Liangliang Jia
email: jiall@cau.edu.cn

Zihui Yu

Fei Li

Martin Gnyp

Wolfgang Koppe

Georg Bareth

Yuxin Miao

Xinping Chen

Fusuo Zhang

{liangliang Jia

Nitrogen Status Estimation of Winter Wheat by Using an IKONOS Satellite Image in the North China Plain

Keywords: IKONOS image, vegetation indices, nitrogen status, winter wheat

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

Introduction

The North China Plain is one of the most important grain yield production areas in China. Excessive N fertilizer application is considered as a common problem for wheat production in this area and results in low nutrient use efficiency and potentially exerts more pressure on the environment such as nitrate leaching to the ground water [START_REF] Zhao | Fertilization and nitrogen balance in a Wheat-Maize Rotation System in North China[END_REF][START_REF] Ju | Changes in the soil environment from excessive application of fertilizers and manures to two contrasting intensive cropping systems on the North China Plain[END_REF][START_REF] Gao | Investigation on nitrate pollution in ground water at intensive agricultural region in Huanghe-huaihe-haihe Plain[END_REF][START_REF] Zhang | Nitrate pollution of groundwater in northern China[END_REF] . In recent years, scientists have developed several in-field soil-plant analysis methods to monitor the N status of various crops to optimize the timing and rate of N application, including the plant total N content and SPAD readings [START_REF] Blackmer | Analysis of aerial photography for nitrogen[END_REF] , the testing of sap nitrate in the basal stem of wheat at specific growth stage [START_REF] Fox | Soil and tissue nitrate tests compared for predicting soil nitrogen availability to corn[END_REF][START_REF] Li | Nitrogen nutritional diagnosis and recommendation as topdressing fertilizer N for several crops in North China: diagnosis of nitrate in plant tissue by quick test method[END_REF] and soil Nmin test [8- 9] . All these approaches, however, need destructive sampling and laboratory analysis, which limited their applications for wheat N management for large areas in the North China Plain.

Considering these problems, some researches now focus on remote sensing methods especially using canopy multispectral reflectance or aerial photography to monitor wheat growth conditions. Aerial photography, which uses color or infrared film, has been used in detection N deficiency, evaluation of plant growth status and prediction of N fertilizer requirements of wheat [START_REF] Flowers | Remote sensing of winter wheat tiller density for early nitrogen application decisions[END_REF][START_REF] Jia | Low altitude aerial photography for optimum N fertilization of winter wheat on the North China Plain[END_REF] and maize [START_REF] Scharf | Calibrating corn color from aerial photographs to predict sidedress nitrogen need[END_REF][START_REF] Sripada | Aerial Color Infrared Photography for Determining Late-Season Nitrogen Requirements in Corn[END_REF][START_REF] Sripada | Aerial Color Infrared Photography for Determining Early In-Season Nitrogen Requirements in Corn[END_REF] . In these contributions, aerial photography provided high spatial resolution images of crop canopy and showed the potential of quantifying crop N status variation within and between fields. However, aerial photography is always restricted by the weather conditions and flying control on the ground. Another shortcoming is that aerial photography can only cover small areas with plane [START_REF] Scharf | Calibrating corn color from aerial photographs to predict sidedress nitrogen need[END_REF] or hellium balloon [START_REF] Jia | Low altitude aerial photography for optimum N fertilization of winter wheat on the North China Plain[END_REF][START_REF] Gerard | Estimation of spatial variablity in pearl millet growth with non-destructive methods[END_REF] . When it comes to regional scale, such as a county level or provincial level, the acquisition of crop canopy images will be too time-consuming and expensive.

With the development of remote sensing technology, satellites now can provide commercial, public accessible and high spatial resolution image data. For example, IKONOS or Quickbird can provide multi-spectral data of 2-4 meter resolution covering the blue to near infrared spectrum, which may be used for crop N management [START_REF] Franizer | Remote sensing and satellite image technology: capabilities and implementation[END_REF] . Wright et al. [START_REF] Wright | Canopy reflectance estimation of wheat nitrogen content for grain protein management[END_REF] found significant correlations between NDVI, extracted from IKONOS image, with N content of wheat flag leaves and pre-season N input. Zhang et al. [START_REF] Zhang | Predicting nitrogen status of rice using multispectral data at canopy scale[END_REF] indicated the IKONOS image successifully estimate the rice leaves N content. Shou et al. [START_REF] Shou | Using high-resolution satellite image to evaluate nitrogen status of winter wheat in the North China Plain[END_REF] found significant correlations between vegetation indices (NDVI, RVI, GNDVI and DVI) and digital values of NIR, Red, Green and Blue bands extracted from a Quickbird image with N status indicators (chlorophyll meter readings, stem sap nitrate concentration, aboveground biomass and shoot N concentration at shooting stage). However, all these researches were conducted in small and controlled experiments. Studies conducted in real farmers' fields, especially in overfertilized fields in the North China Plain, and which transfer the small experimental based studies results to farmers fields N recommendation, are very limited. Therefore, the objective of this research was to use high spatial resolution, multispectral satellite image to predict winter wheat N status in farmers' fields at the shooting stage, which was the critical N sensitive stage to wheat, and evaluate this approach against conventional soil and plant sampling methods.

2

Material and Methods replications. The pre-plant N levels covered the basal fertilization rate on the North China Plain. At shooting stage, each plot was divided into two parts, one received no N topdressing while for the other part N topdressing was applied. The fertilization rate was determined based on an improved Nmin method [START_REF] Chen | Synchronizing N supply from soil and fertilizer and N demand of winter wheat by an improved Nmin method[END_REF] . The detailed N fertilization management practices in this research are described in Besides the field experiment, three separate sites of Xujia, Xili and Shizhang were selected as the sub-study areas (Figure 1), which included 10, 12 and 16 farmers' fields, respectively. The sites were selected due to similar wheat varieties were planted in each sites. All the farmers' fields were managed by the farmers themselves, and the basal and topdressing nitrogen fertilization rates for the selected fields were summarized in Table 2.

Field survey and agronomic measurements

Wheat N status indicators were measured at the shooting stage in Apr. 10, 2006. The nitrate concentration of the plant stem sap was determined with a Reflect Meter (Merck Co., Darmstadt, Germany), and SPAD readings were taken on the uppermost fully expanded leaves from 30 randomly selected plants in per plot and the average value was used for further analysis. Five soil cores were collected from each plot and pooled at 0 to 30 cm, 30 to 60 cm, and 60 to 90 cm depth intervals. To analyze the soil Nmin, all samples were sieved, extracted with 0.01 M CaCl2 and analyzed for NH4+-N and NO3--N by continues flow analyzer TRAACS 2000 [20] . The soil Nmin was used to determine the optimum N topdressing rate at shooting stage as described by Chen et al. [8] . Aboveground wheat biomass were harvested (1 m 2 per plot) for the experiment, and dried at 60 °C to constant weight for biomass dry weight, and then analyzed for total N concentration using Kjeldahl digestion method.

For the selected farmers' fields in Xili, Xujia and Shizhang sites, only the aboveground plant biomass and total shoot N concentration were determined at shooting stage.

A "Trimble-AgGPS ® 132" GPS receiver was used to georeference the sampling points in the experiment and farmers' fields, and the boundaries of the experiment plots.

