Jin Chen
email: jinchen@ujs.edu.cn

Yuelan Zheng
email: yuelan188@163.com

Yaoming Li
email: yaomingli@ujs.edu.cn

Xinhua Wei
email: wei_xh@126.com

Study on Integration of Measurement and Control System for Combine Harvester

Keywords: Combine harvester, ARM, CAN bus, iCAN protocol, integration

Using CAN (controller area network) field bus and iCAN protocol, an integrated measuring and controlling system for combine harvester is constructed in this paper. ARM is used as the processor of host computer. Lower computers are several measuring and controlling modules of combine harvester. Host computer communicates with lower computers through CAN bus. Information between measurement and control modules can be shared so that combine harvester's whole performance can be improved. LCD on host computer shows working state and values of relative parameters of each module in real-time. Experiment shows that the system can work correctly and reliably.

Introduction

With supervision and automatic alarm installed in the combine harvester, it is much easier for operator to know the working condition of some running parts and adjust machine's operating parameters in time. So the fault frequency of combine harvester can be reduced, MTBF (the Mean Time Between Failure) can be longer, and the purpose of improving the whole machine's work efficiency can be realized [START_REF] Zhang | A Study on the Auto-alarm System of Combine Harvester[END_REF]. But at present, the monitoring and controlling objects of combine harvester are mainly walking speed, threshing cylinder speed, state of hydraulic system, etc. Each parameter is basically handled separately. Connections between one another aren't so close [START_REF] Wang | Automotive Electronics Technology Integrated Control System Based on CAN Bus[END_REF], and the information can't be shared [START_REF] Li | State-of-Art and Prospect on Combine Monitor System[END_REF]. Therefore subsystem's function will be influenced and lead to the degradation of the whole machine's performance. To solve this problem, the integration technology is studied in this paper, taking CAN bus as tie. The system is based on the relatively independent modules designed by our studying team. They are Load Feedback Control Module, Real-time Loss Quantity Detecting Module, Electric System Self-checking Module, Mechanical System Fault Monitoring Module, and Hydraulic System Fault Monitoring Module. The system is based on the structure of working process intelligent monitoring and controlling for combine harvester [START_REF] Wei | System Integration of Working Process Intelligent Monitoring and Controlling Devices for Combine Harvester[END_REF], aiming to realize information sharing and improve coordinative relations between parameters effectively.

The System's General Structure

The general structure of intelligent monitoring and controlling system for combine harvester is shown in Fig.

1 [START_REF] Wei | System Integration of Working Process Intelligent Monitoring and Controlling Devices for Combine Harvester[END_REF]. The system consists of host computer and five measurement and control modules as lower computers. Lower computers realize the function of measuring and controlling of subsystems. Host computer is used for parameter setting, communication and display, etc.

Lower computer modules and their functions are as follows:

1) Load Feedback Control Module: Control the walking speed of combine harvester in real time according to parameters like feed rate, threshing cylinder speed and so on.

2)

Real-time Loss Quantity Detecting Module: Detect cleaning loss and entrainment loss real-timely.

3)

Electric System Self-checking Module: Detect working status of electric components like battery and generator.

4)

Mechanical System Fault Monitoring Module: Offer the early warning and fault alarm for mechanical conveying appliance's jam fault based on parameters like threshing cylinder speed, crop screw conveyor speed, etc.

5)

Hydraulic System Fault Monitoring Module: Detect parameters like oil temperature of cooler inlet and outlet, HST forward pressure and recession pressure in real time. Functions of host computer system and each module of it are as follows: 1)

ARM: The processor of host computer.

2)

LCD: Display data delivered from lower computers properly.

3)

Keyboard: Input parameters. [START_REF] Wang | The Design and Application of CAN Field Bus System[END_REF]. Considering combine harvester's harsh working conditions and its own characteristics, CAN field bus is used in this system. ICAN is the abbreviation of "Industry CAN-bus Application Protocol", one of the latest application layer protocols of CAN-bus. It is easy to understand, realize, and has properties of real-time and reliability. ICAN protocol uses the way of connection and management that basically similar to CANopen and DeviceNet protocol. The complicated handshake management and resource allocation etc are deleted. Nods on CAN bus is managed in the way of predefined data combination. ICAN protocol's meeting surface is wide, application mode is flexible. It is particularly suitable for distributed data control network under different industrial environment conditions.

Only the extended frame format of CAN message is used in iCAN protocol. It regulates 29 bytes ID and data part of CAN message. ICAN protocol identifier is allocated as shown in Table 1. Table 2 shows how the data part of frame is allocated [START_REF] Zhou | ICAN Field Bus Principle and Application[END_REF]. 1) Main interface module: Get into main interface firstly since the system starts running. Main interface gives three buttons, which are "Self-Check", "Set Parameters" and "Real-Time Display". Click one of these three buttons to get into the corresponding interface. 2) Self-check at the beginning interface module: After getting into self-check at the beginning interface, host computer will broadcast message to lower computers asking for their self-check status of the beginning. Meanwhile Timer 1 is activated. Host computer will do the process according to the information returned from lower computers: if the self-check state is normal, the edit control that represents this module will have a background color of green and show word of "Normal"; if the self-check state is fault, the background color of this edit control is red and word of "Fault" is showed. When Timer 1 reaches its timeout value and host computer still hasn't received self-check message from a lower computer, this module will be regarded as fault. The button relevant to this module showing words of "Error Information" will be able to be seen which is unseen before. Click this button, error code and information will be displayed. Controls of subsequent interfaces relative to this module are forbidden to use. If all of the self-check states of the five lower computer modules are normal finally, the "Self-Check" button on the main interface will be hidden. Otherwise it will keep showing for checking fault information in later time.

3) Parameter setting interface module: In this interface, operator can modify parameters about hydraulic (such as cooler inlet and outlet oil temperature) and loss (1000-grain weight and correction coefficient). After operator input new parameter values and click "OK", host computer will store them and send them to relevant lower computers. Or else, the previous values will be maintained. 4) Real-time display interface: After getting into real-time interface, host computer will broadcast message to lower computer modules for their realtime state data, including the module's current working status and relevant parameters' real-time values. Timer 2 is activated in the mean time. Since then host computer will broadcast to lower computers for real-time status data in certain intervals repeatedly. Host computer will show the parameter's real-time value in the relevant edit control after received it. If host computer receives a lower computer module's current working state, it will process it based on information received. Take Mechanical System Fault Monitoring Module as an example: if the current state is normal, then its edit control will have a green background color and show characters of "Mechanical System is normal"; if current working status is early warning, the edit control has a background color of yellow and shows characters of "Mechanical System is giving early warning"; the background color of the edit control will be red and characters of "Mechanical System is fault" will be displayed if current working state is fault. When a lower computer module's working state at present is fault, fault code and information can be seen by clicking the edit control that represents this module. Connect serial port 1 of the experiment box with CSM100, and CSM100 with lower computer measurement and control modules. Then the whole indoor experiment system of measurement and control integrated system for combine harvester is constructed, and the indoor communication experiment can be taken. Fig. 2 shows the devices used in the experiment.

Indoor Experiment

Application programs of this system are based on dialog, including the main application program class, serial port class, self-check at the beginning dialog class, hydraulic system parameter setting property page class etc. To avoid main thread being blocked, both reading serial port and writing serial port possess a single thread.

First, use UART debug assistant to make sure that each module of this system, including upper computer and all of the lower computers can respectively communicate correctly. Then do the experiment to test the whole system's performance. The result of the experiment is shown as followed.

Main interface, self-check at the beginning interface, parameter setting interface and real-time display interface are shown in Fig. 3, Fig. 4, Fig. 5, Fig. 6 and Fig. 7 respectively.

Main interface (shown in Fig. 3) is activated when system start running. Click "Self-Check" button to get into self-check at the beginning interface (shown in Fig. 4) to see self-check states of measurement and control modules and fault information when a module is in fault. Click "Real-Time Display" button to get into real-time display interface (shown in Fig. 7). Each lower computer module's current working state and relevant parameters' real-time values are showed here. Click its edit control to see fault information when a module's current working state is fault.

Conclusion

The experiment shows that this system can realize communication in real-time between host computer and lower computers. LCD on host computer shows working state and parameters of several objects of combine harvester correctly and properly. And the whole machine's performance is increased by shared information between modules. And due to CAN bus's characteristics of high reliability and real-time, the system's reliability is greatly improved. It has a very good application prospect.

Fig. 1 .

 1 Fig.1. System's general structure

Fig. 2 .

 2 Fig.2. Devices used in experiment

Fig. 3 .

 3 Fig.3. Main interface

Fig. 4 .

 4 Fig.4. Self-check at the beginning interfaceGet into parameter setting interface by clicking "Set Parameters" button. Working parameters of hydraulic and loss quantity can be modified here (shown in Fig.5and Fig.6respectively).

Fig. 5 .

 5 Fig.5. Hydraulic parameter setting interface

Fig. 6 .

 6 Fig.6. Loss parameter setting interface

Fig. 7 .

