农业机器系统优化模型与水稻种植区典型系统评价的研究
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摘要
农业机器系统是指作物、土壤和农业机器等组成的一个大系统,其中农业机器的功能是在特定的气候、土壤等作业条件下,完成作物生产作业工序要求的各项任务,国内农业机器的作业成本通常要占到农业生产总成本的30-50%,因此,农业机器系统优化问题的研究具有重要的理论和实践意义。
很多学者对农业机器系统进行了研究,但目前还存在一些问题有待解决,如农业机器选型尚局限于选型方法的研究,缺乏能指导生产实践的应用性成果；农业机器折旧成本的估计缺乏科学依据,一般都用直线折旧法来计算折旧费用,与农业机器实际价值转移规律并不一致。维修保养费的估计没有充分考虑机器类型、机龄、累积作业小时和作业工序等因素的显著影响,导致估计值与实际值有很大差异；农业机器的可下地概率一般根据经验来估计,没有充分考虑当地气候条件和土壤条件等特殊性,导致估计值与实际值有很大偏差；农业机器关键作业的适时性损失尚无应用性成果,优化时一般限定某项作业在规定时间段内完成,与实际作业情况有差异；优化配备中一般也没有考虑农业机器完好率的影响等。
本文首先对农业机器产品的满意度进行了调查与分析,研究结论可为选型提供重要依据。其次根据农场实际数据对农业机器系统优化的几个关键子模型进行了研究,即残值系数模型、可下地概率相关模型和作业成本预测模型,并在这些模型的基础上构造了新的农业机器系统优化配备模型。最后对水稻种植区典型的农业机器系统进行了比较分析与评价,评价结果对各稻区有重要的指导意义。
在对农业机器产品的满意度进行调查与分析时,采用美国社会心理学家R.A.Likert提出的李克特量表(Likert Scale)设计了问卷,采用广泛调查法获得了满意度分析的样本数据,产品范围涉及到50个生产厂家生产的轮式拖拉机、联合收获机、插秧机、秸秆还田机和条播机五大类产品中的150个具体产品,调查对象均为熟悉被调查产品的管理人员、服务人员和机手,共在江苏省13个地级市和省农机局直属机关发放调查问卷11102份,现场回收有效问卷9982份,保证了样本数据的真实性和可靠性,并用SAS软件进行了数据处理。研究结果表明,五大类农业机器产品质量满意度的次序为：拖拉机、秸秆还田机、条播机、联合收获机和插秧机；五大类农业机器产品中,拖拉机、秸秆还田机、条播机、联合收获机和插秧机满意度在“基本满意”以上的产品数比例分别为91.23%、100%、94.12%、87.10%和81.82%,而在“比较满意”以上的产品数比例分别为84.12%、91.18%、88.24%、41.94%和72.73%,说明拖拉机、秸秆还田机和条播机的满意度比较高,而联合收获机和插秧机的满意度相对来说还比较低；试验还得出了全部50个生产厂家的满意度排序、五大类13个系列产品的满意度排序,同一厂家不同型号联合收获机的满意度之间还呈现出显著的相关性(相关系数为0.9061)。本研究成果已在江苏省得到了具体应用。
在对残值系数模型的研究中,采集了江苏省大丰市的上海农场近15年的128个样本数据,随机抽取其中116个样本来构造模型,并用其它12个样本进行了验证试验,结果表明,双平方根模型为6个备选模型中的最佳残值系数模型(调节平方和为0.8367),残值系数模型中机龄是决定实际残值系数的主要因素,对比分析表明,预测模型中12个验证样本中的8个样本的预测偏差在以±10%内,验证样本中最大预测偏差为17.50%；而在ASABE模型中12个验证样本中仅有3个样本的预测偏差在±10%以内,验证样本中最大预测偏差为47.59%；说明新建立的拖拉机残值系数模型达到了满意的精度,可以用来预测我国同类条件下大中型拖拉机的残值系数。
在对农业机器可下地概率相关模型进行研究时,采集了近15年的历史气象数据,并建立了蒸发量和降雨量模型,结果表明,农场上半年和下半年的蒸发量模型都达到了较高的预测精度(调节平方和分别为0.7985和0.7167),平均温度和日照小时数对蒸发量均有显著影响,在同样温度和日照小时数条件下,上半年的蒸发量比下半年的蒸发量大。实证分析表明,蒸发量模型的预测精度是满意的,随机抽取的12个验证样本中,9个样本的预测偏差在±20%以内；用马尔科夫模型来预测降雨量这样一个随机过程是合理的,2006年、2007年多数月份的模型预测结果与实际月降雨量是一致的,从而为今后充分利用农业气象信息提供了一种新的方法与手段。最后建立了月降雨量与可下地概率的经验关系,为实践中根据预测的月降雨量来估计当月农业机器的可下地概率提供了依据。