IKONOS Image processing

The IKONOS images, which included 4-m resolution multispectral bands (Blue band: 0.45-0.53 µm; Green band: 0.52-0.61µm; Red: band 0.64-0.72 µm; and NIR band: 0.77-0.88 µm) and 1 m resolution panchromatic band (0.45-0.90 µm), were acquired on Apr. 20th, 2006, at the shooting stage of winter wheat. Since no temporal comparison or with other imagery was planned, the atmospheric corrections in this research was not performed [START_REF] Song | Classification and change detection using Landsat TM Data: When and How to Correct Atmospheric Effects?[END_REF] . The collected DGPS points at the transaction points around the study sites were converted to shapefiles using ArcGIS 8.1 and were imported into ENVI 4.2 as a vector layer. This layer was used to georectify the IKONOS image and to extract the plotted pixels' raw digital spectral reflectance values of NIR, Red, Green and Blue bands. At least 3 pixels in each experiment plots were used to get averaged raw digital spectral reflectance values.

Data analysis

The vegetation indices (VIs) of NDVI (normalized vegetation index [START_REF] Rouse | Monitoring vegetation systems in the great plains with ERTS[END_REF] ), RVI (ratio vegetation index [START_REF] Jordan | Derivation of leaf area index from quality of light on the forest floor[END_REF] ), GNDVI (green vegetation index [START_REF] Gitelson | Remote sensing of chlorophyll concentration in higher plant leaves[END_REF] ) and OSAVI (optimized soiladjusted vegetation index [START_REF] Rondeaux | Optimization of soil-adjusted vegetation indices[END_REF] ) were calculated for the obtained IKONOS satellite image. The detailed expressions of the vegetation indices were listed in Table 3. [START_REF]SAS/STAT user's guide[END_REF] . Linear or nonlinear models were fitted based on the plot patterns and best-fit R 2 values for the relationship. When linear relationships were not significant, exponential or logarithm models were attempted. Then the best-fit models were used to predict, nitrogen uptake in the farmers' fields. The root mean square error (RMSE) and relative error was calculated to compare the precision of estimation between the measured values (X) and the estimated values (X').

3 Results:

Nitrogen status of wheat at shooting stage

The N status of winter wheat at shooting stage was significantly affected by the N treatments (Table 4). High N supply (initial soil Nmin + pre-plant N fertilization) led to high SPAD readings, aboveground biomass, shoot N concentration, stem sap nitrate concentration and total N uptake. The significant difference among the 5 pre-N treatments showed that the low N supply treatments of 1 and 2 were in the state of N deficiency, having lower aboveground biomass, SPAD readings, stem sap nitrate concentration and N uptake than the high N treatments of 4 and 5, which were in the state of over fertilization. The treatment 3 seemed to have optimum N status with relatively high SPAD readings and stem sap nitrate concentration.

Correlations of vegetation indices with wheat N status at shooting stage

Significant correlations were found between NDVI, RVI and GNDVI with SPAD readings, stem sap nitrate concentration, aboveground biomass and nitrogen uptake at shooting stage (R 2 = 0.488-0.739) (Table 5). All of the single band reflectance of NIR, Red and Green had significant correlations with SPAD, stem sap nitrate concentration, biomass and N uptake (R 2 = 0.240-0.645), which were lower than those with VIs.

Neither VIs nor single band reflectance used in this research could significantly explain wheat N concentration variation (R 2 = 0.008 -0.397), but the high R 2 values with aboveground biomass (R 2 = 0.667-0.708) and nitrogen uptake (R 2 =0.678-0.739) suggested that VIs could still be used to explain N and growth status of wheat at shooting stage.

Nitrogen uptake estimation of winter wheat for farmers' fields

The N uptake of the winter wheat at shooting stage for farmers' fields in Xili, Shizhang, Xujia and Xizhangliu were predicted using the relationships developed with the experimental data (Figure 2). The estimated N uptake values using any of the four vegetation indices were all significantly correlated with measured values. Considering the normally farmers N application rate of 277-441 kg/ha in this region, the RMSE for NDVI and OSAVI were acceptable (26.6-27.4 kg/ha) while the RMSE for RVI and GNDVI were too high (42.2 -61.0 kg/ha) to be acceptable by the farmers. Compared with the other vegetation indices used in this research, OSAVI was the best for nitrogen uptake prediction (closest to 1:1 line), while N uptake predicted with RVI and GNDVI were either higher or lower than measured values at the state of high N uptake. The results suggested that NDVI and OSAVI derived from IKONOS satellite image could be used for monitoring and estimating of wheat growth and nitrogen status. A very important approach to precision N management is to detect crop N status during the growing season and use this information to guide in-season N management. Traditional soil-plant analysis methods, such as SPAD, biomass, shoot N concent and soil Nmin, are either destructive or too time-and money-intensive for regional investigation. High resolution multispectral satellite imagary provides a promising solution for non-destructive and fast estimation of crops growth and N status. The vegetation indices used in this research all showed significant correlations with wheat N status indicators of SPAD readings, stem sap nitrate concentration, upland biomass and nitrogen uptake, but not with shoot N concentration. These results differ from earlier findings by Shou et al. [19] and Wright et al. [17] , which showed that vegetation indices derived from Quickbird satellite image had significant correlations with shoot N concentration. An explanation of the low correlations of IKONOS image with shoot N concentration is that rapid wheat aboveground biomass increase at shooting stage causes unstable changes of the shoot N concentration when field measurements and satellite image were taken. Measurements at later growing stages and corresponding image acquisition may have a better relationship. More studies are needed to test this hypothesis.

The successful estimation of the nitrogen uptake in overfertilized farmers' fields, rather than in control experiments [START_REF] Xue | Monitoring leaf nitrogen status in rice with canopy spectral reflectance[END_REF][START_REF] Chang | Predicting rice yield using canopy reflectance measured at booting stage[END_REF][START_REF] Elwadie | Remote sensing canopy dynamics and biophysical variables estimation of corn in Michigan[END_REF][START_REF] Ferrio | Assessment durum wheat yield using visible and near-infrared reflectance spectra of canopies[END_REF] , supported the idea suggested by Shou et al. [START_REF] Shou | Using high-resolution satellite image to evaluate nitrogen status of winter wheat in the North China Plain[END_REF] to use high resolution satellite image combined with in-site soil-plant analysis at small reference fields to monitor regional nitrogen status of winter wheat in North China Plain.

The fast development of the satellite technologies have made the acquisition of high spatial and spectral resolution satellite images become cheaper and easier than before. In recent years, some researchers have considered hyperspectral sensors to monitor rice nitrogen status and nutrients disorder [START_REF] Chen | Estimating canopy chlorophyll and nitrogen concentration of rice from EO-1 Hyperion data[END_REF] and satellites with radar systems (SAR) to study soil and crop moisture [START_REF] Leconte | Mapping near-surface soil moisture with RADARSAT-1 synthetic aperture radar data[END_REF][START_REF] Griffiths | Temporal Monitoring of Soil Moisture Using ERS-1 SAR Data[END_REF] and forest biomass [34] (Austin et al., 2003). Although more sensors have been used in monitoring vegetation growth status than before, satellite images for agriculture uses still face many challenges, such as clouds or dust which interfere the acquisition or quality of the image, low spectral resolution in explaining the canopy and canopy reflectance disorders caused by other factors except nutrients, etc. A possible solution to this problem is multi-source remote sensing data fusion. For example, high spatial resolution IKONOS or Quickbird satellite images combined with high spectral resolution EO-1 Hyperion and soil-plant analysis may provide better explanations for crop canopy nutrient disorders. In such studies, plots with optimum N management should be included to provide reference values. This may allow the development of site-specific fertilizer recommendation maps as described by Scharf and Lory [START_REF] Scharf | Calibrating corn color from aerial photographs to predict sidedress nitrogen need[END_REF] . Further researches are needed to verify this idea and develop a robust system that will allow farmers to access these data to improve their management decisions.