 7 Fig.7. Real-time display interface

3.1 ICAN Protocol

	control effectively. CAN bus is of extremely high reliability, high data transmission
	rate, long transmission distance and good real-time performance. It is especially
	suitable for computer control system in cars, interconnection of industrial field
	monitoring devices under circumstances of harsh temperature, strong electromagnetic
	radiation and large vibration
	CAN bus, developed in 1983 by Bosch Company of Germany for application in cars,
	is a serial communication network which can support distributed control and real-time

  The software of this system is designed by Embedded Visual C++, running under Windows CE circumstance. The host computer software is an important part of the whole measurement and control system. It is mainly used for realizing man-machine interface, receiving and displaying data, communicating with lower computers via CAN bus, setting parameters and showing working state of lower computers etc. Software is designed in the way of modularization. Main modules consisted in this software are as follows:

	3.2 System's Software Design
	Table1. ICAN message ID allocation
		ID28 ID27	ID26	ID20 ID19 ID18	ID12	ID11	ID7	RTR
	Frame			~ID21			~ID13		~ID8	~ID0
	ID	0	0	Source	0	0	Target	ACK FUNC	Source	0
				node			node		ID	ID
				ID			ID
	Table2. Allocation of data part of iCAN frame
		iCAN frame data byte			Function
		Byte 0					Segment flag
		Byte1(Length Flag, Error ID )
							Parameters relevant to FUNC ID
		Byte2~7
	Take cooler inlet oil temperature delivered from Hydraulic System Fault
	Monitoring Module to host computer as an example:
	1)	Source node ID: 0x03(Nod number of Hydraulic System Fault Monitoring
		Module)
	2)	Target node ID: 0x01(Nod number of host computer)
	3)	ACK: 1(Response frame, no need to be answered)
	4)	FUNC IN: 0x01(Write port continuously)
	5)	Source ID: 0xb4(Use this number to represent cooler inlet oil temperature)
	6)	DLC: 4(Data length)
	7)	Byte 0: 0(No Segmentation for data in this frame)
	8)	Byte1~Byte4: oil temperature

4 Experiment 4.1 Hardware Connection of Indoor Experiment ARM

  on the Embedded Experiment Development System OURS-PXA270-EP produced by Beijing Owles Electronic Technology Limited Company is taken as host computer in laboratory. PXA270-EP is a processor based on INTEL XSCALE PXA270, supporting Windows CE 5.0 operating system. There are LCD, serial ports and keyboard in the experiment box. So it can meet the demand of the experiment. Lower computers are Load Feedback Control Module, Real-time Loss Quantity Detecting Module, Electric System Self-checking Module, Mechanical System Fault Monitoring Module and Hydraulic System Fault Monitoring Module and Hydraulic System Fault Monitoring Module. CSM100, integrated with microprocessor, CAN-bus controller, CAN-bus transceiver, DC-DC module and high photoelectric isolation, is an embedded module used for converting from UART (serial port) to CAN. It is produced by Guangzhou Zhiyuan Electronic Limited Company. It can be embedded in device with UART port conveniently. Without changing the previous hardware structure, the device can get CAN-bus communication interface, realizing data communication between device with UART and CAN-bus network.

Acknowledgments. This work was supported by the Digestion Longitudinal Flow Intelligent Controlling for Rice and Wheat Harvesting Technology & Equipment

Research (2010AA101402) during the Eleventh Five-Year Plan Period.


Rui Guo

Fengmin Li

Wenying He

Sen Yang

Guojun Sun

Spatial and temporal variability of annual precipitation during 1958-2007 in Loess Plateau, China

Keywords: Trend analysis, Abrupt change;Mann-Kendall method, Sliding t-test method

The non-parametric Mann-Kendall method and Sliding t-test were applied in this study to analyze the spatial and temporal patterns of trends of the precipitation in the Loess Plateau from 1958 to 2007. The main trends of the precipitation in this region is downward, only four upward time series have not reach the significant level. Most of the abrupt changes happened in northeast of Loess Plateau during 1980s and 1990s. Furthermore, the abrupt changes happened in the northeast portion of the Loess Plateau were relatively later than those in the west portion of the Loess Plateau.

Introduction

Loess Plateau is located in arid and semi-arid area of China. It faces too many problems such as low vegetation cover, water scarcity and severe soil erosion and so on. Lots of the problems were affected by the precipitation. The precipitation is also one of the major factors limited agricultural and forestry productivity. Fierce human activities increase the possibility of abrupt changes in climate [START_REF] Alley R B | Abrupt clmate change[END_REF]. It is a meaningful work to find out the regulation of the precipitation in Loess Plateau.

Mann-Kendall trend test is a non-parametric, rand-based method for assessing the significance of trends in time-series data such as water quality, streamflow, *Corresponding Author. Email: sungj@lzu.edu.cn temperature, and precipitation [START_REF] Yue | Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series[END_REF]. Not only can examine the trends in time-series decline or increase, but also reflect the degree of changes, Mann-Kendall trend test could be a very good description of the characteristics of the time-series trend. Now, the Mann-Kendall trend test has been widely used and tested as an effective method to evaluate a presence of a statistically significant trend in hydrological and climatological time series [START_REF] Yue | Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series[END_REF][START_REF] Douglas | Trends in floods and low flows in the United States: impact of spatial correlation[END_REF][START_REF] Birsan | Streamflow trends in Switzerland[END_REF]. In the study, we use this method to test trends on precipitation in Loess Plateau from 1958 to 2007. The Sliding t-test [START_REF] Wei | The statistics and prediction of climate[END_REF] was used to detect the abrupt changes in the study.

The objectives of this study are [START_REF] Alley R B | Abrupt clmate change[END_REF] to reveal the spatial and temporal patterns of trends of the precipitation in the Loess Plateau; [START_REF] Yue | Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series[END_REF] to analyze the abrupt changes of the precipitation in the Loess Plateau from 1958 to 2007.

Study area and data processing 2.1 Loess Plateau

The Loess Plateau is a subsystem of mutually interlinked and feedback system of northwest Gobi desert, middle Loess plateau and east alluvial plain [START_REF] He Xiubin | Down-scale analysis for water scarcity in response to soil-water conservation on Loess Plateau of China[END_REF]. It is located in the middle reaches of the Yellow River and extends 7 latitudes (35-41°N) and 13 longitudes (102-114°E), with total area of more than 630,000km 2 and a population of 86 million. The plateau area is 1000-1600 m above sea level and surface is covered by an average 100-meter thickness of loess-paleosol layers. The Loess Plateau belongs to the continental monsoon region with annual average temperature of 4.3℃ in northwest and 14.3℃ in southeast. Annual precipitation ranges from 300mm in northwest to 700mm in southwest [START_REF] Jie We | Decoupling soil erosion and human activities on the Chinese Loess Plateau in the 20th century[END_REF]. There is much sunshine in the Loess Plateau.

The soil erosion rate on the Loess Plateau of China is among the highest in the world.

Loess Plateau contains arid and semi-arid regions; the eco-environment is fragile.

Precipitation data

The precipitation data were downloaded from China Meteorological Data Sharing Service System. The time series of annual precipitation records at 42 meteorological stations were used in this study from 1958 to 2007. The stations in the Loess Plateau were shown in Fig. 1.

Methods

Mann-Kendall method

The non-parametric Mann-Kendall method was used to assess the significance of a trend, and originated from [START_REF] Mann | Non-parametric test against trend[END_REF] [START_REF] Mann | Non-parametric test against trend[END_REF] and rephrased by [START_REF] Kendall | Rank Correlation Methods[END_REF] [START_REF] Kendall | Rank Correlation Methods[END_REF]. The test statistic is calculated as follows:

sgn( )

n n i j i j i S x x - = =+ = - ∑ ∑ . ( 1
)
Where n is the length of the data set, and x is the data point at time i and j, and

1 0 sgn( ) 0 0 1 0 for for for θ θ θ θ > ⎧ ⎪ = = ⎨ ⎪ - < ⎩ . ( 2
)
For independent, identically distributed random variables with on tied data values:

E(S) = 0 . (3) 2
( 1)(2 5) ( ) 18

n n n Var S σ - + = = . ( 4
)
When some data values are tied, the correction to Var(S) is:

1 ( 1)(2 5) ( )( 1)(2 5) ( )
18

n i i n n n t i i i Var S = - + - - + = ∑ . ( 5
)
Where t i is the number of ties of extent i. The standardized test statistic Z is computed by:

1 0 0 0 1 0 S for S Z for S S for S σ σ - ⎧ > ⎪ ⎪ = = ⎨ ⎪ + ⎪ < ⎩ . ( 6
)
The standardized MK statistic Z follows a standard normal distribution with mean of zero and variance of one under the null hypothesis of no trend. A positive Z value indicates an upward trend, whereas a negative one indicates a downward trend. The p value (probability value p) of the MK statistic S of sample data can be estimated using the normal cumulative distribution function:

p 0.5 ( ) Z φ = - . ( 7
)
where

2 2 0 1 ( ) 2 t Z Z e dt φ π - = ∫ . ( 8
)
If the p value is small enough, the trend is quite unlikely to be caused by random sampling. At the significance level of 0.05, if p ≤ 0.05, then the existing trend is assessed to be statistically significant [START_REF] Douglas | Trends in floods and low flows in the United States: impact of spatial correlation[END_REF][START_REF] Bouza-Deano | Trend study and assessment of surface water quality in the Ebro River (Spain)[END_REF].

Sliding t-test method

One moment is set to be a reference point in time series "x" with a sample size "n".

The two subsequences before and after the reference point were defined to be x 1 and

x 2 . 1 2 1 2 1 1 X X t s n n - = ⋅ - . ( 9
)
Where

2 2 1 1 2 2 1 2 2 n s n s s n n + = + - . ( 10
)
n 1 , n 2 --the sample size of x 1 and x 2, separately.

1 X , 2 X --the average of x 1 and x 2, separately. s 1 2 ,s 2 2 --the variance of x 1 and x 2, separately.

If |t i |<t α , there is no significant difference between the two subsequence. Otherwise, there is an abrupt change at the reference point [START_REF] Wei | The statistics and prediction of climate[END_REF].

