Efficiency Evaluation of Provincial Grain Production Based on DEA

Agriculture and grain production is the focus all over the world. The agriculture, rural area, peasant are the basis of Chinese modernization construction. The scientific management and evaluation of agriculture grain production could raise the utilization rate of resources effectively. There are two main classes of the methods to the measurement and analysis of the performance which are nonparametric methods and parametric methods. Different from the usual regression statistical methods and neuron network methods, DEA prediction method is about the optimal efficiency analysis, and can give the improving measures of non-technical valid units. In the paper, the efficiency of grain production was analyzed, the input items include labor input, the seeding area of grain, energy, irrigation area, the amount of mechanical power input and agriculture production goods, the output item includes grain yield. The performance of multi-level evaluation index system was constructed at first. The evaluation efficiency of grain production´s BCC mathematic model based on DEA which is parametric method was proposed, the evaluation method information-based was realized by adopting DEAP2.1 and MATLAB. At last, efficiency evaluation of a complex system with the cases from 11 urban districts of Jiangxi province in China was realized, which was more than one homogeneous decision-making units of multi-input (more influencing factors) and multi-output (output index). Provincial grain production efficiency order problems based MDEA was solved by a series of linear programming equations though Matlab7.0. Validity determination of technology and scale was realized, management problems of grain production was diagnosed. Meanwhile, input´s improving measure of non-technical valid units was given out under input-orientation. Suggestions of expanding or downsizing production about units which were non-scale valid were proposed. The model can be used to efficiency analysis and diagnose in differe- nt units at same time or same unit at different time. It can be more accurate and convenient for the management process of grain production.