Figure 1 .

 1 Figure 1. The distribution of experiment and research sites at 2005/2006 wheat season in Huimin County, Shandong Province

Figure 2 .

 2 Figure 2. Comparison of the accuracy of the measured N uptake at shooting stage with predicted N uptake in the farmer's field (RMSE = root mean square error; RE% = relative error; number of observations = 84)

Table 1

 1

	. The area

Table 2 .

 2 The average basal N fertilization rate for the selected farmers' fields in 2005/2006 wheat season

	Sites	Total area	Number	Varieties	Planting date	Averaged basal
		(ha)	of Fields			N rate (kg/ha)
	Xujia	3.81	12	JM21	14, Oct. 2005	117 (58-180)
	Xili	4.00	16	JM23	15, Oct, 2005	149 (49-316)
	Shizhang	3.75	10	JM22 and LM23	18, Oct, 2005	78 (34-120)

Table 3 .

 3 Broadband vegetation indices used for image analysis Nitrogen effects were analyzed quantitatively by comparing the means of agronomic parameters of each treatment through LSD test at a probability of 0.05. The single band reflectance of R, G, B and NIR bands and the vegetation indices of NDVI, GNDVI, RVI and OSAVI were regressed with the chlorophyll meter readings (SPAD), soil N input before sowing (including initial Nmin and basal N), stem sap nitrate concentration, shoot N concentration, aboveground biomass and nitrogen uptake. The regressions of single bands reflectance and VIs with plant N status indicators were made using Statistical Analysis System version 8.1

	Abbreviation	Name	Vegetation Index	Reference
	NDVI	Normalized Difference Vegetation Index	NDVI=(NIR-Red)/(NIR+Red)	Rouse et al., 1973
	RVI	Ratio Vegetation Index	RVI=NIR/Red	Jordan, 1969
		Green Normalized		Gitelson and
	GNDVI	Difference	GNDVI=(NIR-Green)/(NIR+Green)	Merzlyak,
		Vegetation Index		1998
	OSAVI	Optimized soil-adjusted vegetation index	OSAVI =(NIR-Red)/(NIR+Red+0.16)	Rondeaux et al., 1996

Table 4 .

 4 Nitrogen status of winter wheat at shooting stage

	Items\Treatments	1	2	3	4	5
	Nmin (0-90 cm)	56b	62b	82b	124a	112a
	Chlorophyll meter readings (SPAD)	46.2c	48.2b	49.6a	50.1a	50.6a
	Upland biomass (kg/ha)	852c	1137c	1273b	1543ab	1703a
	Shoot N concentration (g/kg)	32.4c	32.7bc	33.8bc	36.6ab	37.8a
	N uptake (kg/ha)	27.5d	36.9cd	43.5bc	57.0ab	63.6a
	Stem nitrate Concentration (mg/kg)	1228c	1295c	1672b	1834ab	1955a
	*Means with different letter are significant different at 0.05 level.

Table 5 .

 5 Coefficient of determination (R 2 ) for wheat N status with single band digital value and vegetation indices derived from IKONOS satellite image at shooting stage

	R2	SPAD	Stem sap nitrate	Biomass	N Content	N uptake
	NIR	0.424*	0.434*	0.572*	0.291	0.615**
	Red	0.645**	0.342*	0.501*	0.046	0.430*
	Green	0.483*	0.352*	0.426*	0.008	0.329*
	Blue	0.284	0.318	0.297	0.015	0.240
	NDVI	0.707**	0.507*	0.704**	0.181	0.678**
	GNDVI	0.647**	0.541*	0.708**	0.184	0.683**
	RVI	0.686**	0.537*	0.667**	0.199	0.704**
	OSAVI	0.678**	0.513*	0.707**	0.397*	0.739**
	* LSD=0.05, ** LSD=0.01, number of observations = 20

Acknowledgements

This research was financially supported by the Special Fund for Agro-scientific Research in the Public Interest (201103003), the Program for Changjiang Scholars and Innovative Research Team in University of China (IRT 0511), International Bureau of the BMBF, Germany and the GIS & RS Group of the University of Cologne.


Lingling Sang

Chao Zhang
email: zhangchaobj@cau.edu.cn

Jianyu Yang

Dehai Zhu

Wenju Yun

Assessment of potential risk in soil and early warning analysis in four counties, Northeast China

Keywords: agricultural soil, heavy metal, potential risk, early warning

Heavy metal pollutions in the soil have been an important content of its quality. The objectives of this study were to evaluate the potential risk of heavy metals and early warning analysis of agricultural soil in four counties, Northeast China. One method based on toxicity coefficients was applied to assess the potential risk of these heavy metals in soil. The results showed that average content of Cu, Pb, Zn, Cd, As, Ni, Hg and Cr in agricultural soil was lower than the first level standard of agricultural soil, and Cd led to risk was the most important factor, which 0.21% of the area was in the state of potential risk of elements Cd. And other heavy metals were not harmful to the environment, which potential risk of harm in descending order is E

The other method is that the Cd pollutants can be calculated by the atmospheric dry and wet deposition, the irrigation water, the chemical fertilizer and the crops. There was a point on the assumption that the pollution caused by the atmospheric dry and wet deposition was worse than the pollution from irrigation water and chemical fertilizer. It is concluded that heavy metal pollution of Cd in the southeast of the study area will be getting worse within the next few decades by forecast warning analysis. Overall, some part of study area is in the state of potential risk and the Cd of soil is a major factor to control the retention of the heavy metals in the soil. According to analysis of their characteristics and the level of potential risk, there will not be great impact on quality by developing appropriate control measures timely.

Introduction

The quality of the field is not only the comprehensive measurement for the quality of the soil, environment and health of the overall field, but also the main embodiment for the ability of maintaining the productive power, fighting against natural calamities, and hazard-free treatment of the agricultural products and the high nutrition and quality [START_REF] Gao Xiangjun | Research advances of gradation and evaluation of agricultural land in China[END_REF]. In recent years, the evaluation for the agricultural products usually focuses on the productivity and the effectiveness, among which the classification of the agricultural field and the earth chemical evaluation for the quality of the soil are considered as two important branch of the soil quality evaluation [START_REF] Huang | Heavy-metal pollution and potential ecological risk assessment of sediments from Baihua Lake, Guizhou, P.R. China[END_REF][START_REF] Ahnstrom | Development and assessment of a sequential extraction procedure for the fractionation of soil cadmium[END_REF]. Those two works undertake scientific evaluation to the quality of the soil as well as the ecoenvironment from different perspective and conform to each other in terms of the rational application of the soil. However, the evaluation upon the agriculture focuses on the effects of the quality of agricultural field and considers the earth chemical evaluation for the quality of the soil as part of the standard for the evaluation of the environment-friendly and healthy elements [START_REF] Fernandes | Heavy metals distribution in sediments and ecological risk assessment: The role of diagenetic processes in reducing metal toxicity in bottom sediments[END_REF].

In recent years, the intensified industrialization and urbanization as well as the effects of human activity lead to many eco environmental problems, among which the heavy metal pollution of the agriculture field has aroused extensive concern of the overseas and domestic scholars [START_REF] Donald | Toxic metals in the environment: the role of surfaces[END_REF]. As the important earth resource, agriculture field has shift from the state of insufficient quantity to the state of premium insufficient quantity, which in essence implies important environment pollution and the food safety and affects negatively upon the human health and environmental safety. Therefore, it is of high significance to undertake risk evaluation to the heavy metal pollution of the soil and the early warning analysis [START_REF] Casper | Contamination of surface soils, river water and sediments by trace metals from copper processing industry in the Churnet River Valley, Staffordshire, UK[END_REF].