Results and discussion

The spatial structure of the precipitation

The linear regression was used to detect the relationship between the average rainfall for this 50 years and longitude, latitude, elevation (Fig. 2). The results presented that the average precipitation was linear positively related to the longitude, but it is not significant. The average precipitation was not the linear relation with the latitude or the elevation. It was suggested that the spatial differentiation of the precipitation was not significant on the Loess Plateau from1958 to 2007.

Trend analysis and abrupt changes

The non-parametric Mann-Kendall method was used to assess the significance of the trend of precipitation. By this method the significant trends were detected for 42 time series of annual precipitation for different stations in the Loess Plateau (Fig. 3). There were only 7 among 42 stations showed a significant trend (p=0.05), the remaining 35 stations showed no significant trend. Moreover, all these 7 stations with significant trend (p=0.05) displayed a downward trend. The four stations with an upward trend showed in Fig. 3 were not assessed to be statistically significant. In other words, the standardized MK statistic Z of these four stations is positive, but did not reach significant levels. The standardized MK statistic Z of the remaining 31 stations with no significant trend is negative. It can be easily found from the distribution of the trends for the annual precipitation that no trend and upward trend were mainly in the western portion of Loess Plateau. The downward trend concentrated in the eastern portion of central Loess Plateau. The results also indicated that the precipitation possesses the longitude zonality. This result was similar as Q. Liu et al. [START_REF] Liu | Spatial and temporal variability of annual precipitation[END_REF]that studied the precipitation during 1961-2006 in Yellow River Basin. The precipitation changed obviously from west to northeast. This situation may be due to the monsoon, as the precipitation was sensitive to the climatic change. And the influenced by human activities might another reason.

The Sliding t-test method was used to test the abrupt changes. The Youyu station (112°27′E, 40°00′N) was selected as an example to demonstrate the abrupt change happening in the time series of annual precipitation (n 1 =n 2 =5). It revealed that the abrupt change occurred in 1987, 1992 and 2001 (Fig. 4). The t-test in 1987 reached the significant level (t i < -t 0.05 ) which revealed the abrupt change was from less to more.

In 1992, t i > t 0.05 , i.e. the abrupt change was from more to less. The situation in 2001 was similar to that in 1987. Because the choice of n 1 and n 2 are man-made, the results may drift. In order to avoid this situation, the n should be chose combined with specific needs, and constantly changes, so as to enhance the reliability of the results. The n 1 =n 2 =5, n 1 =n 2 =7,

Conclusions

In this study, the Mann-Kendall method and Sliding t-test were used to analyze the precipitation from 1958 to 2007 in Loess Plateau. The results presented as follows:

(1) The relationship between precipitation and longitude/latitude/elevation is not linear. The precipitation didn't have clear trend with the longitude/latitude/elevation increasing.

(2) The results of Mann-Kendall method indicated that only 7 stations showed a significant trend, the other 35 stations have no significant trend. Only 4 stations have positive Z values but didn't reach the 95% significant level.

(

Fig. 1 .

 1 Fig. 1. The location of the Loess Plateau in China. The locations of the Meteorological stations are shown by the black triangle.

Fig. 2

 2 Fig. 2 Relationship between the average rainfall and longitude, latitude, elevation in Loess Plateau, the linear models and R 2 also gave in the figure.

Fig. 3 .

 3 Fig. 3. The trends in annual precipitation during 1958-2007 detected by the Mann-Kendall method in Loess Plateau.

Fig. 4 .

 4 Fig. 4. The abrupt change tested by the Sliding t-test for annual precipitation series in Youyu station from 1962 to 2002 (n 1 =n 2 =5).

n 1 =n 2

 2 =10, n 1 =n 2 =11 and n 1 =n 2 =12 were chose in the study (Tab.1). By comparing, n 1 =n 2 =10 had been chose to analyze abrupt change. The time and frequency of the abrupt changes showed in Fig.5. There were 31 abrupt changes happening in 18 meteorological stations. Two abrupt changes took place in the fallowing 5 stations, decreasing change, 1986-decreasing change), Huanxian (1968-decreasing change, 1970-decreasing change), Suide (1995-decreasing change, 1996-decreasing change), Lishi (1967-decreasing change, 1990-decreasing change). Three abrupt changes took place in Xiji (1967-decreasing change, 1968-decreasing change, 1992-decreasing change). Four abrupt changes took place in the fallowing 2 stations, Jiexiu (1989, 1990, 1991 and 1992-decreasing change), Wutaishan (1988, 1989, 1992 and 1996-decreasing change). 12 stations among these 18 stations locate in the northeast of Loess Plateau. 20 times of the 31 abrupt changes occurred in the northeast. All of this indicated that the northeast of Loess Plateau was sensitive to the climate change. Moreover, the times of abrupt changes happened in different decades showed in Fig.6. It can be found from the chart that the times of abrupt change increase with the time except from 1971 to 1980. Only one station which located in the west of Loess Plateau happened abrupt change from 1971 to 1980. The abrupt change mainly happened in west of Loess Plateau from 1961 to 1970. However, during 1981-1990 and 1991-2000, the abrupt change was mainly happened in the northwest of Loess Plateau. That is the abrupt changes happened in the northeast portion of the Loess Plateau were relatively later than those in the west portion of the Loess Plateau. Moreover, there were 6 stations among 12 which locate in the northeast happened abrupt change in 1996. It indicated that the northeast of Loess Plateau became more and more sensitive to the climate change since 1980s. The reasons that affected the precipitation in the Loess Plateau need further study on the interaction between the climate change and human activities. The results in this study are significance for the further studies and forecast of climate in Loess Plateau, and also offer the basis for decision-makers.

Fig. 5 .

 5 Fig. 5. The time and frequency of the abrupt change detected by the Sliding t-test (n1=n2=10) in the meteorological stations of Loess Plateau. The year that the abrupt change happened has been simplified in two figures, e.g. , 67 for 1967; ** indicated p<0.01.

( 4 )

 4 ) The results of Sliding t-test showed that there were 31 abrupt changes happening in 18 meteorological stations. Most of the abrupt changes happened in the northeast of Loess Plateau. Moreover, the abrupt changes happened in the northeast concentrated during 1980s and 1990s. And the abrupt changes happened in the northeast portion of the Loess Plateau were relatively later than those in the west portion of the Loess Plateau. The northeast of Loess Plateau was sensitive to climate change recent years, as most of the abrupt changes occurred at here during 1980s and 1990s. More attention should be paid to this region, and the changes in land cover/use could be considered in further studies.

Table 1

 1 The time of the abrupt change detected by the Sliding t-test. (* p<0.05. ** p<0.01. The year that the abrupt change happened has been simplified in two The times of abrupt change detected by the Sliding t-test (n 1 =n 2 =10) in different decades.

	figures, e.g. , 67 for 1967)
	Stations			Sliding t-test			Stations			Sliding t-test
		n1=n2=5	n1=n2=7	n1=n2=10	n1=n2=11	n1=n2=12		n1=n2=5	n1=n2=7	n1=n2=10	n1=n2=11	n1=n2=12
	Youyu	87*,92*,01*					Jingyuan
	Datong			96*			Huanxian		68*	68*,70*	68*,69*,71*	69*,70*
	Hequ	87*					Jingtai	66*			91*
	Wutaishan	80*	92*	88*,89*,92*,96* 88*,89*,90*,92*,	87*,88**,89**,90	Weixian		92*	92*	92*,95*
					95*,96*	**,91*,92*,93*,9
						4*
	Wuzhai						Sanmenxia	80*,85*			85*,89*,90*
	Xingxian	78*,79*		96*			Changwu
	Yuanping			96*			Luochuan				85*
	Lishi	72**	64*,67*	67*,90*	90*	90*	Yan'an			90*	68*,69*,88*	69*,88*,89*,90*,
											94*
	Taiyuan			96*			Yushe		74*
	Yangquan					69*	Suide	70**		95*,96*	94*,95*,96*	88*,94*,95*
	Yushe						Hengshan			90*	88*
	Xixian	96*					Yulin
	Jiexiu	93*,94**	93**,94*,95*,00* 89*,90*,91*,92* 88*,89*,90*,91**	85*,88**,89*,90*	Guide	02*
					,93*	*,91*,92*,93*,94
						*
	Linfen				76*	77*	Minhe	79*	79*	79*,91*	79*,91*	78*,79**,91**
	Yangcheng			67*	68*		Xining				91*,92*,93*
	Lintao		79*	92*			Menyuan
	Huajialing			85*,86*	85*,86*,87*	84*,85**,86*,90*	Yanchi				69*,70*,71*,83*
	Kongtong	68*					Guyuan	68*		67*
	Linxia						Haiyuan
	Xifeng		00*				Tongxin
	Yuzhong						Xiji	68*	67*,68*,95*	67*,68**,92*	68*	69*,70*,94*,95*

Acknowledgments

This work is financial supported by National Key Basic Research Program (973) project (2007CB106804) and the Construction of an Information Platform/module in Eco-agricultural Assessment and Management (EAM) (2010DFA31450). The authors are grateful to the anonymous referees for their careful corrections and constructive comments on the manuscript. The authors thank the China Meteorological Data Sharing Service System for offering the precipitation data.

during 1961-2006 in Yellow River Basin, China. Journal of Hydrology. 361, 330-338 (2008)


Weihua Gan
email: ganweihua8421@yahoo.com.cn

Tingting Zhang

Yuwei Zhu
email: zhuyuwei3113@163.com

On Countermeasures of Promoting Agricultural Products' E-Commerce in China

Keywords: Agricultural products, electronic commerce (EC), Fuzzy Analytic Hierarchy Process (AHP)

China is a large agricultural country, yet the development environment of E-business in China is not mature. E-Commerce of agricultural products is a revolutionary change of traditional agricultural economy. This paper focuses on agricultural products' electronic commerce in China. Firstly, the paper describes the current situation of electronic commerce. Afterwards, the paper uses Fuzzy Analytic Hierarchy Process (AHP) to analyze the factors hindering agricultural products' electronic commerce in China. Finally, it puts forward the countermeasures to promote the development of agricultural products' e-commerce.