在对农业机器作业成本预测模型进行研究时,根据农场的实际情况,农业机器的总作业成本主要包括折旧费、维修保养费、油料费用、人员工资和管理费5项成本,采集了7台JDT-654拖拉机机组历史年的相关数据,并用SAS进行了建模分析,结果表明,本文提出的建立农业机器作业成本预测模型的一般方法是可行的；根据农场实际数据建立的拖拉机残值系数、JDT-654拖拉机累积维修保养费系数的回归方程均达到了较高的预测精度(调节平方和分别为0.8367和0.8840),可以用来科学预测拖拉机的折旧成本和维修保养成本；实证分析中6号JDT-654拖拉机组成的犁耕机组和旋耕机组作业成本的预测偏差分别为-2.11%和-5.92%,这台拖拉机所有机组的总作业成本的预测偏差为-3.88%,证实了模型的可行性。典型机组的经济性分析表明,与纽荷兰110-90拖拉机和JDT-654拖拉机组成的犁耕机组和旋耕机组在5年内的单位面积平均作业成本分别为316.47元·hm-2、139.65元·hm-2和242.24元·hm-2、122.64元·hm-2,纽荷兰110-90犁耕机组和旋耕机组5年内平均作业成本要JDT-654拖拉机同类机组分别高126.62%和97.52%；JM-1605收获机5年内的单位面积作业成本为168.03元·hm-2,并在3年到4年间平均作业成本达到最低值。
在建立农场农业机器系统优化配备模型时,首先假定已建立了农业机器作业成本中5项成本预测的子模型,选择农业机器系统总作业成本最小为目标,主要约束条件包括作业量约束、拖拉机或联合收获机工作时间约束、作业工序先后顺序约束、特定机型选择约束、有效作业时间约束和变量非负约束等,构造了农业机器系统优化配备的非线性规划模型。本模型的主要特点是模型中各项成本是用预测模型来估计的,提高了预测精度,充分考虑了田间作业工序的先后顺序要求,允许用户选择目标机型等。
在对我国三大稻区水稻生产的典型农业机器系统方案进行比较分析时,系统介绍了三大稻区的典型农业机器系统方案,并安排了农业机器系统方案的比较试验,这些系统方案的种植方式包括常规育秧手工插秧、机械插秧、机械直播、和机械钵苗行栽等,试验的品种为当地种植面积较大的6个品种,采集了不同试验方案中的各项成本,如种子、化肥、水费等物质成本,农业机器作业成本和劳动力成本,测定了主要水稻产量指标如样本穗数、样本粒数、样本实粒数、平均千粒质量、理论平均产量和实际平均产量等,还测定作业环节消耗工时等数据,以评价不同种植方式的农业机器系统方案在“省工、节本、增效”方面的实施效果。结果表明,南方一季稻区三种不同农业机器系统方案的水稻生产总成本的排序为：常规育秧手插方案>机械直播方案>机械插秧方案,常规育秧手插方案、机械直播方案和机械插秧方案中农业机器作业成本占总成本的比重平均值分别为28.56%、43.20%和42.91%；南方双季稻区早杂交稻机插秧方案比常规育秧手插方案的总成本降低11.46%,其中常规育秧手插方案和机械插秧方案中农业机器作业成本占总成本的比重分别为33.92%和48.88%；而晚杂交稻机插秧方案比常规育秧手插方案的总成本降低9.47%,其中常规育秧手插方案和机械插秧方案中农业机器作业成本占总成本的比重分别为30.72%和43.30%；北方稻区常规稻手插和机械钵苗行栽两种方案的总成本比较接近,常规育秧手插方案和机械钵苗行栽方案中农业机器作业成本占总成本的比重分别为18.69%和25.70%；在南方二稻区,机械插秧方案平均增产7.53%、平均省工41.44%,平均增加效益46.78%,在所有方案中“省工、节本、增效”效果最为显著,是一项值得在大部分稻区推广应用的种植方案；机械直播方案产量平均下降6.34%,平均省工31.30%,而对效益的影响因品种不同而表现出差异性,常规稻效益略有下降,杂交稻平均增效8.50%；机械钵苗行栽方案平均增产8.95%、省工64.29%,增加效益22.57%,是北方稻区值得进一步示范和推广的一种水稻种植技术。以上研究结论对中国各主要稻区明确今后水稻生产机械化的发展方向有一定的参考价值。
最后本文还提出了农业机器系统优化有待进一步深入解决的几个问题。
Farm machinery system is composed of a number of factors such as crop, soil and farm machinery etc., the function of which is to perform field operations relating to crop production in specific weather and soil conditions. Research show that field operations by farm machinery may account to 30-50% of total cost for crop production in China, making the task of optimization of farm machinery system significant both in theory and practice.
