农场农业机器作业成本动态预测模型的研究

农业机器作业成本的预测是优化决策的关键,该文提出了建立农业机器作业成本动态预测模型的一般方法,模型由折旧费、维修保养费、油料费、劳动力成本和管理费5个预测子模型组成.模型结果表明,利用农场相关实测数据建立的模型均达到了较高的精度,其中拖拉机残值系数、累积维修保养费系数回归模型精度的调节平方和分别为0.8367和0.8840;模型的比较分析表明,在2007年机龄为9.16 a的6号JDT654拖拉机组成型耕机组和旋耕机组分别完成180.28 hm2和165.46 hm2作业面积的条件下,二台机组总作业成本的预测值分别为28585.79元和23868.42元,预测偏差分别-2.11%和-5.92%;二台机组作业总成本和的预测偏差为-3.88%.

Dynamic prediction model for operation costs of agricultural machinery in Chinese state farms

In order to optimize agricultural machinery system, it is necessary to predict operation costs of agricultural machinery. Based on real situation in Chinese state farm, the dynamic prediction model for operation costs of agricultural machinery was proposed. The operation costs of agricultural machinery were composed by depreciation costs, maintenance costs, fuel costs, labor costs, and management costs. The results show that the models have reached high precision, of which the adjusted sum squares of remaining value index and cumulated reapir and maintenance index are reached 0.8367 and 0.8840, respectively. The comparative analyses of the models show that total prediction operation costs are 28585.79 yuan for plough working unit and 23868.42 yuan for rotary tiller working unit, respectively, when these two working units powered by tractor JDT654 No.6 are supposed to finish operation area of 180.28 hm2 and 165.46 hm2 respectively in 2007. The prediction errors are -2.11% and -5.92% respectively, and the prediction error of the total operation costs of the two working units is -3.88%.