Urgency and Necessity on Adjustment of China’s Current Agricultural Structure Based on the Stability and Developmental Trend Analyses of Pork Yield in China

With the development of economy, meat gradually plays an important role in Chinese people’s dietary pattern. As the most direct reflection of agricultural system, food system has always been the main output of agricultural system, and pork has occupied an important position in Chinese food system. We attempts to find the systematical disorder of current agricultural system by analyzing the meat output of the agricultural system. H-P Filter and Grey Prediction GM(1,1) Model was adopted to explore the inner rules between pork production and agricultural system in China. The results indicated that pork consumption ratio in Chinese urban residents’ dietary pattern constantly kept about 6%, the growth potential of grain yield is limited while the growth potential of pork yield is increased in China. By Grey Prediction GM(1,1) Model, we predicted the pork yield would reach 59.07 Mt in 2020, 110.25 Mt in 2060, 205.78 Mt in 2100, the demand of feed would reach 177.22 Mt in 2020, 330.75 Mt in 2060, 617.34 Mt in 2100. In China, agricultural system is traditional “pork-grain” mode, feed has been one of the biggest section that consumed grain. The present “pork-grain” agricultural system cannot meet the huge demand of grain from feed, adjusting the agricultural structure is imperative. Reforming the current agricultural system into grassland agricultural system which takes the beef and mutton as predominates would be a good choice for China in the future.

Model predictive control system based on direct yaw moment control for 4WID self-steering agriculture vehicle

A model predictive control(MPC)approach based on direct yaw moment control(DYC)was proposed to realize the self-steering drive for a newly autonomous four-wheel independent-drive(4WID)agricultural electric vehicle.The front axle and rear axle of the vehicle chassis could rotate simultaneously around their respective center points and cut the turning radius in half at most through specific mechanical chassis structure design and four-wheel electrical drive.It had great potential to reduce wheel traffic damage to field crops if two rear electrical drive wheels can be controlled to follow wheel tracks of two front wheels during self-steering operation.Therefore,firstly,a two-degree-freedom dynamics model presenting this agricultural electric vehicle was constructed.Then,an MPC controller combined with DYC was applied to arrange torques from four wheels to match desired turning angles,direct yaw moments and travel speeds.The simulation results existed small steady error of steering angles below 0.22%as they were set at 5°,followed with yaw moment under 0.17%and velocity less than 1%.Finally,according to experiment results,the vehicle successfully made a working turning radius of 9.1 m with maximum error of 0.55%when desired steering angles were 5°at the speed of 1 m/s and a minimum turning radius of 1.51 m with maximum error of 6.6%when steering angles were 30°at the speed of 0.5 m/s.It verified that the 4WID agricultural electric vehicle could drive autonomously and steady with small self-steering angle error under the proposed control system and has a feasibility to reduce wheel traffic damage during driving and operation.

基于灰色GM(1,1)的农业机械化水平预测模型

根据1986～2005年我国农业机械化综合水平统计数据，并对由于我国耕地面积统计滞后使得在机耕水平计算中存在误差进行了合理分析和修正，结合数据平滑处理，建立了基于灰色GM（1，1）的我国农业机械化综合水平预测模型。通过残差检验和后验差检验方法对预测结果进行了检验，模型拟合精度较好。采用模型对2006年我国农业机械化综合水平值进行预测，结果表现出较高的预测精度，进一步验证了所建模型的可行性。运用该模型对我国2007～2020年间农业机械化综合水平进行预测，结果表明到2020年我国综合机械化水平将达到68％左右。通过定性分析及与其他预测结果比较，模型表现出较好的预测能力。

黑龙江省农业机械化作业水平预测方法

黑龙江省农机化作业水平的预测是一个复杂的非线性系统,其发展变化具有增长性和波动性,对于拟合的方法要求较高。该文对黑龙江省农机化作业水平预测方法进行研究,在传统预测模型灰色GM(1,1)模型、平滑预测模型和回归预测模型的基础上建立了基础预测模型,并与BP神经网络模型组合,建立了灰色神经网络、平滑回归神经网络等组合预测模型,并预测了黑龙江省2008～2015年的农机化耕、种、收、植保、灌溉作业水平。结果表明,新的预测方法拟合精度高、有效、可行,为农机化作业水平的预测提供了一条新的途径;黑龙江省机耕、机播、植保作业水平很高,但是机收作业水平不高,机械化灌溉是主要的瓶颈,需要进一步发展。