Though a number of researches have been conducted on the farm machinery systems, some key problems still remain unresolved. One of which is how to correctly select suitable farm machineries, as most studies were limited to theories and lacking enough operational approaches to provide guidance to farmers. The second problem is the determination of the depreciation cost, of which the linear model of depreciation cost were generally adopted, deviating largely from the real situation for most types of farm machinery. The third is the repair and the maintenance cost, here attention were not enough paid to the application of machinery information, such as machinery type, age, cumulated working hours and field working procedure etc. The forth is the machinery field workable probability, which were occasionally judged empirically other than enough consideration on the local weather and soil conditions. Another problem is the lack of operations from timeliness cost of key operations, due to the unavailable experience function of timeliness cost for most regions, in most cases key field operations were limited to perform in fixed period. The last problem is the ignorance of the reliability degree of farm machinery upon optimization.
The customer satisfaction degrees of farm machinery were firstly surveyed and analyzed, yielding results that can be applied as a basis for farm machinery selection. After that a number of sub-models based on real farm data were studied, which include value index model for farm machinery and those relating to machinery field workable probability and field operation cost forecast. Again the provision of these sub-models facilitated the optimization of a renovated model. For the purpose of evaluation of the new models, field tests were conducted on typical rice production systems with different features and evaluations were performed on each rice production regions in China.
In order to analyze customer satisfaction of different farm machinery products, a questionnaire was designed in accordance to Likert scale. And then totally 9982 questionnaires were collected from Jiangsu Province, sources of which came from managers, extension workers and drivers familiar or expertise in this area. The investigation covered five types and 150 specific products made from 50 different manufacturers. The five types of farm machinery include tractor, straw-return rotary tiller, planter, combine harvester, and rice transplanter. Their grades, distribution and ite correlations to customer satisfaction were analyzed in SAS software. Results show that customer satisfactions on the five types of farm machinery products were ranked, from high to low, as tractor, straw-return rotary tiller, planter, combine, and rice transplanter, respectively. The corresponding rates of products number related to the customer satisfaction surpassed the "general satisfaction" to 91.23%,100%,94.12%,87.10%and 81.82%, respectively. And those higher than the "relative satisfaction" were 84.12%, 91.18%,88.24%,41.94% and 72.73%, respectively. The detailed analysis yielded the ranks of customer satisfactory for each manufacturer and each 13 different series products, showing that the customer satisfaction correlation for combines manufactured by the same manufacturer was 0.9061. These achievements have already been successfully used to manage farm machinery in Jiangsu province.
To develop new residue value index models for medium tractors,128 tractor auction sales data in the past 15 years in Shanghai State Farm were collected. From them sub 116 samples were randomly selected as regression samples, and the remaining 12 data were designated as testing data. Different Box-Cox transformations and regression methods were carried out in SAS, showing that the double square root model was best one among the 6 proposed explanatory models for tractor age and average yearly working hours. Tractor age was the main factor for determining tractor residue value index. The test on the real data also show that the forecasting errors of the model were within±10%for 8 of the 12 tested samples, the maximum one is-17.15%, and that of ASABE model were within±10%only for 3 of the 12 tested samples, the maximum one is 47.59%. This proved that such models can supply satisfactory forecasting results in similar situations.
