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中国水稻机插秧发展趋势预测与装备需求研究 被引量:14

Development Trend Forecast and Equipment Requirements of the Rice Transplanting Mechanization
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摘要 水稻机插秧是中国农机化发展的难点与重点,长期以来一直滞后于其他作业环节,未来发展趋势准确预测对促进机插秧的发展非常重要,然而国内理论界对水稻机插秧的发展趋势与装备需求缺乏定量研究。本文利用水稻主产区近10年的历史数据,基于Eviews 5.0面板回归,对影响水稻机插水平的主要因素进行了深入分析,找到了农业机械化水平与各因素之间的回归关系,并对插秧机需求量进行了研究。研究表明,水稻户均种植规模是制约机插秧发展最主要的因素,平地比例越高机插水平越高,到2020年中国水稻机插秧水平仍只能达到65%左右,并需要新增插秧机70万台左右,应鼓励耕地流转从而促进耕地适度规模经营,同时加大农田基本建设力度。 Rice transplanting technology, which haslagged behind other aspects of the working link,is the emphasis and difficulty of agricultural mechanization development in China. Therefore, accurateprediction of future trends is very important to promote the development of the rice transplanting mecha-nization. However, there is a lack of quantitative research on the development trend and equipment re-quirements of the rice transplanting mechanization. In this studied, the data of main rice producing ar-eas in the past 10 years was collected, based on panel regression of Eviews 5.0, the main factors influ-encing the rice planting mechanization were deeply analyzed to find out the regression relationship be-tween agricultural mechanization level and the various factors, and to initially predict the market de-mand of the rice planting mechanization. The result showed that the average plant scale was the mostimportant factor restricting the development of transplanting mechanization. The higher proportion ofthe ground meant, the higher level of the planting mechanization was. The level of rice transplantingmechanization can only reach 65% in 2020 with 700 000 transplanters required. Therefore, farmlandtransfer should be encouraged to promote the appropriate scale of arable farmland, meanwhile, thefarmland capital construction also should be strengthened.
出处 《云南农业大学学报(自然科学版)》 CAS CSCD 北大核心 2015年第2期289-293,共5页 Journal of Yunnan Agricultural University:Natural Science
基金 中国农业科学院科技创新工程 中国农业科学院基本科研业务费项目(2014ZL028)
关键词 水稻机插秧 机械化水平 装备需求 面板数据 预测 rice planting mechanization mechanization level equipment requirements panel data forecast
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