基于BP神经网络的农业机械化作业水平预测

我国农业机械化作业水平的发展变化具有增长性和波动性,对预测的方法要求较高。鉴于单一预测模型的局限性,在确定我国农业机械化作业水平各单一预测模型的基础上,建立了基于BP神经网络的农业机械化作业水平非线性组合预测模型,并对我国农业机械化作业水平进行预测。误差分析表明,该模型可以有效地提高农业机械化作业水平的预测精度,用该模型对我国2012-2020年农业机械化耕、播、收作业水平进行了预测。预测结果表明,在未来几年我国农业机械化作业水平将保持快速增长趋势,到2020年机耕、机播和机收作业水平分别为91.37%、66.77%和71.93%。

农机化发展的灰色系统预测模型研究

摘 要 采用灰色系统预测模型对农机化发展问题进行了探讨,通过分析认为,灰色系统预测模型具有某些独到的优点。将定性分析及灰色关联分析的结果作为选择进入模型的数据数及修正模型的依据。采用了“线性化处理”及“分段建模”的方法,初步解决了在建立模型时所遇到的GM(1,N)模型的系数不符合定性分析及许多自主因子的较长时期发展过程不能用同一个GM(1,1)模型描述的问题。

面向宜机化整治的丘陵山区耕地生态安全预测与提升路径研究--以重庆市典型地区为例

对丘陵山区耕地实施机械化整治,是提升土地质量、增强农业生产效率及推动农业向高质量发展阶段迈进的关键策略。选取重庆市为研究区,基于径向基函数模型,从耕地生态系统外源干扰胁迫和内源恢复状态角度,测算并预测宜机化整治工程实施前后耕地生态安全水平时空变化特征,识别并诊断其主导障碍因子,进而针对性地提出面向宜机化整治的耕地生态安全提升路径。结果表明:(1)2010—2020年研究区耕地生态安全水平呈现出先下降后上升的“U”型变化趋势,空间上呈现显著的“东高西低”分布格局。(2)预测2021—2025年耕地生态安全水平呈缓慢上升趋势,由0.53增长至0.54,耕地宜机化整治工程的生态正向效应正在逐步显现。(3)整治前期,坡度、景观格局指数等耕地自身生产和生态状态是主要限制因子;整治过程中,工程对地表植被的破坏性是影响耕地生态安全的重要原因;整治完成后,劳动力、农机总动力等农业生产要素配置失衡是降低耕地生态安全的主导因素。(4)未来应以“两山”理念为引领,推进基于生命周期的耕地整治生态建设,同时坚持因地制宜、分区施策等原则,设计实施差异化的宜机化整治耕地生态安全提升路径。研究结果将为落实丘陵山区“三位一体”的耕地保护要求和实现耕地利用生态文明提供科学参考。

基于参数化模型的零转弯半径割草机侧翻稳定性

针对近年来零转弯半径(ZTR)割草机在横向坡道上频发的侧翻安全事故,基于准静态动力学理论,建立了一种坡道工况下能预测分析ZTR割草机失稳的参数化力学模型。根据割草机实际工作状态,加入了驾驶员因素以及不同规格割具在悬挂、牵引状态下对整车侧翻稳定性的影响。利用Matlab求解了理论侧翻预测方程,分析了不同工况下割草机横向载荷转移率与其侧翻敏感参数之间的关系,并利用ADAMS对割草机进行了多组仿真实验,进一步验证了参数化模型的准确性。通常,割草机底部配备悬挂割具可降低整车质心高度从而增强坡道稳定性。但通过实验发现,割草机底部配备牵引式割具比配备悬挂式割具的抗侧翻性能更强:其中配备1.2 m和1.8 m规格悬挂式割具的临界侧翻角较裸车分别提升了7%和10%;而配备1.2 m和1.8 m规格牵引式割具的整车临界侧翻角较裸车分别提升了9%和21%。根据这一特性,建议在设计农用或园林车辆的工作器械时优先考虑底部中置牵引结构,以提高各类车辆在斜坡上工作的安全性。