Introduction

Research of agricultural products in electronic commerce (EC) can be dated back to 1970s in the United States. Then much attention comes from Japan, Netherland, etc [START_REF] Hu | Analysis in EC Development of Agricultural Products in China[END_REF] . China is a large agricultural country, yet the development environment of E-business in China is not mature. E-Commerce of agricultural products is not a simple substitution of traditional distribution way. It is a revolutionary change of traditional agricultural economy since agricultural products in E-Commerce need the electronic commerce system through producing, processing and selling. Moreover, information infrastructure, supporting facilities and talents are the key factors influencing agricultural products' E-Commerce.

Background of Agricultural Products' E-Commerce in China

The application of internet technology gives agricultural circulation a new life. Contrary to the traditional face-to-face trading in the agricultural products market, agricultural products' E-Commerce can integrate all kinds of resources. In recent years, some agricultural e-commerce platforms are emerging in China, e.g. Shouguang county, Shandong province has set up E-Commerce service platform of vegetable future transaction, auction and exchange online. Shanghai Nongxin E-Commerce Co. Ltd. is operating a high-quality-agricultural-product ordering platform providing the door-to-door service for customer [START_REF] Shao | EC Summarize[END_REF] . Nowadays the ministry of agriculture tries to help more and more peasants to utilize such advanced technology, e.g. website of Chinese agricultural information net, -one-stop-trade‖ and -onlineexhibition-hall‖, to expand their market and increase their incomes.

Factors Influencing Agricultural Products' E-Commerce

There are many factors hindering e-commerce of agricultural products, but major factors is still on the debate [START_REF] Yang | Research of Electronic Trade Market of Supply Chain Management[END_REF] . With the development of network economy, Ecommerce gradually turns to be significant trading patterns in the modern society. In order to mature such pattern, effective measures should be taken as soon as possible. Furthermore, the key factors should be found. Then use Fuzzy Analytic Hierarchy Process (AHP) to determine the influence of factors related to agricultural ecommerce.

Introduction of Fuzzy AHP

AHP proposed by American professor T• L• Saaty in 1980s is an evaluation and decision making method with qualitative and quantitative analysis. Fuzzy AHP based on AHP and the fuzzy method comprehensively analyzes every element in the system to find the best scheme. By fuzzy AHP the values to a 0-1 range are used instead of 1-9 nine evaluation methods to establish decision matrix [START_REF] Sun | Research of Rural Economical Increasing[END_REF] . The basic process is as follows:

(1) Firstly, draw analytic hierarchy chart based on the decision object.

Objective level--decisions among several plans Criteria level--factors in decision making processing. Scheme level--several alternatives (2) Secondly, grade the scores. According to the importance of each factor in criteria level with decision-making requirement, the weight vector is obtained: W=(w l , w 2 , … w 5 ). W i ≥0, and

1 W 1 i    s i . If i W =0
, it means that failure to take the factor into consideration in some situation.

Then, evaluation system can be established to make a comparison among chosen plans with every factor in criteria level.

Scoring vector of factor i in several alternatives can be achieved by making points within﹛0, 1﹜.

 

. i P as Row i of a matrix to get an s × n matrix P and weight vector W as a 1 × s matrix. The scoring vector and weight vector can be called fuzzy sets in Fuzzy Mathematics, matrix is called Fuzzy Matrix.

(3) Thirdly, calculate the product of W and P to matrix multiplication. The result is a 1×n matrix A.

n i P w P w P w P w ki s k k si s i i i ,... 2 , 1 , ... a 2 2 1 1        . i
a is the final evaluation of Scheme i under the impact of factors S.

(4) Finally, carry out a strategic choice to the vector in the matrix A.

Main Factors Affecting Agriculture Products' E-Commerce

Figure 1 shows the structure of factors affecting agriculture products' E-Commerce.

The first layer-objective level is the target, i.e. the result for such analysis. The second layer-criteria level, lists some main factors by the method of Delphi, they are transaction subject, object and platform, environment. And the third layer-scheme level, respectively presents each main factor in detail, e.g. for the transaction subject, subject cognition and information quality are considered, while for the transaction object, standard, brand and industrial and commercial level are considered, and for the transaction platform, information infrastructure and classification and normalization are considered, and for social environment, logistics level, transaction security, electronic payment, social credibility are considered. Afterwards, determine the weight of these factors in layer B accounting for layer A, then calculate the weight of factors in layer C accounting for layer B. Finally, obtain the affecting scores of each factor in layer C accounting for layer A. Determine each factor's importance from 0 to 1 by comparison. The bigger the value is, the more important. Table 1 shows the fuzzy decision matrix [START_REF] Yang | Technology and Application of EC[END_REF] . [0.7-0.9) Compared with each other, one element is greatly important than the other.

[0.9-1] Compared with each other, one element is critically important than the other.

From the above, there are four elements in layer B: B1= Transaction Subject B2= Transaction Object B3= Transaction Platform B4= Social Environment A decision matrix can be achieved according to the method of Delphi (see Table 2).

   4 1 D j ij B , ) ( 4 1 4 1      j i ij B F , 且(i≠j).
∴ the weight of every element in layer B is w i =D/F. B i W = (0.325, 0.275, 0.217, 0.183).

In the same way, there are 13 elements in layer C. C1= 3, Table 4, Table 5 and Table 6 show the weights of element in layer C accounting for the element in layer B:

∵ B i W (i= l, 2, 3, 4) and C k W (k=1, 2, 3,…13).
∴ The influence of each factor on electronic commerce can be found. The following conclusions are drawn:

A C W = (0.16575, 0.15925, 0.06985, 0.0682, 0.06545, 0.0715, 0.083979, 0.05642, 0.072261, 0.59658, 0.041175, 0.040443, 0.041724).

It is obvious that 0.596589 (C10) is the maximum, then 0.16575 (C1) and 0.15925 (C2), while 0.040443 (C12) is the minimum, following is 0.041175 (C11) and 0.041724 (C13).

Therefore, the most important factors to the development of agricultural products' e-commerce are logistics development level, subject of cognition, information quality of peasants. On the contrary, electronic payment, transaction security and social credibility make the slightest effect on agricultural e-commerce.

4

Countermeasures for the Development of Agricultural EC

Set up Effective Agriculture Product Logistics System

Owing that agricultural products varied from types and volumes, much difficulty will come up in the logistic activities, i.e. fresh-keeping, transportation, follow-up treatment [START_REF] Li | Basis of Electronic Commerce[END_REF] . Nowadays weakness of agricultural products distribution system is one of the bottlenecks in the development process of agricultural products' E-commerce in China. It is necessary to set up refrigerated-warehouses, distribution centers, etc. to accelerate the speed from the countryside to city. On the other hand, tracking and tracing system through GPS and GSI should also be introduced to help buyers and sellers monitor the whole process via network to improve the safety, reliability, accuracy of agricultural products.

Improve Rural Information Infrastructure

Although internet is available everywhere in the city, the users of internet in the countryside are so less [START_REF] Zhao | Some Thoughts about the Development of Agricultural E-Commerce in China[END_REF] . Such unbalanced situation becomes the second obstacle for agricultural EC. It is necessary to improve rural information infrastructure in China in order to reduce the cost of gaining information for the peasants.

Implement Standardization and Brands

After accession to WTO, our exports of agricultural products often reject. No wonder that consumers prefer safety and famous agricultural products. It is eager for agricultural products to implement standardization and brands. Ministry of agriculture in China has carried out some standards about agricultural products [START_REF] Timmesr | Business Models of Electronic Markets[END_REF] . In addition, our agricultural products should have our own brands and register trademarks all over the world. It is useful to depend on the intellectual property rights to protect our own brands and expand our markets. If national agricultural product becomes the famous brand in the world then the buyers abroad can easily click the button -Search‖ and catch the related information.

Cultivate Talents in Agricultural Products' EC

Another key element for the success of agricultural products' EC is talents in this field. If millions of peasants can understand the principles of EC and are in their positions to trade via internet, agricultural products' EC will realize. Firstly, a long-term education plan about science and technology should be made in the countryside [START_REF] Kraemer | Strategic Use of Internet and E-commerce: Cisco System[END_REF] . Secondly, knowledge related to information technology and electronic commerce should be taught for the peasants. Another way is preferential policies guiding EC professional talents in the colleges to service for countryside.

Promote Industrial Management of Agriculture

The current main body in agricultural products' EC is agricultural operation enterprises since the firms could take use of their own advantages on capital and technology to improve the value of agricultural products and speed up the standardization of local agricultural production, enhance the competitiveness. Industrialized operation of agriculture is not only useful to open both international and domestic market of our agricultural products, but also can accelerate the development of rural social economy. Therefore, more and more agricultural products operating and processing enterprises should be encouraged to open to strengthen the competitiveness and raise agricultural labor employment.

Specialize E-Commerce Websites

The EC website is the network platform to develop EC, there are more than 6000 websites related to agriculture in the countryside in China [START_REF] Henderson | Internet and E-Commerce Adoption by Agricultural Input firms[END_REF] . However, agricultural websites in western China are fewer than in other regions. A number of websites focus on information integrating function, the payment function of website is not perfect. At present, the EC domestic websites of agricultural products are far from professional and colorful. As a result, more agricultural enterprises and intermediary organizations should establish commercial websites. And these websites should be estimated regularly by the public and government.

