Study On Index System For The Monitoring Of Farmland Quality

Farmland is a complex system resulted from both natural environment and human society, which have a great influence on its quality. Based on arable grading appraisal, with the help of field method, mathematical statistics method and technology integration of RS and GIS, after study in Dalad Banner, Inne Mongolia, quality monitoring index system is set up in accordance with the three factors which influence farmland quality including natural factors, land use factors and land economic factors. Besides, monitoring period and methods are analyzed. The study results can provide foundation and technique support to farmland quality dynamic monitoring and farmland quality improving. Following is the main study results.Firstly, farmland quality monitoring index system is set up. This system is made up of nature index which can reveal the local productivity and environmental quality, land use index which can present farmland real productivity like grain production, price and so on, and land economy factor to embody the change of overall quality comprehensively. What’s more, grading index in farmland grading is taken into consideration and used to that it can meet requirements of the main farmland quality change evaluation system.Secondly, monitoring period for farmland quality monitoring index is decided. With factor stability analysis, farmland quality monitoring index factors are classified into stable factor, gradually changing factor and suddenly changing factor. According to different monitoring scope and degree of farmland quality changing, monitoring period consists of fixed monitoring, yearly monitoring and real-time monitoring. Fixed monitoring is usually applied to stable factors, once in five years or ten years, while gradually changing factor is monitored yearly and real-time monitoring is used to monitor sudden changing factor.Thirdly, relative methods for farmland quality monitoring are put forward. There are filed research, remote sensing, test report and model prediction, among which, remote sensing enable researchers to obtain target information intensively, which can never be found in traditional monitoring methods. As a result, remote sensing should be considered to use firstly, especially in real-time monitoring.