Field workable probability of farm machinery was predicted with weather data from the last 15 years and then it was linked to models developed. The results show that evaporation models of the first half year and the second half year were satisfactory (adjusted sum square were 0.7985 and 0.7167 respectively). Both average daily temperature and daily sunshine hours affect evaporation dramatically, with the evaporation in the first half year slighter higher under the same temperature and sunshine hours. The real data test also show the forecast errors of the models, ranging in±20% for 9 of the 12 tested samples. Markvo model was a reasonable tool for the forecast of monthly rainfall, mainly due to its randomized procedure. The application of this model revealed a good fit of the forecasted rainfall with the real ones for most of the months in 2006 and 2007. Thus it is a good way to make use of weather information. Finally, empirical relationship between rainfall and machinery field workable probability was provided, and thereby machinery field workable probability can be forecasted with monthly rainfall data.
To develop forecasting models of operation cost for farm machinery, it was necessary to forecast different cost items, such as depreciation cost, repair and maintenance cost, fuel consumption cost, labor cost and management cost. Data related to 7 sets of working units of JDT654 tractor was further collected, from which forecasting models for five field operation cost items of farm machinery were derived. Results show that the proposed methods for the development of forecasting models were feasible, with 0.8367 for adjusted sum square and 0.8840 for depreciation cost model relating to repair and maintenance, respectively. Forecasting errors were-2.11% and-5.92% for ploughing implement and rotary tiller, respectively. The average forecasting error for the two implements was-3.88%. Case study show an average operation cost for ploughing implement and roatry tiller powered by tractor 110-90 and tractor JDT-654 were 316.47,139.65 yuan per hectare and 242.24,122.64 yuan per hectare respectively, in the first five years. The average cost of them were 126.62% and 97.52% higher, respectively, while the average operation cost of combine JM-1605 was 168.03 yuan per hectare in the first five years, a value being the least one among machineries with age of 3 to 4 years.
The development of a non-linear optimization model for farm machinery systems was base on the provision of a number of, say five, sub-models. Optimization objective of the model was to achieve the least machinery field operation cost, with constraints of the quantity of field work, working hours of working units or combines, field operation sequences, target for select some specific machinery, available working hours and non-negative variables. Thus the main advantages of the model were to allow the use of sub-models to forecast individual cost items, to consider the sequences of machinery field operation, and to select special model machinery etc.. The optimization results were achieved by using LINDO software.
In order to evaluate typical farm machinery systems of rice production in the three main rice planting regions in China, i.e. the one-season rice production region of South China(OSSC), two-season rice production region of South China(TSSC) and rice production region of North China(NC), a brief introduction of these typical systems was followed with a series field tests. Rice planting methods include traditional manual transplanter(TMT), rice mechanized transplanter(MT), rice mechanized direct seeding(MDS) and rice mechanized potted-seedling transplanter(MPST). Variables used in the test include 6 types of rice seeds, seed cost, fertilizer and chemical cost, water cost, machinery field operation cost, labour cost, as well as main rice yield indexes of sample fringes, sample grains, sample fruit grains, average weight per thousand grains, average theoretical yields, average real yields, labour hours for each field operation etc.. Based on the data, evaluation were assigned to the effects of labour saving, cost saving and benefit increasing. So that the ranking list for each total operation cost was provided, from high to low, as TMT, MDS and MT. Their average machinery field operation costs account to 28.56%,43.20% and 42.91% in OSSC, respectively. The highest total operational costs were TMT and MT, either for early and for late rice production, with a respective difference of 11.46% and 9.47%. Average machinery field operation costs account to 33.92%,30.72% and 48.88%,43.30% respectively in TSSC. Total operation costs for the system with TMT and MPST are close, their respective average machinery field operation costs account to 18.69% and 25.70% in NC. Compared with the system of TMT, MT was recommended for both OSSC and TSSC. ough rice yields and net profit are increased by 7.53% and 46.78%in average, and labor hours is reduced by 41.44% in average. Rough rice yields are decreased by 6.34% and labor hours is reduced by 31.30% in average for the system with MDS, their net profit are close or increased by 8.5% for traditional rice and cross one respectively. The system with MPST is recommended in NC, compared to the system with TMT. Rough rice yields and net profit were increased by 8.95% and 22.57% respectively, and labor hours reduced by 64.29%. These results served as a good guideline for the selection of farm machinery system for different rice planting regions in China.