Taking some counties in Northeast China as the sample region, based on the data of heavy metals, the paper analyzed its potential sources of heavy metals and assessed the potential risks of heavy metals. According to early warning after the study, the protective measures are put forward, providing the scientific suggestion for the farmland protection and the security of land resource.

2

Data and Methods

Study area and data

The southwest of Songnen Plain, which is also the western region of Jilin Province, belongs to the sub humid and sub arid continental and seasonal climate zone with obvious changes of the weather in the year. The annual rainfall is about 400mm, which is 3.5-4.7 times of the evaporation capacity. Due to the difference of the agricultural climate condition and the earth eco condition, it forms the combined agricultural production state of both the typical agricultural region and the ecotone between agriculture and animal husbandry. The soil types are mainly composed of meadow soil, black earth, saline-alkali soil, and aeolian sandy soil. The loose structure of the soil deduces the capability of preserving moisture and fertility, so that the natural structure and the fertility would be damaged by the drought and flood. Because of human beings' interference, the quality of the soil decreases by a large margin. It is now becoming the potential sand resource for the sandy soil. While the accumulated substance in the plain is sticky and heavy with low penetration power and developing water net so that the water cannot flow out and the soluble salt cannot be drain out, and due to the evaporation, the soil is easy to undertake salinization and desertification. Because of the long term overlook of the vulnerably field as well as the long-term and large scale agricultural development, the eco environment of this region is deteriorated, the agricultural and economic development is relatively slow. This research applies BJZ 54 and Gauss-Kruger Projection to pasteurize the blank miles grid, so that the data of the investment sample could be integrated with the attributes of the blank miles grid and construct the foundation of the research work. The main resource of the data derives from the basic material for the chemical evaluation of the earth quality provided by the Ministry of Land and Resources. In accordance with the multi target earth chemical investigation standard, we station according to the earth chemical sampling method. With the facilitation of the fieldwork illustration of 1:50 000 topographic map, we undertake the investigation on the state-province level. The main investigation focuses on 50 elements including the soil, ground water, shallow ground water, atmospheric bulk deposition, and crop's grain etc. The intensity of the sample is 1 point/km 2 , among which there are 5179 soil data sampling spots. The sampling station of this time and the sample analysis are completed by China University of Geosciences and Geological Survey Institute of some Province jointly.

Potential risk assessment

The potential risk index method developed by Hakanson [START_REF] Hakanson | An ecological risk index for aquatic pollution control: a sedimentological approach[END_REF] was applied in this study.

According to this method, the potential risk coefficient ( i r E ) of single element and the potential risk index(RI)of multi-element can be computed via the following equations:

  i r E RI (1) i r i r i r C T E   (2) i F i D i r C C C /  (3)
In these equations:

i F i D i r C C C / 
is the accumulating coefficient for the element of "i"and is single heavy metal pollution index;

i D
C is actual density of heavy metal quality in soil;

i F
C is background value of heavy metal quality in soil; RI is comprehensive potential risk index of muti-element;

i r E is the potential risk index of single element; i r T is the toxicity coefficient for the heavy metals element of "i", reflects its toxicity levels and the sensitivity to harm the human and the ecosystem. The toxicity coefficients for common heavy metals were Hg(40)> Cd(30)>As (10)> Pb(5)=Ni( 5)=Cu( 5)>Cr( 2)>Zn(1) [7][8]. Table 1 is the Classification standard of potential risk of heavy metals in soil.

Early warning analysis

In this paper, the research on the atmospheric dry and wet deposition, the irrigation water and the chemical fertilizer in the Cd were analyzed. Input pathway of Cd in soil is mainly from the atmospheric dry and wet deposition, the irrigation water and the chemical fertilizer, output pathway of Cd in soil is mainly from the crops [START_REF] Usepa | Review of ecological risk assessment methods[END_REF][START_REF] Qifeng | A study of cadmium ecological security forecasting and early warning in agroecosystem of the Chengdu Economic Region[END_REF].

According to the concept of potential risks, the total Contents of the Cd pollutants can be calculated by the atmospheric dry and wet deposition, the irrigation water, the chemical fertilizer and the crops via the following equations:

() DI D I F OP     (4) 0 m M DI n M    (5)
In these equations: DI is the total input of heavy metal in soil, and the unit is g/(hm 2 •a );D、I、F are respectively input of heavy metal in soil by the atmospheric dry and wet deposition, the irrigation water, and the unit is g/(hm 2 •a ); OP is the total output of heavy metal in soil for the element of "i", and the unit is g/ (hm 2 •a ); M is the total content of heavy metal in soil for the element of "i" for several years (n) later, and the unit is mg•kg -1 ; M 0 is the current input of heavy metal in soil, and the unit is mg•kg -1 .

Results and discussion

Characteristic analysis of the content of heavy metal in farmland soil

The analysis results of heavy metal contents of farmland soil in four counties of Northeast China are listed in Figure 1. Statistical analysis of the heavy metals of Cd, Hg, As, Cu, Pb, Cr, Zn and Ni in farmland soil has proved to be normal distribution. In this paper, arithmetic mean value and median value were used to describe the contents of heavy metals in the soil. Compared with Environmental Quality Standard for Soils in China (GB 15618-1995) [START_REF]Environmental Quality Standard for Soils[END_REF], average content of Cu, Pb, Zn, Cd, As, Ni, Hg and Cr in agricultural soil was lower than the national first class standard of agricultural soil, and the heavy metal contents in the soil was in the natural background level basically in study area. In addition to Cd, the maximum value of the content of each element was lower than the national secondary standard.

Fig.1. Statistics of heavy metal concentrations in soil of study area

Based on the monitoring data of soil quality of study area, a quantitative analysis of farmland soil is conducted by applying the method of single and composite pollution exponent Indexes. The analysis results showed that there were 11 units in the soil samples pollution of heavy metal Cd and one unit in the soil samples pollution of heavy metal As. Other units in the soil samples were almost non-pollution. Three units in soil samples were light pollution (integration pollution index is from 1.0 to 2.0), and ten units in soil samples were to be at the alert posture (integration pollution index is from 0.7 to 1.0). Other units' synthesis pollution index was smaller than 0.7, belongs to the security rank (integration pollution index is lower than 0.7). 99.79% of the total land area was at safety level, 0.17% affected by light pollution and 0.04% affected by medium pollution.

Several results had been obtained: At the 5179 sampling sites in study area, the heavy metal pollution index descended in the order of Cd(2.30) > As(1.14) > Hg(0.52) > Cr(0.46) > Pb(0.11). The contents of heavy metals of Cd and As in the soil were higher than the contents of other heavy metals, separately accounts for 0.21% and 0.02% in Table 2. So there should pay attention to the increase of the content of soil Cd and bring the potential risks.

Single and comprehensive potential risk assessment of heavy metal in soil

Refer to the Hakanson assessment method, heavy metal's potential risk index of single element in soil( i r E )and comprehensive potential risk index of muti-element (RI)were shown in Table 4. From Table 3, the scopes of the potential risk coefficients of 8 kinds of heavy metals are: According to the calculated accumulating coefficients, Hg, As, Cu, Pb, Cr, Zn and Ni had not achieved the ecology risk level, Cd was the most serious polluting element among these heavy metals, and its mean value of i r E was up to 1.8, and Cd was the main influencing factor to causes the risk. Some 0.21% region existed potential risk of Cd element, and other heavy metals were not harmful to the environment. Therefore Cd element was the key element to determine to study.

