table 7 Selected soil factors of selected monitoring sites Site number

Topography

Soil horizon composition

Depth of cultivated layer (cm)

Depth to water table (cm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

High plain High plain Plain Low High plain High plain Low-lying Low-lying Plain Plain Plain Plain Hill Slope Hill

A – P – Wg – C A–P–W–B A – P – Wg – Bg – C A – P – Wg – Bg – G A–P–W–B–C A – P – Bg – G A – P – Wg – Bg – G A – P – Wg – Bg – C A – P – W – Bca – C A – P – Wca – Bca – C A – P – Wca – Bca A – P – Wca – Bca – C A–P–E–B–C A–P–E–B–C A–P–E–B–C

15 17 16 16 15 15 15 13 15 18 15 13 14 14 14

>100 >100 70 70 80 >100 80 < 60 75 80 80 80 >100 >100 >100

Depth to obstacle layer (cm)

Organic matter (g kg1)

Bulk density (g m3)

>40 20 – 40 45 – 60 20 – 35 35 – 53 24 – 53 27 – 42

24.3 25.5 27.0 30.3 27.2 12.8 36.2 29.5 17.2 16.2 20.0 14.4 11.7 18.2 13.2

1.29 1.37 1.06 1.30 1.29 1.32 1.16 1.20 1.35 1.22 1.35 1.22 1.29 1.29 1.27

Cultivated horizon (A); compacted horizon under plough layer (P); deposit horizon (B); deposit waterlogging (Bg); deposit horizon by leaching (W); waterlogged (G); bleached horizon (E); parent horizon (C); intermittent waterlogging (Wg); calcium carbonate accumulation horizon (Bca).

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Table 8 Soil productivity index, crop yield (without and with fertilizer), fertilizer cost and major limitations of the soils at the selected 15 monitoring sites Site

SPI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.92 0.88 0.83 0.82 0.82 0.81 0.81 0.81 0.71 0.69 0.68 0.63 0.61 0.58 0.55 a

Crop yield (kg ha1 year1)a  Fertilizer

+ Fertilizer

Fertilizer cost (Yuan per kg grain)

10,440 9232 8151 8437 6966 8967 8398 8613 6859 5211 6449 5903 5118 4183 5130

14,535 13,470 11,895 12,825 15,480 15,270 12,855 13,575 10,650 11,250 11,145 9525 12,150 11,670 10,755

0.14 0.13 0.15 0.16 0.20 0.19 0.14 0.19 0.22 0.25 0.22 0.20 0.27 0.25 0.23

Major soil limitations A13 = 7 mg P kg1 A13 = 4 mg P kg1; A15 = 0.16 mg B kg1; A11 = 8.2 A2 = 60 cm; A4 = 70 cm; A18 = 0.5 m3 s1 km2 A4 = 70 cm; A19 = 0.012 m3 s1 km2; A8 = 1.32 g cm3 A2 = 70 cm; A4 = 80 cm; A13 = 5 mg P kg1 A18 = 0.7 m3 s1 km2; A8 = 1.32 g cm3 A4 = 80 cm; A18 = 0.6 m3 s1 km2; A7 = medium clay A4 < 60 cm; A7 = medium clay; A13 = 4 mg P kg1 A1 = CaCO3 layer 48 cm below A1 = CaCO3 layer 20 – 40 cm; A13 = 1 mg kg1 A1 = CaCO3 layer 45 – 60 cm; A2 = 60 cm A1 = CaCO3 layer 20 – 35 cm; A2 = 62 cm A5 = sloping field; A1 = bleached layer 35 – 53 cm A5 = sloping field; A1 = bleached layer 24 – 50 cm A5 = hillock field; A1 = bleached layer 27 – 42 cm

The crop yield and fertilizer cost were recorded from 1997 to 1998.

makers and farmers to find appropriate measures to ameliorate soil problems. For example, Gaoyou County is a water-abundant area, with more than half the area covered by surface water (streams, ponds and lakes). Waterlogging is one of the major causes of low crop productivity such as at monitoring sites 3, 4, 7 and 8 (Table 8). This limitation could be mitigated by a new drainage system. For the area where obstacle layers in the soil profile are identified as the main cause of low productivity, such as at monitoring sites 9, 10, 11, 12, 13, 14 and 15, alternative land use should be considered because these constraints are not easily ameliorated. In the case of the calcium carbonate layer present at sites 9, 10, 11 and 12, farmers can change from a rice –wheat rotation to a rape –cotton rotation, because rape plants have a deep and strong root system and should improve soil structure gradually. The soils with a bleached layer in the soil profile and located in sloping areas (sites 13, 14, and 15) are not suited for rice and wheat, but suitable for tea plantations, fruit trees or other kinds of cash crops. Compared with CAMLES, the proposed system has other advantages: (a) The proposed system is based on actual management land units (about 11 ha), normally

owned by 30 – 50 farmers, rather than the village units (about 136 ha), owned by 350 – 600 farmers, used by CAMLES. The smaller the area, the more likely the farmers are to be applying the same agricultural management practices. Therefore, the proposed system can provide more precise recommendations for farmers. (b) The system is GIS based, which provides a mechanism for further monitoring the effects of the implementation of recommended practices. The capacity for further monitoring and fine tuning is an important function of a good evaluation system. (c) The system can serve as a platform for planning other agricultural research, such as water and nutrient management modelling, by providing essential spatial information of soil and land. (d) The system can be used as the basis for other kinds of land evaluation (e.g., soil suitability evaluation, economic evaluation, etc.). It should be pointed out that this system only provides a protocol for the evaluation of soil productivity, depending on the local climate, social and economic conditions. The selection of factors and

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their critical limits has to be adapted to the local circumstances when the method is used in other areas. For example, soil salinity is an important factor in coastal regions, but not necessarily the presence of obstacle layers.

4. Conclusions A combined membership function-AHP method based on 19 soil and local infrastructure properties (a soil productivity index SPI) was quantitatively developed and used to evaluate soil productivity in Gaoyou County. The results indicated that the most important factor affecting soil productivity was soil horizon composition, followed by drainage and irrigation infrastructure, cultivated layer depth, and soil texture. The overall status of soil productivity in Gaoyou County based on this evaluation system was in the medium (grade 3) and medium –high grade (grade 4). Compared with the land productivity evaluation system of the China Agriculture Ministry (CAMLES), the SPI system provided a realistic evaluation of soil productivity, independent of agricultural practice and climate. The system can help decision makers and farmers to have a better understanding of the productivity of the land they are managing, without interference from intensive inputs of fertilizers and other human activities, thereby achieving better precision, higher productivity and sustainability simultaneously. In addition, this evaluation system, which is GIS-based, permits ongoing monitoring and fine tuning of an agricultural system, and can serve as a platform for other agricultural research needs.

Acknowledgements The research was funded by the China Agriculture Ministry, Jiangsu Science and Technology Commission, China National Scholarship Council and The University of Melbourne, Australia. Support from the Australian Centre for International Agricultural Research Project LWR1/96/164 is also acknowledged. The technical assistance of B. Liu, L. Liu, M. Xu and X. Wang is gratefully acknowledged.

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