Finally, some suggestions are also given for further research.
很多学者对农业机器系统进行了研究,但目前还存在一些问题有待解决,如农业机器选型尚局限于选型方法的研究,缺乏能指导生产实践的应用性成果；农业机器折旧成本的估计缺乏科学依据,一般都用直线折旧法来计算折旧费用,与农业机器实际价值转移规律并不一致。维修保养费的估计没有充分考虑机器类型、机龄、累积作业小时和作业工序等因素的显著影响,导致估计值与实际值有很大差异；农业机器的可下地概率一般根据经验来估计,没有充分考虑当地气候条件和土壤条件等特殊性,导致估计值与实际值有很大偏差；农业机器关键作业的适时性损失尚无应用性成果,优化时一般限定某项作业在规定时间段内完成,与实际作业情况有差异；优化配备中一般也没有考虑农业机器完好率的影响等。
本文首先对农业机器产品的满意度进行了调查与分析,研究结论可为选型提供重要依据。其次根据农场实际数据对农业机器系统优化的几个关键子模型进行了研究,即残值系数模型、可下地概率相关模型和作业成本预测模型,并在这些模型的基础上构造了新的农业机器系统优化配备模型。最后对水稻种植区典型的农业机器系统进行了比较分析与评价,评价结果对各稻区有重要的指导意义。
在对农业机器产品的满意度进行调查与分析时,采用美国社会心理学家R.A.Likert提出的李克特量表(Likert Scale)设计了问卷,采用广泛调查法获得了满意度分析的样本数据,产品范围涉及到50个生产厂家生产的轮式拖拉机、联合收获机、插秧机、秸秆还田机和条播机五大类产品中的150个具体产品,调查对象均为熟悉被调查产品的管理人员、服务人员和机手,共在江苏省13个地级市和省农机局直属机关发放调查问卷11102份,现场回收有效问卷9982份,保证了样本数据的真实性和可靠性,并用SAS软件进行了数据处理。研究结果表明,五大类农业机器产品质量满意度的次序为：拖拉机、秸秆还田机、条播机、联合收获机和插秧机；五大类农业机器产品中,拖拉机、秸秆还田机、条播机、联合收获机和插秧机满意度在“基本满意”以上的产品数比例分别为91.23%、100%、94.12%、87.10%和81.82%,而在“比较满意”以上的产品数比例分别为84.12%、91.18%、88.24%、41.94%和72.73%,说明拖拉机、秸秆还田机和条播机的满意度比较高,而联合收获机和插秧机的满意度相对来说还比较低；试验还得出了全部50个生产厂家的满意度排序、五大类13个系列产品的满意度排序,同一厂家不同型号联合收获机的满意度之间还呈现出显著的相关性(相关系数为0.9061)。本研究成果已在江苏省得到了具体应用。
在对残值系数模型的研究中,采集了江苏省大丰市的上海农场近15年的128个样本数据,随机抽取其中116个样本来构造模型,并用其它12个样本进行了验证试验,结果表明,双平方根模型为6个备选模型中的最佳残值系数模型(调节平方和为0.8367),残值系数模型中机龄是决定实际残值系数的主要因素,对比分析表明,预测模型中12个验证样本中的8个样本的预测偏差在以±10%内,验证样本中最大预测偏差为17.50%；而在ASABE模型中12个验证样本中仅有3个样本的预测偏差在±10%以内,验证样本中最大预测偏差为47.59%；说明新建立的拖拉机残值系数模型达到了满意的精度,可以用来预测我国同类条件下大中型拖拉机的残值系数。
在对农业机器可下地概率相关模型进行研究时,采集了近15年的历史气象数据,并建立了蒸发量和降雨量模型,结果表明,农场上半年和下半年的蒸发量模型都达到了较高的预测精度(调节平方和分别为0.7985和0.7167),平均温度和日照小时数对蒸发量均有显著影响,在同样温度和日照小时数条件下,上半年的蒸发量比下半年的蒸发量大。实证分析表明,蒸发量模型的预测精度是满意的,随机抽取的12个验证样本中,9个样本的预测偏差在±20%以内；用马尔科夫模型来预测降雨量这样一个随机过程是合理的,2006年、2007年多数月份的模型预测结果与实际月降雨量是一致的,从而为今后充分利用农业气象信息提供了一种新的方法与手段。最后建立了月降雨量与可下地概率的经验关系,为实践中根据预测的月降雨量来估计当月农业机器的可下地概率提供了依据。