The scope of the potential risk index (RI) of multi-element is 14.87~228.74 in Table 3. According to its frequency distribution in Figure 2, there is no area in the considerable risk and the serious potential risk. 0.06% of the soil sample points were in a medium risk degree, which the mainly reason was that potential risk coefficient of Cd element was great. The result was shown that agricultural soil in study area would encounter the Moderate risk of Cd element, and 3 soil sampling points (0.06%) were above the level of risk.

Forecast and early warning analysis

Through the statistical analysis on contents of heavy metals by the atmospheric dry and wet deposition, the ground water and the surface water, the atmospheric dry and wet deposition is the main input way of Cd. Drawing up the maps of Cd distribution by the spatial interpolation, the results showed that the greatest regional distribution affected by the atmospheric dry and wet deposition were located in the central and southwestern part of Da'an, as well as the northern and southern of Zhanlai. Besides, some areas in the eastern of Qianguo were occupied by acid soils which Cd pollution needed to pay more attention. This is because the soil pH is an important factor affecting the content of Cd. The content of Cd in crops will enrich with the increase of pH value [START_REF] Qifeng | A study of cadmium ecological security forecasting and early warning in agroecosystem of the Chengdu Economic Region[END_REF]. Distribution characteristics of Cd were obtained through connecting the layer properties, re-calculating the content of Cd by the atmospheric dry and wet deposition and restart interpolation. The potential risks were only considered by input of the dry and wet deposition, and the content of Cd in soil of agricultural land was increased at an average annual speed of 0.006 mg in Table 4. Evaluations on the potential risk and forecasting were carried by the formula (5), and calculated the content of Cd in the next 20, 50 and 100 years. 0.006 Through computing the content value of Cd in soil in the next 20, 50 and 100 years, and comparing with the standard value of soil environmental quality, the pollution indexes of Cd element in the next 20, 50 and 100 years were calculated and determined whether the risk exists. The areas with heavy warning in the southeast of study area will be lead Cd pollution in the agricultural land area in 20 years in Figure 4(a); the areas with moderate warning in the southeast of study area will be lead Cd pollution in the agricultural land area in 50 years in Figure 4(b), and the areas with light warning in the southeast of study area will be lead Cd pollution in the agricultural land area in 100 years in Figure 4(c).

Through the early warning analysis of Cd element in research area, the area of agricultural land which was polluted by Cd, will be 1 634.79 hectares in 20 years; the area of agricultural land which was polluted by Cd will be 4 050.36 hectares in 50 years; the area of agricultural land which was polluted by Cd will be 10 355.35 hectares in 100 years. 1.5% of the total area was with potential risks, and mainly located in the southeastern of study area, where of agricultural land was high-yield and soil type are mainly based on sand. From Figure 3, the area with warning will continue to expand as time goes by, so some preventive measures and early warning suggestions are put forward on the problems.

Conclusions

(1)Among the pollution index in the unit soil of the research area, 11 units of Cd are more than 1, and one of the unit surpasses the As standard, while other elements and unit pollution indexes are all less than 1; In terms of the comprehensive pollution index, there are 3 of the unit belonging to light pollution, 10 of them under the warning state, and other units are blow 0.7, which belong to the safety grade.

According to the outcome of the evaluation, 99.79% of the total research area is labeled as the non-polluted area during the quality evaluation of the soil environment. Besides,the 11 polluted grid region accounts for 0.21% of the total research region, among which the number of light polluted area, and 2 middle polluted area.

(

) 2
The damage level of the potential risky heavy metal is as follows: E(Cd)> E(Hg)>E(As)>E(Ni)>E(Cu)>E(Pb)>E(Cr)>E(Zn). Hg、As、Cu、Pb、Cr、Zn and Ni has not achieved the risky level of the eco pollution, among which Cd element is the main reason for the influence of the eco system. 0.21% of the sampling regions are under the potential risk of Cd pollution, and other heavy metals place little or no damage to the environment. Therefore, Cd element has been taken as the important research target.

(3) Through the warning analysis of Cd element in the sampling region, we can draw the conclusion that the Cd pollution would reach 1 634.79 hm2 after 20 years, 4 050.36 hm2 after 50 years and 10 355.35 hm2 after 100 years. The total area of the agricultural field under potential risk accounts for 1.5% of the total area, mainly lying in the southeast region of the research area.

According to the risk evaluation analysis, we could estimate the potential risk confronted by agriculture field, which would set the quantitative basis for not only the administration of the soil environment and the environmental protection, but also the construction of the green agriculture product and the establishment of the heavy metals pollution as well as the proper evaluation to the agriculture field and the environment. Therefore, it is of vital importance to the rational application of the soil resource. Aiming at the outcome of the evaluation, we can draw the conclusion that part of the area is in the potential risk, and we would analyze the reason through further intensification of the sampling station; Because of the lack of investigation data, we only take the atmospheric bulk deposition into consideration in the net quantity of heavy metal Cd in the potential risk evaluation method. For the lack of the elements like fertilizing, watering and the crops, further study concerning this dynamic mechanism is in need.

  )0.18~5.08。From the means of the potential risk coefficients of 8 kinds of heavy metals, the potential risk descended in the order of E (Cd) >E (Hg) >E (As) >E (Ni) >E (Cu) >E (Pb) >E (Cr) >E (Zn).

Fig. 2 .

 2 Fig.2. Frequencies of potential risk coefficient (E) and potential risk index(RI)of heavy metals in study area.

Fig. 3 .

 3 Fig. 3. Warning distribution map of heavy metal in soil.

Table 1 .

 1 Classification standard of potential risk of heavy metals in soil.

	E	i	Pollution degree	RI	Pollution degree
		r

Table 2 .

 2 Heavy metal pollution.

	heavy metal index(Minimum)	index(Maximum)	index(mean)	Portion (%)
	Cd	0.020	2.297	0.172	0.21%
	As	0.040	1.140	0.259	0.02%

Table 3 .

 3 Statistics of potential risk coefficient (E) and potential risk index(RI)of heavy metals in study area.

				potential risk coefficient	E	r i	RI
		Cd	Hg	As	Cu	Pb		Cr	Zn	Ni
	Minmum	1.80	1.07 0.67	0.42	1.80	0.21	0.10	0.18	14.87
	Maximum	206.70 69.6 19.00 4.81	4.73	1.78	1.15	5.08	228.74
	Mean	14.29	5.02 4.40	1.89	2.84	0.78	0.37	1.86	31.44
	Std.Deviation 7.51	3.34 1.21	0.57	0.35	0.21	0.13	0.63	10.02

Table 4 . Annual input of Cd each grade.

 4

	grade of Cd	Annual input(mg•kg -1 )
	1	0.0005
	2	0.001
	3	0.0015
	4	0.0025
	5	0.004
	6

Acknowledgements

We are thankful that the study is supported by Geological Survey Institute of some Province, P.R. China and Chinese University of Geosciences (Beijing) which provide the research data for this paper.


Yongliang Zhang

Jianping Hu

Chunjian Zhou

Chuantong Lu

Research Throwing Soil Regular Pattern of Reversal Cultivated Land and Fertilization Seeder

Keywords: Soil particle flow, Distribution curve, Second collision, Sowing tube, Matlab

The distribution of soil particles and planting tube space position affects reversal cultivated land and fertilization seeder seeding performance.

