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中国农机化学报

中国农机化学报 ›› 2022, Vol. 43 ›› Issue (5): 211-217.DOI: 10.13733/j.jcam.issn.20955553.2022.05.030

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基于BoxBehnken模型大蒜机播成本影响因素分析

吴小伟1,张璐1,唐莉莉1,韦家武1,陆海莉1,周忠诚2   

  1. 1.江苏省农业机械技术推广站,南京市,210017; 2.江苏省农业农村厅,南京市,210036
  • 出版日期:2022-05-15 发布日期:2022-05-17
  • 基金资助:
    江苏省现代农机装备与技术示范推广项目(NJ2020—26、NJ2019—17);江苏省农业科技自主创新资金项目(CX(19)2007)

Influencing factors analysis of garlic mechanized sowing cost based on BoxBehnken model

Wu Xiaowei, Zhang Lu, Tang Lili, Wei Jiawu, Lu Haili, Zhou Zhongcheng.   

  • Online:2022-05-15 Published:2022-05-17

摘要: 为解决大蒜播种机购机价格、维修保养、配套动力等方面对机械化播种成本影响的主次顺序不明确现状,开展大蒜机械化播种成本影响因素分析。依据BoxBehnken中心组合设计理论模型,通过选取单机价格、机械运行成本、播种效率、种植年限、种植面积为分析因素,播种成本为响应指标,进行相关性分析。结果表明,种植面积是影响播种成本的最大因素,机械运行成本是影响播种成本的最小因素,各因素对播种成本的影响呈现如下关系:种植面积>播种效率>单机价格>种植年限>机械运行成本,分析发现84.78%的机播方式优于人工播种成本。播种成本模型优化后,F值为593.33,P<0.000 1,模型决定系数R2值为0.994 8,失拟项P值为0.91,播种成本预测值与实际值跟踪效果较好,可知优化模型可靠。研究结果以期为播种成本预测和选用适宜的机械化播种技术与装备提供参考。

关键词: 大蒜, BoxBehnken模型, 机械化播种, 播种成本, 回归分析, 响应面分析

Abstract:  In order to solve the problem of ambiguity of main factors affecting mechanized sowing cost of garlic, the factors of garlic planter price, maintenance price and tractor price were analyzed. Based on the BoxBehnken central composite design theory model(BBD model), the correlation analysis was carried out by planter price, mechanical operation cost, sowing efficiency, planting years, planting area, and sowing cost as response. The results showed that planting area was the biggest factor affecting sowing cost, and mechanical operation cost was the smallest factor affecting sowing cost. The effects of factors on sowing cost as follows: planting area>sowing efficiency>planter price>planting years>mechanical operation cost. Analysis found that 84.78% of the machine sowing method was better than the artificial sowing cost. After optimization of the sowing cost model, the F value was 593.33, P<0.000 1, model determination coefficient R2 was 0.994 8, and lack of fit P value was 0.91. The tracking effect of the predicted and actual sowing cost values was good, indicating that the optimization model was reliable. The results are expected to provide reference for garlic sowing cost prediction and selection of suitable mechanized sowing technology and equipment.

Key words: garlic, BBD model, mechanized sowing, sowing cost, regression analysis, response surface analysis

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