在对农业机器作业成本预测模型进行研究时,根据农场的实际情况,农业机器的总作业成本主要包括折旧费、维修保养费、油料费用、人员工资和管理费5项成本,采集了7台JDT-654拖拉机机组历史年的相关数据,并用SAS进行了建模分析,结果表明,本文提出的建立农业机器作业成本预测模型的一般方法是可行的；根据农场实际数据建立的拖拉机残值系数、JDT-654拖拉机累积维修保养费系数的回归方程均达到了较高的预测精度(调节平方和分别为0.8367和0.8840),可以用来科学预测拖拉机的折旧成本和维修保养成本；实证分析中6号JDT-654拖拉机组成的犁耕机组和旋耕机组作业成本的预测偏差分别为-2.11%和-5.92%,这台拖拉机所有机组的总作业成本的预测偏差为-3.88%,证实了模型的可行性。典型机组的经济性分析表明,与纽荷兰110-90拖拉机和JDT-654拖拉机组成的犁耕机组和旋耕机组在5年内的单位面积平均作业成本分别为316.47元·hm-2、139.65元·hm-2和242.24元·hm-2、122.64元·hm-2,纽荷兰110-90犁耕机组和旋耕机组5年内平均作业成本要JDT-654拖拉机同类机组分别高126.62%和97.52%；JM-1605收获机5年内的单位面积作业成本为168.03元·hm-2,并在3年到4年间平均作业成本达到最低值。
在建立农场农业机器系统优化配备模型时,首先假定已建立了农业机器作业成本中5项成本预测的子模型,选择农业机器系统总作业成本最小为目标,主要约束条件包括作业量约束、拖拉机或联合收获机工作时间约束、作业工序先后顺序约束、特定机型选择约束、有效作业时间约束和变量非负约束等,构造了农业机器系统优化配备的非线性规划模型。本模型的主要特点是模型中各项成本是用预测模型来估计的,提高了预测精度,充分考虑了田间作业工序的先后顺序要求,允许用户选择目标机型等。
在对我国三大稻区水稻生产的典型农业机器系统方案进行比较分析时,系统介绍了三大稻区的典型农业机器系统方案,并安排了农业机器系统方案的比较试验,这些系统方案的种植方式包括常规育秧手工插秧、机械插秧、机械直播、和机械钵苗行栽等,试验的品种为当地种植面积较大的6个品种,采集了不同试验方案中的各项成本,如种子、化肥、水费等物质成本,农业机器作业成本和劳动力成本,测定了主要水稻产量指标如样本穗数、样本粒数、样本实粒数、平均千粒质量、理论平均产量和实际平均产量等,还测定作业环节消耗工时等数据,以评价不同种植方式的农业机器系统方案在“省工、节本、增效”方面的实施效果。结果表明,南方一季稻区三种不同农业机器系统方案的水稻生产总成本的排序为：常规育秧手插方案>机械直播方案>机械插秧方案,常规育秧手插方案、机械直播方案和机械插秧方案中农业机器作业成本占总成本的比重平均值分别为28.56%、43.20%和42.91%；南方双季稻区早杂交稻机插秧方案比常规育秧手插方案的总成本降低11.46%,其中常规育秧手插方案和机械插秧方案中农业机器作业成本占总成本的比重分别为33.92%和48.88%；而晚杂交稻机插秧方案比常规育秧手插方案的总成本降低9.47%,其中常规育秧手插方案和机械插秧方案中农业机器作业成本占总成本的比重分别为30.72%和43.30%；北方稻区常规稻手插和机械钵苗行栽两种方案的总成本比较接近,常规育秧手插方案和机械钵苗行栽方案中农业机器作业成本占总成本的比重分别为18.69%和25.70%；在南方二稻区,机械插秧方案平均增产7.53%、平均省工41.44%,平均增加效益46.78%,在所有方案中“省工、节本、增效”效果最为显著,是一项值得在大部分稻区推广应用的种植方案；机械直播方案产量平均下降6.34%,平均省工31.30%,而对效益的影响因品种不同而表现出差异性,常规稻效益略有下降,杂交稻平均增效8.50%；机械钵苗行栽方案平均增产8.95%、省工64.29%,增加效益22.57%,是北方稻区值得进一步示范和推广的一种水稻种植技术。以上研究结论对中国各主要稻区明确今后水稻生产机械化的发展方向有一定的参考价值。
最后本文还提出了农业机器系统优化有待进一步深入解决的几个问题。
Farm machinery system is composed of a number of factors such as crop, soil and farm machinery etc., the function of which is to perform field operations relating to crop production in specific weather and soil conditions. Research show that field operations by farm machinery may account to 30-50% of total cost for crop production in China, making the task of optimization of farm machinery system significant both in theory and practice.