Through the establishment of ratary tiller system coordinate system; Firstly giving the analysis of soil grain not heel cover shell collision and the first meeting with cover shell collisions motion situation, through Matlab found the first meeting with cover shell collisions will collision with spin plow knife again, and calculated separately spin plow knife in a horizontal position, tangent 45 ° direction and in vertical direction of three position of the collision curve. Findly, fitting total soil particle flow distribution curve. According to sow requirements, for agriculture combined machine seeding tube decorate location provides an important basis.

Introduction

Working principle of reversal cultivated land and fertilization seeder as shown in figure 1, is 1GHB-175 agriculture combined machine using up-cut rotary way, no open ditch the way the homework, throw the soil particles into the sowing fertilizer tube of behind to cover seeds and fertilizers. According to the soil particle distribution laws, the reasonable layout of sowing pipe line, in order to realize the deep fertilization, shallow the purpose of sowing 1.Fallow land 2. Cover shell 3. Row fertilizer tube 4. Being thrown around the soil particles 5.

Pattern of reversal cultivated land soil grain distribution calculation

Knife shaft speed for 270r/min, cover shell radius is 356mm, angle is 70. From the macro analysis, will spin tillage soil into forward throwing and backward throwing two circumstances.using calculation of back-throw ratio of soil for up-cut rotary cultivation cast soilprocess simulation program, 86.32% soil grains not with cover shell collision and then thrown back, the rest of the soil grains collision with cover shell and throw in front of the machine. In order to guarantee operation quality, must be theory discussion and analysis for the two parts are soil particles. Draw the total cast soil trajectory fitting curve, then determines the resettlement of sowing pipe line position.

2.1

No collision soil particle movement analysis soil for up-cut rotay cultivation research, when regardless of the air resistance, was thrown soil particles motion equation is:

          2 2 1 gt t v y y t v x x y o x o (2.1)
According to the condition of soil particle threw back :

2 / arctan     x y v v
Can get curve equation of soil particle flow which was not collision with the cover of shell:

56 5714 . 28 ) 40 ( 2 2     x y (2.2)
In the formulas  --The velocity v direction angle

2.2

The first meeting with cover shell collisions motion situation analysis

According to the actual size of this model, establish spin plow knife static coordinate system xoy , make the shaft center of spin plow knife as coordinates xoy origin, same as the positive direction of x axis and machine driving direction. Establish the polar coordinate of cover shell According to dynamic simulation of the collision of throwing soil with camber cover of rotary plow research:known cover shell parameters and soil particles being thrown around the initial velocity and direction angle, get after collision trajectory:

                                cos ] cos ) ( 2 sin ) )[( 1 ( sin ] cos ) ( 2 sin ) )[( 1 ( 0 2 0 2 ' ' ' y y g v v v f y y g v v v f y x S Q oy m ox Q oy m ox                             Q Q Q oy m ox Q oy m ox y t g x t y y g v v v k y y g v v v k 2 ' ' 0 2 0 2 ) ( 2 1 sin ] sin ) ( 2 cos ) [( cos ] sin ) ( 2 cos ) [(       (2.3)

Determine trajectory of soil particle flow which collision with spin plow knife again

All parameters in generation going into the type 1.3, use Matlab calculation the curve track when collision extremely sngle in 10 °, 20 °, 30, 40 °, 60 °, 50 °, ss shown in figure 3.

Fig. 3. soil grain after collision with cover shell trajectory

We know from figure 3 soil grain fell on 0.245m-0.490m between will collisions again with spin plow knife and thrown by high speed. according to the soil particle collisions cover shell movement analysis, similarly establish mathamatics grains and spin the plow knife collision coordinate system According to type 2.2, assuming soil grain evenly distributed, its distribution in between 0 and 0.8m. combining figure 5, figure 6 and figure 7 knowable, the collision happened twice almost entirely throw at the soil particles that no collided rear.

         QHy y Q QHx x Q kv v v f v
Q ( 2 Q x , 2 Q y ), according to 2 Q v for initial velocity continue to do parabola, trajectory equation for                               2 2 2 ' ' ' 2 ' ' 2 ' ' ' ' ' ' 2 1 Q Q y Q x Q y x gt t v t v y x S

Decorate sowing tub

From the analysis of soil grain not heel cover shell collision and the first meeting with cover shell collisions motion situation fitting two parts soil particle flow distribution, its distribution curve shown as shown in figure 8 Fig. 8. Throw the soil trajectory curve, sowing decorate position and throw seeds range The machine's spin plow depth is 15cm, planting depth is 1cm-3cm, so 6.7% to 20% soil cover seeds. Assuming thrown soil particles evenly distributed, according to the figure 8 can calculate the most distant soil particles is 6.32%, assuming critical point 8 shows, if the pipe line at the bottom port layout online 1

x .

The seed will fall in between 79.77cm and 83.97c, namely seeds in the soil amount covered 1.5cm and 2.0cm, achieving the desired between planting depth.

Conclusion

1. No collision soil particle movement analysis, get curve of soil particle flow which was not collision with the cover of shell.

2. The first meeting with cover shell collisions motion situation analysis, give trajectory and description.

Use Matlab calculation the first meeting with cover shell collisions motion situation, through

Matlab found the first meeting with cover shell collisions will collision with spin plow knife again. Found the collision happened twice almost entirely throw at the soil particles that no collided rear. 4. From the analysis of soil grain not heel cover shell collision and the first meeting with cover shell collisions motion situation fitting two parts soil particle flow distribution, use of distribution curve, rational arrangement of pipe line

Fig. 1 .

 1 Fig. 1. Agriculture combined machine working principle diagram

  The soil was thrown the initial position coordinates;x v y v --The soil was thrown initial velocity; g --Gravity acceleration;

  space arrangement as shown in figure2

Fig. 2 .

 2 Fig. 2. Curved shell with knife shaft rotary center position relations and collision motion analysis Assumptions are thrown soil particles point ) , (  r S and Q superposition, the coordinates in static coordinate system is S(x,y); collision point isQ, the normal and tangent pass the collision point Q and forming the Dynamic coordinate system.

2 2

 2 Fig. 4. collision analysis between soil grains and Spin plow knife In this paper, using spin plow knife rotation radius is 245mm, side blade of spin plow knife spirals radius mm R O 120  , therefore, calculate the points 2 Q location on the static coordinates xoy

  In the formula f -Collision friction coefficient 0.4 k -Collision recovery coefficient 0.4After soil particleS collisions spin plow knife in static coordinate system xoy will starting with 2

( 2 . 5 ) 7 Fig. 5 .

 2575 Fig. 5. Horizontal position soil distribution curve and spin plow knife position That from figure 5 we know that most of the soil distributed in spin plow knife rotating scope inside will collision with spin plow knife again.

Fig. 6 .

 6 Fig. 6. tangent 45 ° direction soil distribution curve and spin plow knife positionThat from figure6we know that soil particles is mainly distributed in between 0.75m and 1.3m.

Fig. 7 .