Conclusions

Depending on the basic industry of national economy, agriculture has a bearing on the healthy and stable development for economic and society. Along with optimized structure of agricultural production and improved level of e-commerce, technology innovation could accelerate modernization and industrialization of agriculture, and provide more comprehensive prospects for agricultural e-commerce. A new generation agricultural e-commerce platform comes to the top. With the development of network economy, e-commerce gradually will turn to be significant trading patterns in the future.

Fig.

  Fig. 1. Factors Hindering Agricultural Product's E-Commerce Development

4 CW

 4 B =(0.326, 0.225, 0.221, 0.225)

Table 1 .

 1 Value and Signification

	Value	Signification
	[0-0.1)	Compared with each other, one element is obviously less important than the other.
	[0.1-0.3)	Compared with each other, one element is less important than the other.
	[0.3-0.5)	Compared with each other, one element is slight unimportant than the other.
	0.5	Compared with each other, elements both have the equivalent importance.
	(0.5-0.7)	Compared with each other, one element is more important than the other.

Table 2 .

 2 Importance for elements of layer B

	∵

Table 3 .

 3 Importance for elements of group CB1

Table 4 .

 4 Importance for elements of group CB2

		C3	C4	C5	C6
	C3	-	0.56	0.54	0.42
	C4	0.44	-	0.52	0.53
	C5	0.46	0.48	-	0.49
	C6	0.58	0.47	0.51	-

Table 5 .

 5 Importance for elements of group C B3

		C7	C8	C9
	C7	-	0.60	0.56
	C8	0.40	-	0.44
	C9	0.44	0.56	-

Table 6 .

 6 Importance for elements of group C B4

		C10	C11	C12	C13
	C10	-	0.70	0.65	0.60
	C11	0.30	-	0.60	0.45
	C12	0.35	0.40	-	0.58
	C13	0.40	0.55	0.42	-

Acknowledgments

Thanks for the support from national science fund: On Relation Between Mode of Pig Green Supply Chain and Performance around the Poyang Lake(70962002)


Hongxin Cao
email: caohongxin@hotmail.com

Chunlei Zhang

Baojun Zhang

Suolao Zhao

Daokuo Ge

Baoqing Wang

Chuanbao Zhu

David B Hannaway

Dawei Zhu

Juanuan Zhu

Jinying Sun

Yan Liu

Yongxia Liu

Xiufang Wei

Research and application of cultivation-simulation-optimization decision making system for rapeseed (Brassica napus L.)

Keywords: Rapeseed, Cultivation, Growth Models, Optimization Models, Decision Making System, Research and Application

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Research and Application of Cultivation-Simulation-Optimization Decision Making

System for Rapeseed (Brassica Napus L.)

Hongxin

INTRODUCTION

Rapeseed is one of four main oil crops in the world, whose plant area is about 14.1million ha in general. In the same time, it is also main oil crops in China, and its plant area is 4.0-5.0 million ha.

The crop simulation and the 3S (GPS, GIS, and RS) technology are the core and foundation of digital cultivation technique systems (Cao et al., 2005), and the crop simulation technology mainly included the crop growth models, the optimum models in cultivation, and the simulation-optimization-decision making. In that the optimum models in rapeseed cultivation were mainly the models of the optimum season, population dynamic, seed rate, fertilizer rate, and soil water etc., which set a basis and goals for the simulation-optimization-decision making in rapeseed, and had an important significant in promoting digital cultivation and realizing objective dynamic quantitative and optimum management.

In the world, the research of rapeseed models can be divided into two stages obviously, i.e. the empirical models as main (70-80's of 20 century), and the (partially) mechanism models as main (since 90's of 20 century). In the later stage, the rapeseed growth and development and ecological system models such as EPR95 [START_REF] Kiniry | EPIC model parameters for cereal, oilseed, and forage crop in the north Great Plain region[END_REF], DAR95 (Peterson et al.,1995), LINTUL-BRASNAP (Habekotté , 1997), CERES-rape [START_REF] Gabrielle | Development and evaluation of a CERES-type model for winter oilseed rape[END_REF][START_REF] Gabrielle | CERES-Rape, a model for the production and environmental impact of rapeseed[END_REF], APSIM-Canola (Robertson et al.,1997),

and CECOL [START_REF] Husson | Evaluation of CECOL, a model of winter rape (Brassica napus L.)[END_REF] etc. were developed, and they can simulate rapeseed growth and development in real time. However, in China, the research of rapeseed simulation model were not more. Liao et al. (2002aLiao et al. ( , 2002b) studied characteristics of dry matter accumulation, distribution, and transfer of winter rapeseed, and developed the knowledge-based expert system for rapeseed production. [START_REF] Liu | The simulation models of rape development dynamic[END_REF]Liu et al. ( , 2004) set up simulation model of rapeseed phenophase etc. [START_REF] Zhu | Dynamic knowledge model and decision support system for rapeseed cultivation management[END_REF][START_REF] Zhu | A dynamic knowledge model for fertilization management in rapeseed[END_REF][START_REF] Zhu | A dynamic knowledge model for designing suitable scheme of rapeseed sowing and transplanting[END_REF] studied dynamic knowledge model and decision support system for rapeseed cultivation. [START_REF] Cao | Researches of simulation models of rape (Brassica napus L.) growth and development[END_REF][START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF] carried out the studies of simulation models of phenophase, leaf age, dry matter, leaf area, and ramification numbers. [START_REF] Tang | Development of growth model-based decision support system for rapeseed management[END_REF]Tang et al. ( , 2007aTang et al. ( , 2007b) ) studied dynamic simulation on photosynthesis and dry matter accumulation, shoot dry matter partitioning, and yield formation of rapeseed, and developed a growth model-based decision support system for rapeseed management.

MATERIALS AND METHEODS

Methods

Referring to references

Its aims were to apprehend and master the rapeseed growth and development rules and principles of growth regulation and control for rapeseed whole, and obtain initial parameters on varieties, soil, and climates etc. by referring to the references in papers and books on rapeseed physiology, ecology, and cultivation etc.

Development of Rape-SCODS

The Rape-SCODS were established through combining the rapeseed growth models, the optimization models for rapeseed cultivation, and expert knowledge for plant diseases and insect pest of rapeseed using Visual Basic ver. 6.0 and VFP ver. 6.0.

Validation of Rape-SCODS

The scheme get by this systems, Rape-SCODS, were compared with rapeseed production in the same weather, soil, cultivars.

Experiments

In order to decide the parameters of the models and verify the models, the field experiments with different rapeseed cultivar types in multi-sites and multi-years were carried out.

Experiment 1: Two rapeseed cultivars, V1 and V2, were grown in the field from 2002 to 2005 on Red loam soil near Wuhan (soil test in pre-planting results indicated the following: total nitrogen, 1.2 g kg -1 ; organic matter, 20.7 g kg -1 ; pH, 7.79; and volume weight, 1.50g/cm 3 , 30°05′ N), Hubei Province.

The six planting dates were 15 September, 22 September, 29 September, 6 October, 13 October, and 20

October, respectively. The experiment included 12 treatments, 3 replications, and 36 subplots arranged random with 8.00-by 2.50-m. Fertilizing and other field managements in subplots were the same.

Fertilizer contained 180 kg N ha -1 , 120 kg P 2 O 5 ha -1 , 180 kg K 2 O ha -1 , and 15 kg boron ha -1 .

Experiment 2: The rapeseed cultivar, V3, was grown in the field from 2006 to 2007 on Yellow umber soil with higher fertility in pre-planting in soil in Nanjing (32°03′ N), Jiangsu Province. 2 treatments (direct seeding, and transplant), 3 replications, and 6 subplots arranged random with 3.00-by 2.50-m were set to the experiments. Fertilizing and other field managements in plots were the same.

Experiment 3: The rapeseed cultivars, V3 and V4, were grown in the field from 2007 to 2008 on Yellow umber soil with higher fertility in pre-planting in soil in Nanjing (32°03′ N), Jiangsu Province.

The experiment included 2 cultivars and 2 nitrogen levels (Fertilizer: 0.018kg N•m -2 ; 0.012kg P 2 O 5 •m -2 ; 0.018kg K 2 O•m -2 ; and 0.0015kg borax•m -2 ; CK: no fertilizer), 4 treatments, 3 replications, and 12 subplots arranged random with 40.0-cm row spacing, 17-20 cm plant spacing in 7.00-by 4.30-m area.

Fertilizing and other field managements in plots were the same.

The phenophase, LAI, the total shoot numbers, dry matter, leaf number, leaf photosynthesis, plant characters, and meteorological and soil data etc. were observed during rapeseed growth or after harvest.

CULTIVTION-SIMULATION-OPTIMIZATION DECISION MAKING SYSTEM FOR RAPESEED (Rape-CSODS)

Structure and Functions of Rape-CSODS

The system was developed based on the principles on eco-physiology for rapeseed, balance of soil nutrition and moisture, agricultural system science and agricultural meteorology, the optimization of rapeseed cultivation, and so on, and included rapeseed growth and optimization models, databases, system platform, and function (Fig. 1). In that there were the simulation models for growth and development in rapeseed (see APPENDIX below), the optimization models of rapeseed cultivation (see APPENDIX below), and generating models for weather data (see APPENDIX below), there were mainly the parameters for weather, soil, and cultivars, and expert knowledge for managements for nutrition and water in soil and controlling for diseases, insect pests, and weeds in rapeseed in databases, there were the interface for decision making, databases, 4 sub-systems with parameter adjusting, decision making in normal year, decision making in current year, and analyzing for cultivar suitability in the system platform, and there were mainly determining the model parameter, the optimization cases and for rapeseed cultivation in normal year, the revising scheme for rapeseed cultivation in current year, suggestion for suitable plant areas for rapeseed cultivars, printing sheet for decision making, and so on.