Though a number of researches have been conducted on the farm machinery systems, some key problems still remain unresolved. One of which is how to correctly select suitable farm machineries, as most studies were limited to theories and lacking enough operational approaches to provide guidance to farmers. The second problem is the determination of the depreciation cost, of which the linear model of depreciation cost were generally adopted, deviating largely from the real situation for most types of farm machinery. The third is the repair and the maintenance cost, here attention were not enough paid to the application of machinery information, such as machinery type, age, cumulated working hours and field working procedure etc. The forth is the machinery field workable probability, which were occasionally judged empirically other than enough consideration on the local weather and soil conditions. Another problem is the lack of operations from timeliness cost of key operations, due to the unavailable experience function of timeliness cost for most regions, in most cases key field operations were limited to perform in fixed period. The last problem is the ignorance of the reliability degree of farm machinery upon optimization.
The customer satisfaction degrees of farm machinery were firstly surveyed and analyzed, yielding results that can be applied as a basis for farm machinery selection. After that a number of sub-models based on real farm data were studied, which include value index model for farm machinery and those relating to machinery field workable probability and field operation cost forecast. Again the provision of these sub-models facilitated the optimization of a renovated model. For the purpose of evaluation of the new models, field tests were conducted on typical rice production systems with different features and evaluations were performed on each rice production regions in China.
In order to analyze customer satisfaction of different farm machinery products, a questionnaire was designed in accordance to Likert scale. And then totally 9982 questionnaires were collected from Jiangsu Province, sources of which came from managers, extension workers and drivers familiar or expertise in this area. The investigation covered five types and 150 specific products made from 50 different manufacturers. The five types of farm machinery include tractor, straw-return rotary tiller, planter, combine harvester, and rice transplanter. Their grades, distribution and ite correlations to customer satisfaction were analyzed in SAS software. Results show that customer satisfactions on the five types of farm machinery products were ranked, from high to low, as tractor, straw-return rotary tiller, planter, combine, and rice transplanter, respectively. The corresponding rates of products number related to the customer satisfaction surpassed the "general satisfaction" to 91.23%,100%,94.12%,87.10%and 81.82%, respectively. And those higher than the "relative satisfaction" were 84.12%, 91.18%,88.24%,41.94% and 72.73%, respectively. The detailed analysis yielded the ranks of customer satisfactory for each manufacturer and each 13 different series products, showing that the customer satisfaction correlation for combines manufactured by the same manufacturer was 0.9061. These achievements have already been successfully used to manage farm machinery in Jiangsu province.
To develop new residue value index models for medium tractors,128 tractor auction sales data in the past 15 years in Shanghai State Farm were collected. From them sub 116 samples were randomly selected as regression samples, and the remaining 12 data were designated as testing data. Different Box-Cox transformations and regression methods were carried out in SAS, showing that the double square root model was best one among the 6 proposed explanatory models for tractor age and average yearly working hours. Tractor age was the main factor for determining tractor residue value index. The test on the real data also show that the forecasting errors of the model were within±10%for 8 of the 12 tested samples, the maximum one is-17.15%, and that of ASABE model were within±10%only for 3 of the 12 tested samples, the maximum one is 47.59%. This proved that such models can supply satisfactory forecasting results in similar situations.