 7 Fig. 7. vertical direction soil distribution curve and spin plow knife positionThat from figure7we know that soil particles is mainly distributed in between 0.85m and 1.5m

Acknowledgements

This work was financially supported by Three Engineering of Agricultural Machinery,Jiangsu province(NJ2009-41),by Qing Lan Project of Jiangsu Province (Su teacher (2010)no.27) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (Su financial teacher(2011)no.8)。


Hong Yao

Yong-Xing Wu

A Research about the Application of Information Technology in the Precision Agriculture -Taking the Operating System of Shanghai Agriculture Economy as an Example

Keywords: precision agriculture, GIS, the Operating System of Shanghai Agricultural Economy

In the background of the digital agriculture advanced step-by-step, Shanghai government is also accelerating the pace of agricultural informationization. "Precision Agriculture" has become a pointcut of "Digital Shanghai". Building the Operating System of Shanghai Agricultural Economy is a meaningful practice in Shanghai digital agriculture engineering. Its powerful function of spatial data analysis and inquiry provides the support system for the realization of "precision agriculture". It can provide the decision support and the information service for the government.

Introduction

With the farm produce's large scale marketing and circulation coming into being, the future sustainable development of agriculture in Shanghai will, to a great extent, be decided by the restructuring and optimization of agriculture. On this background, Shanghai's agriculture has quickened the transformation from "Quantity Agriculture" to "Quality Agriculture", from planning typed into market-oriented, from suburban into modern urban agriculture. The government has accordingly adjusted its management, further stressing the macro adjustment and the function of service. The utilization of modern information technology enables the dynamic monitoring and timely appraisal of achievement and outcome of main information of Shanghai suburban agricultural economy operation, thus providing support for relevant government departments in their macro-control and management of agriculture in a timely, scientific and efficient way. Also farmers can obtain effective guiding information in their optimization of their agriculture investment, reasonable restructuring, and gaining higher yield. Obviously the application of information technology in precision agriculture is one of important measures of Shanghai Municipal Government in realizing "Precision Agriculture" for the sustainable development of agriculture.

The Definition of "Precision Agriculture"

The concept of Precision Agriculture or Precision Farming was put forward by some U.S and Canadian agriculture research institutions in the 1990s. Precision Farming is a modern farming operation and management system, supported by the information technology, and exerting a series of modern farming techniques and managing expertise according to variations of locations and operated in a fixture of location, timing and quantity. The basic content of precision farming is to adjust the investment of crops according to the condition of the soil, in which farmers, after confirming the soil condition and the mutation of the space of productivity, decide the objective of the farming. And in this way, by "Systematic diagnosis, optimized recipe, technical installation and scientific management", farmers can fully utilize the fertility of the soil, thus obtaining higher yield with the least and most economical investment without damaging the environment and obtaining economic and environmental benefit by efficiently utilizing various agricultural resources. Ten systems are needed for the technical support in the realization of the precision farming which include the GPS, Information Collection System for Farmland, Farmland Remote Sensing and Monitoring System, Farm GIS, Agricultural Expert System, Smart Farming Equipment System, Environment Monitoring System, Integration System, Network Management System and Training System. The core of the system is the establishment of a complete farmland GIS. Precision Farming can be said as a brand-new farming with the complete integration of information technology and agricultural production, the goal of which is not only for achieving economic efficiency but also for social and ecologic benefits.

3

The Present Situation and Problems in Precision Farming in Shanghai

The Present Situation

Since the beginning of the 21st century, the agriculture in Shanghai has entered a new phase of development and the traditional suburban farming, which only provides farm produce, is no longer suitable for the status of Shanghai as internationalized metropolis. At present the agriculture of Shanghai is undertaking the transformation from being quantitative to qualitative, from suburban-style to metropolis, from extensive farming to integrated farming, from production-styled to service-oriented farming. The changes of farming functioning and ways of farming require urgently the change of mode of agricultural production and management and service system as well. Precision farm has become an inevitable option for Shanghai to develop its metropolitan agriculture because the information resources for agriculture in Shanghai have become multi-factored, with the characteristic of large quantity, effectiveness of timing and broadness of respects concerned. Further developing digital management of agriculture in Shanghai will have a profound sense for pushing forward precision agriculture in Shanghai with the advantages of Shanghai's human resources of GIS, software research and development and complete infrastructure of information technology.

Advantages and Disadvantages

Shanghai has the superior technical conditions and basis in realizing the precision agriculture. With speeding up construction of "digital city" and "digital Shanghai", Shanghai Municipal Agricultural Commission (SMAC) and Shanghai Municipal Agricultural and Forestry Bureau (MAFB) accelerate the construction of digital agriculture. They developed many practical works around the digital agriculture in recent years. First, the network publishing system of agricultural information has basically completed, including building the interior local area network platform of each agriculture department, developing the intranet information management system about the vegetable planting, animal husbandry and aquaculture, and including publishing all kinds of the agricultural information on web. Second, the basic research and development on sharing the basic data and technical standard has been launched. It includes the developer guide for the application system of Shanghai agricultural GIS, the agricultural information classification and coding standard in Shanghai agricultural GIS, working out the classification standard about agriculture industry in thematic mapping and the symbols norms.

Third, some application projects have been carried out successively, including the dynamic monitoring system of non-point pollution control of farmland fertilizers and pesticides in Shanghai, the planning management information system of forestry resources in Shanghai, the GIS application system of animal husbandry management, etc. This series of practices on digital agriculture laid the foundation of the technology for achieving precision agriculture. In addition, there are some disadvantages in the construction of Shanghai precision farming. For example, the construction of network infrastructure is relatively inadequate in Shanghai suburbs. There are great differences on the Information infrastructure in different areas, lack of integrated talent in the agricultural information field. And all kinds of application systems of agricultural informationization also lack sharing mechanism or can't be integrated effectively. These problems in the development need to be considered urgently and should be solved gradually in the future process of development of Shanghai precision agriculture.

4

The Relationship between "the Operating System of Shanghai Agricultural Economy" and "Precision Agriculture"

The Operating System of shanghai Agricultural Economy is a practical project of Shanghai digital agriculture. It provides the support of technology and information for achieving precision agriculture.

The system bases on GIS, database and the network technology, and has established the farming application systems facing the management, the decision-making, the relevant departments and specific users. Because the ordinary farmers are the major producers in suburban agriculture, the system takes advantage of progressive network GIS technology and regards the retail farmers as monitoring objects.

In each plot the farmers should keep steady production. Among the plots there are objective differences. Thus, the system must follow these principles and realize digitalization in all aspects such as the agricultural production and circulation of the monitoring plots. This system constructed a dynamic professional database for the planting and economic operation. Through the comprehensive treatment and analysis, it can provide analysis and evaluation of the thematic information about the planting input-output and the dynamic monitoring data in Shanghai suburbs. It can also evaluate the operating performance of summer crops, autumn crops and crops for rotation under the different patterns, and express in space. The GIS expressive functions of spatial analysis, query and visualization not only show overall situation and spatial distribution pattern about the planting input, output and operating performance, but also show the production state of every monitoring point, various crops and every crop in different suburban, the structure of input and output, the operating performance, and the performance evaluation information in the conditions of different operating scale, different product certification and different market flow direction. All these provide scientific basis for the decision making of the agricultural department. The Operating System of Shanghai Agricultural Economy is a kind of management model of digital agriculture, which is line with the development foundation of crop production in Shanghai. After five years of operation, this system has already completed the construction and management of the dynamic monitoring network, remote data entry and the construction of professional database on the planting industry. It has exerted the certain function and guided the practical planting work. Thus, the Operating System of Shanghai Agricultural Economy is a meaningful practice in the huge digital agriculture projects of Shanghai, and provides the necessary conditions for realizing Shanghai precision agriculture.

