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Yang J.,China Agricultural University | Yang J.,Key Laboratory for Agricultural Land Quality | Yue Y.,China Agricultural University | Yue Y.,Key Laboratory for Agricultural Land Quality | And 5 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

M Monitoring points in country area are the foundation to reflect changes of cultivated land quality, which directly affect the result of farmland grading and its accuracy. Through the monitoring network for cultivated land quality in county area, the distribution and changing trend of the cultivated land quality can be reflected. Besides, the quality of non-sampled locations should also be estimated with the data of sampling points. Due to the correlation among spatial samples, the traditional methods such as simple random sampling, stratified sampling and systematic sampling are inefficient to accomplish the task above. Thus, we propose a new spatial sampling and optimizing method based on the spatial simulated annealing (SSA). This paper presents a pre-processing method to determine the number of sampling points, including preprocessing the data of cultivated land quality before sampling, exploring the spatial correlation and spatial distribution pattern of cultivated land quality, and computing the appropriate quantity of sampling points by analyzing the change trend of sampling number and sampling precision, and on this basis we propose the extended spatial simulated annealing method to optimize spatial sampling design for obtaining the minimal Kriging variance. The main steps for computing the optimal sampling design can now be summarized as follows: 1) calculate the semi-variogram of cultivated land quality and determine the parameters of ordinary Kriging interpolation; 2) identify the quantity of samples, choose a set of cultivated land map spots randomly as an initial design, and compute the associated fitness function; 3) given one design, construct a candidate new sampling design by random perturbation; 4) compute the fitness function for the new design, and if it is smaller than or equal to that for the original design, accept the original design, or else accept the new design with an acceptance probability. If the new design is accepted, the estimated point (j) is returned to zero, or else increased by 1; 5) if j is smaller than or equal to a threshold value of continuous rejections, increase i (representing monitoring point) by 1, or else stop the iteration and current design is the best. Designs by simulated annealing that reduce the average Kriging standard error are always accepted, and designs that worsen the interpolation effect are accepted with a certain probability, which decreases to zero as iterations proceed. However, there are integrated factors such as soil organic matter content, topsoil texture, profile pattern, salinization which affect arable land quality change over time and space, and are taken as potential change factors to detect potential change areas. Under the guidance of expert knowledge, the sampling points are set up through spatial simulated annealing algorithm and adjusted based on potential change areas, rivers, roads and abnormal monitoring points. We illustrate this new method using Daxing District, Beijing City as a case study. Spatial overlay analysis of potential change factors and geostatistics method of GIS are employed to test this method. The spatial variability of cultivated land quality is simulated using natural quality indices and a specified number of network locations is defined which can be used to adequately predict the quality of cultivated land. The experimental results of Daxing District, Beijing City show that 55 monitoring reference sample units are finally deployed, and the average ordinary Kriging standard error with this method is 131.78, which is smaller than the simple random sampling (134.97) and stratified sampling (134.93) when the quantity of samples is the same. Besides, sampling accuracy and cost are both considered and reach a certain balance in this method. This method is suited for counties which have carried out several surveys of cultivated land quality, or counties whose grading factors have certain changes. Besides, it is also suitable for counties which have some prior knowledge but never have conducted a survey of cultivated land quality. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved. Source

Lu X.,Qufu Normal University | Lu X.,Nanjing University | Huang X.,Nanjing University | Zhong T.,Nanjing University | And 3 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

