Study On Construction And Empirical Analysis Of Cultivated Land Quality Monitoring System

Arable land is the essence of land,the most fundamental means of production of agriculture as well as the important guarantee of sustainable agricultural development. How to use arable land rationally relates to people’s living standards and the country’s economic growth.Within the rapid socio-economic and industrial development in China,arable land resources become increasingly scarce,mainly reflected in the decline of land quantity and quality.For a long time,our emphasis on the quantity of land resources at the expense of quality,to a certain extent,affected the output capacity of arable land resources.At present,how to more efficiently monitor arable land quality is still at an exploratory stage.Establishing a scientific monitoring system of arable land quality,has become the main direction of current arable land quality research.Hannan area was selected as the study area for monitoring and evaluating arable land quality.From the perspective of studying monitoring areas of arable land quality, a monitoring system of arable land quality was proposed and built up based on the "pressure- state- response(PSR)" model and the construction of connotation of arable land quality monitoring index system,as well as integrating analytical hierarchy process(AHP) and entropy method to construct an evaluation tool for arable land quality.The main results were as follows:1.Construction of zoning approaches for monitoring arable land quality.Through comparison analysis between cluster analysis method and classification factor combination method regarding to partition of homogeneous arable land areas, "Grades- Limiting factor" method was built up to partition monitoring and controlling areas,integrating the advantages of both methods.Based on the results of agricultural land classification, "Grades- limiting factor" method was used to divide Hannan area into 7 categories of monitoring areas and lay 16 monitoring reference sample units randomly.Surface soil texture,cross-sectional configuration,soil organic matter content and soil pH were selected as four key constrains.Based on the statistic methods,the mean,the standard error of the mean, variance,skewness,and kurtosis of physical grade,soil organic matter content as well as soil pH were calculated for 16 monitoring reference sample units and 596 agricultural land grading units.The results showed that the degree of central tendency and dispersion of both were relatively consistent.The representative test was also conducted for the monitoring reference sample units in the 7 categories of monitoring areas via area representative exponential model.The results showed that the RIA(representative index of areas) of each monitoring reference sample unit was less than or equal to 0.3.2.Construction of arable land quality monitoring and evaluation system.Based on "Pressure-State-Response(PSR)" model,combining with the the connotation of arable land quality,arable land per capita,output of arable land per capita,soil pollution condition,surface soil texture,soil organic matter content,cross-sectional configuration,soil pH,irrigation guarantee rate,drainage conditions,land use efficiency and capital investment efficiency were selected as monitoring indicators.AHP and entropy weight method were integrated to build up "AHP- Entropy method." Based on the data from 16 monitoring sample units,the weights and evaluation results were analyzed and compared from AHP,entropy method and "AHPEntropy method" via weighted factor method.The results showed that:(1)The weight and evaluation results obtained from "AHP- Entropy method" was between the results from the AHP and entropy method.(2)All the evaluation results obtained from AHP were higher than those from entropy method.(3)The weights of cross-sectional configuration as well as soil pH obtained from AHP was only slightly different from those from entropy method.(4)Compared to the AHP,entropy method greatly weakened the impact of soil pollution conditions,surface soil texture,irrigation guarantee rate,drainage conditions,land use efficiency and capital investment efficiency,and strengthens the impact of arable land per capita,food production per capita and soil organic matter content.