Field workable probability of farm machinery was predicted with weather data from the last 15 years and then it was linked to models developed. The results show that evaporation models of the first half year and the second half year were satisfactory (adjusted sum square were 0.7985 and 0.7167 respectively). Both average daily temperature and daily sunshine hours affect evaporation dramatically, with the evaporation in the first half year slighter higher under the same temperature and sunshine hours. The real data test also show the forecast errors of the models, ranging in±20% for 9 of the 12 tested samples. Markvo model was a reasonable tool for the forecast of monthly rainfall, mainly due to its randomized procedure. The application of this model revealed a good fit of the forecasted rainfall with the real ones for most of the months in 2006 and 2007. Thus it is a good way to make use of weather information. Finally, empirical relationship between rainfall and machinery field workable probability was provided, and thereby machinery field workable probability can be forecasted with monthly rainfall data.
To develop forecasting models of operation cost for farm machinery, it was necessary to forecast different cost items, such as depreciation cost, repair and maintenance cost, fuel consumption cost, labor cost and management cost. Data related to 7 sets of working units of JDT654 tractor was further collected, from which forecasting models for five field operation cost items of farm machinery were derived. Results show that the proposed methods for the development of forecasting models were feasible, with 0.8367 for adjusted sum square and 0.8840 for depreciation cost model relating to repair and maintenance, respectively. Forecasting errors were-2.11% and-5.92% for ploughing implement and rotary tiller, respectively. The average forecasting error for the two implements was-3.88%. Case study show an average operation cost for ploughing implement and roatry tiller powered by tractor 110-90 and tractor JDT-654 were 316.47,139.65 yuan per hectare and 242.24,122.64 yuan per hectare respectively, in the first five years. The average cost of them were 126.62% and 97.52% higher, respectively, while the average operation cost of combine JM-1605 was 168.03 yuan per hectare in the first five years, a value being the least one among machineries with age of 3 to 4 years.
The development of a non-linear optimization model for farm machinery systems was base on the provision of a number of, say five, sub-models. Optimization objective of the model was to achieve the least machinery field operation cost, with constraints of the quantity of field work, working hours of working units or combines, field operation sequences, target for select some specific machinery, available working hours and non-negative variables. Thus the main advantages of the model were to allow the use of sub-models to forecast individual cost items, to consider the sequences of machinery field operation, and to select special model machinery etc.. The optimization results were achieved by using LINDO software.
In order to evaluate typical farm machinery systems of rice production in the three main rice planting regions in China, i.e. the one-season rice production region of South China(OSSC), two-season rice production region of South China(TSSC) and rice production region of North China(NC), a brief introduction of these typical systems was followed with a series field tests. Rice planting methods include traditional manual transplanter(TMT), rice mechanized transplanter(MT), rice mechanized direct seeding(MDS) and rice mechanized potted-seedling transplanter(MPST). Variables used in the test include 6 types of rice seeds, seed cost, fertilizer and chemical cost, water cost, machinery field operation cost, labour cost, as well as main rice yield indexes of sample fringes, sample grains, sample fruit grains, average weight per thousand grains, average theoretical yields, average real yields, labour hours for each field operation etc.. Based on the data, evaluation were assigned to the effects of labour saving, cost saving and benefit increasing. So that the ranking list for each total operation cost was provided, from high to low, as TMT, MDS and MT. Their average machinery field operation costs account to 28.56%,43.20% and 42.91% in OSSC, respectively. The highest total operational costs were TMT and MT, either for early and for late rice production, with a respective difference of 11.46% and 9.47%. Average machinery field operation costs account to 33.92%,30.72% and 48.88%,43.30% respectively in TSSC. Total operation costs for the system with TMT and MPST are close, their respective average machinery field operation costs account to 18.69% and 25.70% in NC. Compared with the system of TMT, MT was recommended for both OSSC and TSSC. ough rice yields and net profit are increased by 7.53% and 46.78%in average, and labor hours is reduced by 41.44% in average. Rough rice yields are decreased by 6.34% and labor hours is reduced by 31.30% in average for the system with MDS, their net profit are close or increased by 8.5% for traditional rice and cross one respectively. The system with MPST is recommended in NC, compared to the system with TMT. Rough rice yields and net profit were increased by 8.95% and 22.57% respectively, and labor hours reduced by 64.29%. These results served as a good guideline for the selection of farm machinery system for different rice planting regions in China.
Finally, some suggestions are also given for further research.
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