The purpose of this study aimed to discuss the process of construction land expansion and its relationship with economic growth. Firstly, the definitions of temporal equilibrium of construction land expansion were discussed in this research, and an integration model was built up based on coefficient of variation and decoupling mode based on IPAT. The temporal equilibrium index was calculated by this model, and it reflected the temporal equilibrium posture of construction land expansion. Furthermore, the empirical analysis took Tongzhou in Jiangsu Province for an example, and its process of construction land expansion during 1986-2008 was analyzed. The following conclusions were drawn: 1)According to the evaluation results of the coordination between construction land expansion and economic growth in the view of decoupling analysis, at 1year scale in Tongzhou, the relationship between construction land expansion and economic development was in a weak decoupling state, and there were large changes of coordination index C, proving a poor coordination. 2) Using the co-integration analysis and VAR models, we measured the interaction response cycle of economic growth and construction land expansion. Results showed that the response period was 4 years. Thus, the time scale was relaxed to 4 years. Then the relationship between construction land expansion and economic growth remained in a weak decoupling state during the 7 time phase (1986-1990, 1990-1993, 1993-1996, 1996-1999, 1999-2002, 2002-2005, 2005-2008) and the coordination index C and C' both increased firstly and then decreased, indicating that there was a growing gap between the actual rate and ideal rate of the construction land expansion. 3) The temporal equilibrium index (E) of construction land expansion referred to C and its coefficient of variation (CV). CV of construction land's annual average growth rate was higher, and showed a tendency of increased volatility in Tongzhou. Combining the above analysis and calculation results of E, the investigation shows that E of construction land expansion in Tongzhou firstly appeared to decline rapidly, and then increased slightly within a small margin, holistically indicating that its temporal equilibrium of construction land expansion was not optimistic. Many factors would affect the change of E. We concluded there was close contact between the changing trend of E and both economic growth characteristics and relevant national land use policy's implement. Finally, this paper has found that the model can depict the changes of coupling process between construction land expansion and economic growth, which provides scientific new ideas for understanding the process of regional construction land expansion. Source

Li J.,China Agricultural University | Zhang J.,China Agricultural University | Li X.,China Institution of Land Surveying and Planning | Su D.,China Institution of Land Surveying and Planning
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Basic farmland area determined by overall land use planning in China, is regarded as important measures to guarantee the grain quantity and food security. The quantity of basic farmland is strictly restrained by the government, but the quality relate to the location is not well supervised. Thus, with land reclamation planning and high-standard basic farmland project, it is of importance to evaluate the spacial allocation of basic farmland for raising the quality of basic farmland. We pick Yinchuan as study area, which is a rapid development city in the west of China. There are many contradictions between economic development and protection of farmland in recent years. Firstly, according to the requirement of basic farmland delineation, we construct a methodology consisting of suitability and coordination, which represents the impact of natural conditions and land-use system of basic farmland protection areas. Secondly, we choose various index based on the local situation. The suitability index contains slope, soil texture, river system distance, and degree of soil salinization. The coordination index contains the distance to the center town, polygon area, the proportion of arable land, and proportion of overlapping planning zone. The weight of each index is determined by the Delphi method with relevant reference information as well as provincial experienced experts. Besides, with the help of ArcGIS9.3, we use Comprehensive Fitness Evaluation Model and Experience Index Method to calculate and drawing the suitability classification map and land-use system coordination classification map. Lastly, we use GIS spatial analysis to superimpose two classification maps, and mutually exclusive matrix classification is used to integrate these two reciprocally independent indicators. The result shows that, the basic farmland is divided into five different types according to the classification matrix: I. Completely rational zone; II. Basic rational zone; III. Development reserve zone; IV. Reduction zone and V. Unreasonable zone. The rational region (I, II and III types) accounted for 90.76% of total basic farmland protection area in Yinchuan, which means that the current planning scheme is basically rational. Unreasonable regional area (IV and V types) accounted for 9.24%, which mainly distribute in the edge of basic farmland concentration area, with patchy distribution. These districts are influenced by two aspects of the suitability and coordination. It is suggested that delineating basic farmland area does not only mean selecting high quality arable land by technical, but also focusing on the influence of the coordination between the various factors of the land use system. This study provides a new way of evaluating basic farmland's spatial layout, points out the techniques and methods, and shows specific evaluation and decision-making process. The innovation of this paper is that: 1) Establishing evaluation index of system coordination; 2) Applying mutually exclusive categories to integrating natural conditions and system coordination; 3) By selecting Yinchuan city as study area, demonstrating the problems in basic farmland protection areas delineated in western regions of China. Source

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