Elsevier

Geoderma

Volume 123, Issues 3–4, December 2004, Pages 319-331
Geoderma

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

https://doi.org/10.1016/j.geoderma.2004.02.015Get rights and content

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.

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 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 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.

Section snippets

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

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

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

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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