The interface for decision making was developed using Visual Basic ver.6.0, which included main menu and first level sub-menu with linking every sub-systems (Fig. 2 to Fig. 5) with independent main interfaces and main menu, the background databases were designed using VFP ver. 6.0, and the output of the decision making results can link with Excel.1998-2007.

Running Environment of System

Hardware

The system required personal computer with the model Pentium IV or above, disk driver, and laser or stylus printer.

Software

The system required WIN98 or above operation system, Visual Basic ver. 6.0, VFP ver. 6.0 or above, and Excel.1997 or above.

APPLICATION OF SYSTEM

Basic Situations of Site

Rape-CSODS was applied in Liaocheng of Shandong province in 2006-2007, and the demonstration area was at 7.73 ha located in Houlou Village, Jiazhai, Chiping County with planting pattern of direct seeding between rows of pre-crop corn.

The optimization scheme in normal year were gained by Rape-CSODS using the meteorological data in normal year in Liaocheng (Table 1), the average soil nutrition data in the demonstration area (soil texture, sandy loam; soil organic matter, 9.5 g• kg -1 ; total nitrogen, 0.83 g• kg -1 ; P 2 O 5 , 18.5 mg• kg -1 ; K 2 O, 102 mg• kg -1 ), and the cultivar, "Qinyou 2" through combining the scheme with the local expert knowledge on Rapeseed. The managements of the demonstration area were carried in accordance with the optimization scheme from Rape-CSODS, and the control rapeseed field was used in the conventional managements under the same conditions such as soil, weather, and cultivar.

Comparison of results

The Seedling situations in rapeseed before over-winter

The various indices of seedling status in rapeseed before over-winter in demonstration area were larger than that of the control, e.g. the green leaf numbers per plant, leaf area per plant, and dry matter per plant increased 1.1, 221 cm 2 , and 1.5g, respectively (Table 2).

The yield and yield components

Under the same conditions, 1000-grain weight and yield in demonstration area was larger than that of the control, e.g. the 1000-grain weight increased 0.2g, and the yield raised 676.5kg ha -1 , respectively (Table 3).

DISCUSSION AND CONCLUSIONS

Disscussion

At present, the development direction of rapeseed production is the high yield, good quality and end-uses, therefore, it is a basic guarantee of health development of rapeseed production to realize regionalization of distribution, large regional precision planting, standardization of production, and informationalization of managements.

Regionalization of distribution

Analysis of cultivar adaptability can be made by Rape-CSODS, which will help to know possible proper plant regions of cultivars, and develop potential extensive values for it. Of course, in order to fulfill this purpose, it is need to integrate with GIS.

Large scale precision planting

In the large scale planting, it is need to decide corresponding plant sheets under different weather, soil, and cultivar conditions, and the purposes can be realize using Rape-CSODS quickly expediently easily. In addition, the decision making with precision and digital can be done by Rape-CSODS, which will also help to realize precision planting, and it is a precision planting technology with low-cost.

Standardization of production

The cultivation practices with standardization can be integrated in pre-sowing plant sheet, which is a benefit references for planter. As long as you done according to it, standardization of production can be fulfilled.

Informationalization of managements

Informationalization of crop managements is one of crucial contents for modern agriculture. The Rape-CRSODS and its application in rapeseed production set a basis for informationalization management of rapeseed production. With wide use of internet, informationalization management can be more feasible in agriculture, therefore, it is need to be integrated with internet.

Conclusions

Rape-CSODS contained simulation models for growth and development in rapeseed, the optimization models of rapeseed cultivation, and expert knowledge databases for managements for nutrition and water in soil and controlling for diseases, insect pests, and weeds in rapeseed, in that the rapeseed growth models mainly included phenophase, leaf age, population dynamic, and organ forming etc., and the optimization models for rapeseed cultivation mainly included the optimum biomass, the optimum LAI, the optimum shoot and ramification number dynamics models, and the optimum decision making models for nitrogen and water etc. The Rape-CSODS was a system with general adaptability, clear yield aim, better mechanism, and dynamic characteristic.

The system has been applied in Shandong Province in this study, and the results showed that the scheme get by Rape-CSODS can increase green leaf number before over-winter, 1.1; 1000-weight, 0.2g; and rapeseed yield, 676.5kg ha -1 at mature, comparing with the control in the same conditions.

The Rape-CSODS was a result of applying crop simulation technology in rapeseed cultivation, managements, and informational management system for rapeseed cultivation, which overcome lag and non-dynamic disadvantages of results from 2-3 year field experiment data on cultivation conventionally, and had a huge market and wide application foreground. "Rice Clock Models" [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of simulation models of rape (Brassica napus L.) growth and development[END_REF][START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF].

dP j /dt = 1/D Sj = e kj • (T ebj ) pj • (T euj ) qj • (P ej ) Gj • f(E Ci )
T ebj = (T i -T bj ) / (T oj -T bj ), when T i <T bj , T i = T bj ; when T i >T oj , T i =T oj T euj = (T uj -T i ) / (T uj -T oj ), when T i >T uj , T i = T uj P ej = (P i -P bj ) / (P oj -P bj ), when P i <P bj ,P i = P bj ; when P i >P oj , P i =P oj where dP j /dt is the development rate at the j th stages, D Sj is the days at the j th stages, T ebj and T euj are the effective factors for temperature, respectively, pj and qj are the genotypic coefficient of temperature effects, P ej is the effective factor of photoperiod, Gj is the genotypic coefficient of photoperiod effects, and f(E Ci ) is the effective function of agronomic practice factors for rapeseed, T i is the daily mean temperature (℃) in the j th stage, T bj 、T oj and T uj are lower, optimum, and upper limit temperature (℃) demanded in the j th stage for rapeseed, respectively, and P bj 、P oj are the critical and optimum day length (h) demanded in j th stage for rapeseed, respectively.

Ve rnalization models can be described as following through employing ideals of "wheat clock models":

dV/dt = 1 / D s2 = e k2 • (V e ) C
If a cultivar was winter or semi-winter rapeseed, the expression of V e was:

However, if it was spring rapeseed, the expression of V e was:

where K2 and C are the parameters of vernalization, V e is the factor of rapeseed vernalization effect, V ti is the daily mean temperature in vernalization phase. It will finish vernalization phase when V e equal to some extent accumulation days; the vernalization days of the winter rapeseed were 30 to 40 days, the semi-winter rapeseed with 20 to 30 days, and the spring rapeseed with 15 to 20 days.

Leaf age

The growth rate of rapeseed leaf were different in different varieties, development stages, temperature, and nutrition conditions etc., when nutrition condition was optimum, the models of rapeseed leaf age were [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of simulation models of rape (Brassica napus L.) growth and development[END_REF][START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

dL j /dt = f (L j ) = 1/D Lj = D Loj • (T t /T o ) La/Lb D Loj = e LK
where dL j /dt is the development rate of the j th leaf age, f (L j ) is the basic development function, D Lj is the development days demanded from emergence to the j th leaf age, D Loj is the development days demanded from emergence to the j th leaf age under the optimum conditions, T t and T o are the daily mean temperature(℃)of the t th day, and the optimum temperature for rapeseed leaf age development, respectively, and La、Lb、LK are the parameters of leaf models, respectively.

Dry matter

It was results of accumulation and partitioning in various rapeseed organ of assimilate of photosynthesis, the accumulation of dry matter in whole rapeseed growth season was a process with "S-curve" changes. Therefore, simulation models of rapeseed dry matter dynamic were [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of simulation models of rape (Brassica napus L.) growth and development[END_REF][START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

W(t)= W(t-1) +△W(t)
where W(t) and W(t-1) are the dry matter (g) of population in the t th , and the (t-1) th day after emergence, respectively, △W(t) is the net increment (g) of dry matter of population in the t th day after emergence,

P CG is the canopy photosynthesis rate of rapeseed (g co 2 •m 2 •h -1 ), R is the respiration rate (g co 2 •m 2 •h -1 ),
β is the coefficient transforming carbohydrate into dry matter in rapeseed, 0.2 and 0.4 are ratio of photosynthesis of green stem and pod in whole photosynthesis in the same periods.

LAI

In terms of principles of dry matter partitioning for rapeseed, the simulation models of LAI before early anthesis were [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of simulation models of rape (Brassica napus L.) growth and development[END_REF][START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF] :

L AI (t)=L AI (t-1)+ΔL AI (t) ΔL AI (t)= L AII (t)-L AID (t) L AII (t)=C P (t) •ΔW(t) •R LA (t)
where L AI (t) and L AI (t-1) are LAI in the t th , and the (t-1) th day after emergence, respectively, ΔL AI (t) is the daily net increment of LAI in the t th day after emergence, L AII (t) and L AID (t) are the total daily increment and decrement in the t th day after emergence, respectively, C P (t), ΔW(t), and R LA (t) were partitioning coefficient of dry matter in leaf, daily accumulation rate of dry matter in plant (g﹒m -2 ﹒ d -1 ), and ratio of leaf area to dry matter (m 2 ﹒g -1 ), respectively.

It's decreasing after early anthesis was a function of development index:

L AI (t)= (t) D d1 VI 2 AIX 1 L   D VI (t)=   t 1 i DPDi/No
where L AIX is the largest LAI of rapeseed in whole growth period, D VI (t) is the development index in t th day after anthesis with ranges between 0 and 1, No is development physiological days from early anthesis to mature, and d1 is a parameter of models.

Total shoot and ramification numbers

The increasing models of shoot and ramification numbers from elongation to early anthesis were [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

T ILN (t) = T ILN (t-1) + △ T ILN (t) △ T ILN (t) = K TLNI (t) T ILN (t-1) D PD (t) P TILN (t)

P TILN (t) = 1-T ILN (t-1) / T ILNM K TLNI (t) = K TLNO •T LNF (t)
The decreasing models of shoot and ramification numbers from early anthesis to mature were:

T ILN (t) = T ILN (t-1) -△ T ILN (t) △ T ILN (t) = K TLNI (t) • T ILN (t-1) • D PD (t) • P TILN (t) P TILN (t) = 1-T ILN (t-1) / T ILNM K TLNI (t) = K TLNO • T LNF (t) where T LNF (t) is the effective factor of the total leaf numbers in the t th day after emergence, T ILN (t) and △ T ILN (t) are the total shoot and ramification numbers, and its daily increment (number m -2 ) in the t th day after emergence, respectively, T ILN (t-1) is an initial value of the total shoot and ramification numbers in the (t-1) th day after emergence(number m -2 ), T ILNM is the largest numbers of total shoot numbers (number m -2 ), P TILN (t) is the ratio of total shoot and ramification numbers in the t th day after emergence to T ILNM , D PD (t) was the development physiological days in the t th day after emergence, and K TLNI (t) is increasing coefficient of shoot and ramification numbers in the t th day after emergence, which was decided by nitrogen, light, temperature, and soil moisture etc.

Optimization models in rapeseed

Optimum season

There was an overyear in growth and development of winter rapeseed, so an important basis of deciding the optimum planting date was to get the happy seedling under diffrent varieties and cropping systems. The green leaf numbers per plant pre-overyear can be as the happy seedling criteria based on studies of Institute of Oil Crops Research, CAAS. At first, the optimum planting date was calculated by leaf age models above, then, the other optimum phenology were calculated by phenophase models above [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF].

Optimum leaf age

The models of optimum leaf age of rapeseed were expressed as below under the optimum nutrition conditions, and the leaf development affected only by temperature [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

dL oj /dt = f (L oj ) f (L oj ) = 1/ D Loj = D Loj • (T t /T o )

Optimum LAI

The optimum LAI in different phenophase (L AIO ) of rapeseed can be expressed as follows [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

L AIO = -(1/E j )ln(B • I b / I 0 )
where I 0 is the horizontal nature light intensity above the population in the j th phenology in the local, which equals to product of daily mean solar radiation by coefficient (C), (B• I b )is the light intensity of lower part of population in the j th phenology, E j is the extinction coefficient of population in the j th phenology, I b is the compensating light intensity of lower part of population in the j th phenophase.

Optimum dry matter

The optimum dry matter at different phenology and yielding levels equals to dry matter in the optimum LAI pre-elongation, that in the optimum LAI, and green stem area from elongation to pre-anthesis, and that in the optimum LAI, green stem area, and PAI, and its basic models can be expressed as follows [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

W o (t)= W o (t-1) +△W o (t)
where W o (t) and W o (t-1) are the optimum dry matter of rapeseed population in the t th day, and (t-1) th day after emergence (g), respectively, △W o (t) is the net increment of the optimum dry matter of rapeseed population in the t th day after emergence (g), P CGO is the optimum canopy photosynthesis rate (g co 2 •m 2 •h -1 ), 0.2 and 0.4 are ratio of photosynthesis of green stem and pod in whole photosynthesis in the same periods, and the other signs are the same above.

Optimum shoot and ramification numbers

The models of the optimum shoot and ramification numbers of rapeseed population (N FO ) (10 4 ×ha -1 ) can be expressed as follows [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

B SO = N MF / N SV N SV = L N / 4 + k + 1 N FO = N MF + N S •B SO
where B SO is the optimum plant numbers (10 4 ×ha -1 ), N MF is the optimum main stem and primary shoot numbers (10 4 ×ha -1 ), relying on yielding levels, N SV is the valid shoot and ramification numbers per pant, and N F and N S are the optimum primary, and secondary ramification numbers per plant, respectively.

Optimum decision making of ecological and balance sheet for fertilizing

Based on the principles for balance sheet for fertilizing, the nutrition of fertilizer in rapeseed can be calculated as follows [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF]:

F NQ = (F AQ -F SQ )/E F
where F AQ is the nutrition rate demanded in whole rapeseed growth, F SQ is the nutrition rate supplied by soil, the effects of the ecological factors such as the soil nutrition contents, soil organic matter, pH, and soil temperature etc on F SQ can be considered, and E F is the use efficiency of fertilizer in the rapeseed growth season.

Optimum decision making of soil moisture

Based on the principles for soil moisture balance, the models of profit and loss of soil water in rapeseed growth season can be expressed as follows [START_REF] Gao | Rice Cultivational Simulation-optimization-decision Making System[END_REF]Cao et al., 2005[START_REF] Cao | Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.)[END_REF][START_REF] Cao Hong-Xin | Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.)[END_REF] where D SW (t) is profit and loss of soil water in the t th day (mm), I SW (t) and O SW (t) are capture, and loss of soil water in the t th day (mm) in current or normal year, respectively, E TA (t)、R U (t) and C(t) are actual evaporation-transpiration, runoff, and intercept in the t th day(mm), respectively, W 0 (t) is initial soil moisture in the t th day (mm), and P(t) is rainfall in the t th day(mm).

Generating models for weather data

According to Gao et al. (1992a), the daily day length, DL j , and daily radiation, QD j , can be calculated below. where SL is solar latitude, j is day number, LT is local latitude, ω is solar constant (takingω = 6.28 / 365), and ST[j] is sun time on j th day.

  Cao, Chunlei Zhang, Baojun Zhang, Suolao Zhao, Daokuo Ge, Baoqing Wang, Chuanbao Zhu, David B. Hannaway, Dawei Zhu, Juanuan Zhu, et al. To cite this version: Hongxin Cao, Chunlei Zhang, Baojun Zhang, Suolao Zhao, Daokuo Ge, et al.. Research and Application of Cultivation-Simulation-Optimization Decision Making System for Rapeseed (Brassica Napus L.). 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.441-456, 10.1007/978-3-642-18336-2_54. hal-01562807

Fig. 2 .

 2 Fig. 2. The system main interface Fig. 3. Browse of meteorological data in local

Fig. 1 .

 1 Fig. 1. Chart of the structure and functions of Rape-CSODS

  SL = 23.5 ×sin (6.2832× (284 + j) / 365) D6 = sin ((45 -(LT -SL -0.565) / 2) ×3.1416 / 180) D7 = sin ((45 + (LT -SL + 0.565) / 2) ×3.1416 / 180) D8 = cos (LT ×3.1416 / 180) × cos (SL ×3.1416 / 180) C1 = 3.1416 / (1 + (3.191 × cos (ω × j) + 0.695 × sin(ω × j)) / 100) C2 = sin (LT × 3.1416 / 180) × sin (SL ×3.1416 / 180) × D10× (3.1416 / 180) C3 = D8 ×sin (D10 × 3.1416 / 180) QAX = (1.94 × 1440) / C1 × (C2 + C3) QD j = QAX × (0.146 + 0.559× (ST[j]/10) / DL j )/23.8846

Table 1 Meteorological data in the normal year in Liaocheng

 1

	Months	Monthly mean temperature (℃)	Monthly mean maximum temperature (℃)	Monthly mean minimum temperature (℃)	Monthly sun time (h)	Monthly rainfall (mm)	Monthly rainy days (D)
	1	-2.0	3.4	-6.6	162.0	4.3	2.5
	2	0.9	6.1	-4.2	161.0	7.1	2.8
	3	7.1	13.1	1.6	198.0	14.6	4.3
	4	14.6	20.5	8.5	231.0	23.8	5.7
	5	20.0	26.7	14.4	263.0	45.8	5.4
	6	25.2	31.7	19.5	246.0	69.3	7.8
	7	26.7	31.4	22.5	204.0	185.5	12.7
	8	25.4	30.3	21.5	216.0	123.3	10.5
	9	20.6	26.2	15.7	214.0	55.2	6.8
	10	14.4	20.6	9.5	200.0	36.7	5.1
	11	6.6	12.4	2.0	170.0	13.4	5.2
	12	0.2	5.2	-4.2	157.0	6.1	2.4

Table 2 Seedling status in rapeseed before over-winter in 2006

 2

	Items	Green leaf numbers per plant	Diameter of rootstalk (cm)	Leaf area per plant (cm2)	LAI	Fresh weight per plant (g)	Dry weight per plant (g)
	Demonstration area(D)	10.6	1.4	1961	1.82	162.3	15.8
	Control area(CK)	9.5	1.3	1740	1.73	151.4	14.3
	±(D-CK)	1.1	0.1	221	0.09	10.9	1.5
	The investigation date was on Dec. 20, 2006

Table 3 Analysis on rapeseed yield and yield components in 2006-2007

 3

	Items	Plants (10 4 / ha)	Plant height (cm)	Length of available ramification (cm)	available ramification numbers on main stem	Length of main anthotaxy (cm)	Pod number on main anthotaxy	Pod number per plant	Seed number per pod	1000-weight (g)	Yields (kg/ ha
	Demonstration area (D)	17.1	1.65	38.4	8.3	55.5	76	336.2 18.1	3.4	2829.0
	Control area (CK)	17.1	1.55	35.1	6.9	49.3	71	258.5 17.9	3.2	2152.5
	±(D-CK)	0	0.10	3.3	1.4	6.2	5	77.7	0.2	0.2	676.5

  five-year plan (2001BA507A-09-04), 948 projects of Agricultural Ministry of China(2004-Z30), the national star project (2008GA690171), Jiangsu Province projects(06-G-169), and Jiangsu Government Scholarship for Overseas Studies; to Professor Qi Cunkou, and Associated Professor Chen Xinjun of the Institute of Economic Crops Research, JAAS for providing test materials.

  a/b where dL oj /dt is the development rate in the j th leaf age, f (L oj ) is a basic development function of leaf age, D Lj is the development days demanded from emergence to the j th leaf age, D Loj is the development days demanded from emergence to the j th leaf age under the optimum conditions, and T t and T o are daily mean temperature in the t th day, and the optimum temperature for development of leaf age (℃),

	respectively.

ACKNOWLEDGEMENT

The authors would like to express their appreciation to funding of national projects in the tenth

APPENDIX Simulation models for growth and development in rapeseed

Phenology

The basic models of rapeseed phenology were developed in the thesis through employing ideal of


Keywords: agricultural machinery total power, gray prediction, particle swarm optimization, background values, multiple transformation

Abstract. In order to improve the prediction accuracy of agricultural machinery total power then to provide the basis for the agricultural mechanization development goals, the paper used gray GM(2,1) model in the prediction. Through the introduction of parameter λ to correct the background value and parameter ρ for multiple transformation on the initial data, the model was expanded to And agricultural machinery total power was predicted based on Zhejiang province's statistics. Predicted results show that the combination forecast model prediction accuracy is higher than the gray GM(1,1) model and the model better fits the data. The forecast of the agricultural machinery total power of this combination forecast model is feasible and effective, and should be feasible in other areas of agriculture prediction.

Introduction

Agricultural machinery total power is the total power number that is used for agriculture, forestry, animal husbandry and fishery production and transportation of all machinery (including tractors, diesel engines, gasoline engines, turbines, motors, wind turbine, etc.), which reflects the general level of agricultural mechanization in an area. It is an important part of agricultural planning and is often listed as key indicators of planning [1]. There are many specific methods in the prediction of agricultural machinery total power, the gray system has been widely used because of the advantages of less data needed and small amount of computation. However, the current model applied to the agricultural forecast is just gray GM(1,1) model, which is actually a deviation of the index model, so it subjects to certain restrictions in application. Because of GM(2,1) model has two feature values, it can reflect the system monotonous or oscillations and should be widely used in theory, but its goodness of fit and prediction accuracy in practical application is not very satisfactory [START_REF] Xiao | Gray Technology Fundation and Application[END_REF]. This paper uses GM(2,1) model, references the method of Literature [START_REF] Liu | GM(2 ,1 ,λ,ρ) based on particle swarm optimization[END_REF], through introducing parameter λ to correct the background value and parameter ρ for multiple transformation on the initial data, expands the model to (2,1, , ) GM λ ρ model, increasing the prediction accuracy.

Particle swarm optimization (PSO) [START_REF] Kennedy | Particle swarmoptimization[END_REF][START_REF] Eberhart | A new optimizer using particle swarm theory[END_REF] is an evolutionary computation technique developed by J. Kennedy and R. C. Eberhart in 1995. In this algorithm, the objective function is initialized to a random solution, and each solution is regarded as a particle. Each particle has a initial position and a speed which decides the search direction and distance. The merits of particles are determined by the size of the fitness according to the objective function. Particles update themselves according to their fitness and speed, inertia weight w is used to maintain the balance between the global search and local search, larger inertia weight help search out of local minimum points, while the smaller are conducive to make algorithm converge and to improve search accuracy. Original PSO, inertia weight w is a constant, so is it in the literature [START_REF] Liu | GM(2 ,1 ,λ,ρ) based on particle swarm optimization[END_REF], literature [START_REF] Shi | A modified particle swarm optimizer[END_REF] proposed an adaptive strategy, that is just at the beginning inertia weight w is large, as the iteration progressing, it decreases linearly. Literature [START_REF] Eberhart | Particle swarm optimization: developments,applications and resources[END_REF] thinks that inertia weight is started at 1.4 and then gradually decreases to 0.35 is appropriate, so this paper documents the approach of literature [START_REF] Eberhart | Particle swarm optimization: developments,applications and resources[END_REF]. Examples show that the algorithm improves the data fitting accuracy and its relative error is smaller. So it can reveal the development law of agricultural machinery total power within the next few years, and is able to provide an important basis for agricultural machinery development plans. Original data sequence is located as:

(0) ( 0) (0) (0) 0 0 0 0 ( (1), (2) ( )) X x x
x n = Do multiple transformation on the data columns. Multiplication parameter ρ has the effect of zoom in or out of the amendment on the initial data, can adjust condition number of the model coefficients, turns original ill-conditioned matrix into a good state, and does not change development coefficient of the model [8] , the transformed data sequence is:

{ } (0) (0) (0) (0) 0 ( ) ( ) ( ), X x k x k x k k N ρ = = ∈
When this sequence is subjected to the Accumulating Generation Operation (AGO), the following sequence (1) X is obtained:

(1) (1) (1) (0) 1 ( ) ( ) ( ), k
i X x k x k x i k N = ⎧ ⎫ = = ∈ ⎨ ⎬ ⎩ ⎭ ∑
When this sequence is subjected to inverse AGO (IAGO) , the following sequence (1) (1) X α is obtained:

{ } (1) (1) (1) (1) (1) (1) (0) (0) ( ) ( ) ( ) ( 1)
, X x k x k x k x k k N α α α = = - - ∈
The whitening equation of GM (2,1) model is therefore, as follows:

2 (1) (1)
[ ]

1 2
ˆT a a a u = is a sequence of parameters that can be found as follows:

1 1 2 ˆ[ ] T a a a u B Y - = = (0) (1) (0) (1) (0) (1)
(2

) (2) 1 (3) (3) 1 ( ) ( ) 1 x z x z B x n z n ⎡ ⎤ - - ⎢ ⎥ - - ⎢ ⎥ = ⎢ ⎥ ⎢ ⎥ - - ⎢ ⎥ ⎣ ⎦
The original background value is defined as: (1) (1)

( ) 0.5 ( ) 0.5 ( 1)

z k x k x k = + -.
The revised background value by introducting of the parameter [1] is as below:

(

1) (1) (1) ( ) (1 ) ( ) ( 1) z k x k x k λ λ = - + - (1) (1) (1) (1) (1) (1) (2) (3) ( ) T Y x x x n α α α ⎡ ⎤ = ⎣ ⎦
For equation ( 1), when the discriminant of its characteristic equation

2 1 2 4 0 a a Δ = - > , it can get two real roots 1 1 2 a v -+ Δ = , 1 2 2 a v --Δ =
, then we can get the general solution:

1 2 (1) 1 2 2 ˆ( ) v k v k u x k Ce C e a = + +
The parameters 1 C , 2 C in formula are determined by the initial value condition.. By applying IAGO, the predictions for the (0)

X can be obtained:

(0) (1) (1)
ˆˆ( )

( ) ( 1) x k x k x k = - -
The prediction model of the original data sequence is:

(0) (0) 0 ˆ( ) ( ) / x k x k ρ =

Improved PSO algorithm to search the best , λ ρ

Generate randomly M groups , λ ρ according to population to form a twodimensional initial swarm population ( ) 0 λρ and the fitness value is evaluated by a predefined fitness function of the absolute relative error summation. Then search the optimal value , λ ρ for its minimum, the particles update themselves according to fitness and speed. Velocity and position update formula are as follows:

) ( * ) ( * 2 2 1 1 1 k k k k k k x gbest rand c x pbest rand c wv v - + - + = + 1 1 + + + = k k k v x x k v and k
x are the particle's current velocity and position,

GM model

Based on Zhejiang Province's statistics in literature [1] agricultural machinery total power was predicted. Fitness situation is shown in Figure 1, and fitness results are shown in Table 1. From Table 1 we can see that the maximum fitting relative error -12.1271% is smaller than the largest fitting relative error of -24.12% in literature [1], the average fitting relative error -1.5432% is less than -2.6105% in literature [1]. Fitting the initial data more accurate, the model is more in line with the trend of the initial data then can predict with a higher precision. Predicted results from 2004 to 2008 as shown in Table 2: (3) This prediction method on data is of high precision and prediction accuracy meets the practical requirements in the forecast of the agricultural machinery total power, so this study introduces a practical method to agricultural mechanization forecast.

( 2 ,

 2 1, , ) GM λ ρ model and prediction accuracy was improved. Because of the nonlinear traits between parameter , λ ρ and the prediction errors, they are difficult to be solved. The paper used Particle Swarm Optimization (PSO) to search the best parameter , λ ρ , then combination forecast model of PSO-GM(2,1, , λ ρ ) was constructed. In order to avoid incorrect selection of inertia weight w causing the global search and local search imbalance, the paper used Decreasing Inertia Weight Particle Swarm Optimization, in which parameter w gradually decreases from 1.4 to 0.35.

1 c 2 randFitness

 12 and position, k pbest is the optimal solution which the particle itself searches, ie the individual extreme, k gbest is the optimal solution that the entire particle population searches, ie the global extreme. and are accelerating factors within [0,2], in this paper 1 are two random numbers within [0,1]. And w decreases linearly from 1.4 to 0.35.

Fig. 1 .

 1 Fig. 1. Total power fitting image of Zhejiang Province

  Yu-xiao LIU 1 , Xiao-yi MA1 , Yan LONG 21 Electric Department of College of Water Resources and Architectural Engineering, NorthWest A&F University. 712100 Yangling, Shannxi, China 2 College of Mechanical and Electric Engineering, NorthWest A&F University. 712100 Yangling, Shannxi, China { Di-yi CHEN, Yu-xiao LIU, Xiao-yi MA, Yan LONG } ieee307}@163.com

	Prediction of Agricultural
	Machinery Total Power Based on PSO-GM(2,1, , λ ρ ) Model
	Di-yi CHEN 1,2,1 ,