5

Technical Framework and Workflow of "the Operating System of Shanghai Agricultural Economy"

Technical Framework of the System

The implementation of this system adopts the B/S and C/S architecture (as shown in Figure 1). It's comprised of the client (browsers), application service layer and the database server. All operation and data are deposited in server. Through the general Web browser the client can propose request of query and analysis. And the server returns the results. The client can realize the functions of data acquisition and editing, GIS inquire, statistical analysis and so on. The application service layer is the core of the system, is also a bridge between the client and the underlying database. The underlying database server includes the central database and model library. GIS platform software of this system uses the technical route combining the independent development of GIS components with commercial GIS platform. The whole framework system conforms to the standard of TC211/OpenGIS, and can carry out interoperability and data exchange with other GIS software (such as ArcGIS, MapInfo, GeoMedia) that fits this standard.

The Workflow of the System

The research of whole project contains several tasks, including the system design, the questionnaire design, the data acquisition and database design, the data input, the model design, programming and debugging, drawing up the achievements, and documentation, etc.(as shown in Figure 3)

Database Design of "the Operating System of Shanghai Agricultural Economy"

The data of this system includes the attribute data, the spatial data and special graphics library data.

The data storage and access use the technical framework of MSADO, and database system software adopts ORACLE, in order to make full use of the existing database system's functions in multi-user management, query and analysis, and safety and affairs management.

Basic Space Database

The spatial data accuracy is based on "the Shanghai Digital Agriculture Platform for Basic Data".

And the administrative division level reaches the township. The coordinate system combined the coordinates system of WGS84 (the coordinates system of GPS) with Shanghai local coordinate system.

The system supports two coordinate systems transformation and the real-time updating of the dynamic GPS monitoring points.

Service Database

According to the crop type, the crops for rotation and the administrative division, the agricultural specialized data and the related attribute data are classified and graded. It has formed a database system facing the dynamic monitoring and management of agricultural economy. The table design of the database and the relationship among the tables are shown in figure 4. The sampled data and tabulate data, in different years and quarters, are in a list. So this design is beneficial to the year to year comparisons.

Fig. 1 .Fig. 2 .

 12 Fig.1. The technical framework about "the Operating System of Shanghai Agricultural Economy"On the business logic, this system is an important component of comprehensive management information platform for Shanghai agriculture. The design of whole system based on an open system, fully considering the data statistics within the agriculture department and the data exchange with other relevant departments, and strived to achieve standardization.

Data Description (1) Static data

The static data in this system mainly includes basic geographical spatial database provided by the SMAC. It contains the, the road network and the channel network.

The data of the administrative division includes some basic layers of each district and township. It's used as the base map for the query, analysis and thematic mapping.

The data of the road network includes the layers about the main roads and streets in Shanghai. It can show the road network distribution in Shanghai suburban.

The data of the channel network includes the layers of the main rivers which are under municipal management, or under district management, or township management, etc. It's used for display the water system distribution in the suburban of Shanghai.

(2) Dynamic data According to the farming production structure and layout features in Shanghai suburban, the expected dynamic trend, and the development characteristics of the planting industry in each county, we reasonably assigned and established the crops type and the crops for rotation of the dynamic monitoring points in the initial period. The system has formed the dynamic monitoring network which covered 121 townships of Shanghai, the main crops types, and the main crops for rotation types. Each township established 10 monitoring points. There are a total of 1210 monitoring points in Shanghai suburb. In the future, if the administrative division of the townships is adjusted, the monitoring points won't be changed in principle. If a monitoring point is revoked as a result of land acquisition and other reasons, we will add a monitoring point in the same township at the beginning of next year to maintain 1210 monitoring points. We have set up the field archive for every monitoring farmer (enterprise), timely recording the farming activities. So through the agricultural service system and agricultural office system of Shanghai, the archive has formed the standardized data acquisition system.

According to the farmers` demand and the system design, the questionnaire data of "the Operating System of Shanghai Agricultural Economy" mainly includes the following respects: the total cost of production, the output, the data of fertilizer and pesticides utilization, and the other relevant data. The total cost of production includes the material costs, the service costs, the labor costs, the other costs.

The output includes the total quantity of production, the total of sale, the total value of production, and the total sales. This system has completed the design of four basic attribute databases. One is the basic survey information database of 1210 monitoring points. The second database contains two basic sub databases which are the operating system of the summer crops and the operating system of the autumn crops. The third is the annual database characterized by the crops for rotation. The last is the grading summary database of the total sown area in Shanghai suburb. The attribute database takes the monitoring point numbers for the keyword to number, and carries out a targeted data survey of the economy operation for the monitoring farmers in the whole year.

7

The Goal of "the Operating System of Shanghai Agricultural economy"

(1) Taking use of GIS technology and based on "the Shanghai Digital Agriculture Platform for Basic Data", the system will complete establishing a dynamic professional database facing the planting production and economy operation.

(2) Through the system functions of the comprehensive processing and analysis,the system will provide the dynamic monitoring data of the planting input-output in Shanghai suburb, the process analysis and evaluation of the thematic information, the performance appraisal under the different conditions, and the spatial analysis and expression.

(3) Through the functions of GIS, such as the spatial analysis inquiry and visualization, the system could analyze and evaluate the situation, and show the spatial distribution characteristics about the input-output and performance of Shanghai planting production. Aiming at each monitoring point, each crop, or each district, it can also provide the performance evaluation information about the production situation, the constitution of the input-output, and the performance under the conditions of the different production scale, the product certification and the market flow direction. The system can provide the inquiry function of the related information.

(4) Through the GIS spatial analysis and inquiry of the system, we can know the application conditions of fertilizers and pesticides in every plot. So we can timely take measures to increase the utilizing efficiency of them in order to reduce the environmental pollution.

(5) The system has the ability to generate the relevant reports, the report forms, the statistical figure, and the thematic maps. And it supports the real-time inquires query and printout, the data exchange among the systems. These functions can provide the powerful decision support and information services for the macro-control and managing the planting production in the suburb of Shanghai.

In short, "the Operating System of Shanghai Agricultural Economy", by means of monitoring the huge data dynamically, gets the relevant information of the planting timely, and evaluates the real-time conditions of the planting. It is good for advancing the scientific and precision level of the crop production management, improving the economic benefit and social benefits and environmental benefits. And the system provides the technical support for achieving precision agriculture in Shanghai.


Kaimeng Sun
email: kmsun@mail.caas.net.cn

Research on the Data Conversion Specification for Chinese Agricultural Product Quantity Safety

Keywords: specification, agricultural product, data conversion

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

1.Problem Statement

Chinese agricultural product quantity safety management system is an information management, analysis and decision-making system established for the study of agricultural product quantity safety. It involves large amounts of data related to agricultural product quantity safety, including the agricultural economic base data, field collection dynamic weather data, soil and crop growth data, agricultural product price data from internet, agricultural product quantity safety historical data for statistical analysis, etc., with different data sources and types. For overall system needs, to establish a unified and standardized database that can serve the agricultural system is the base for agricultural product quantity safety research.

The key indicators involved in agricultural product quantity safety management system are divided into two categories. The first category is production indicators: including the arable land area, acreage, production, chemical fertilizer and pesticide use, etc. of crop production. As the agricultural product quantity safety in our study includes various consumer goods needed by human life, here the agricultural product is a broad concept, and therefore the indicators also include the breeding area, quantity, meat, milk, etc. of livestock production; the forest area of forestry production, all kinds of fruit production, etc.; production environment such as soil conditions, texture, moisture, fertility, weather factors, natural disaster indicators and others; the second category is consumer indicators: including demographic, agricultural economy, all kinds of income, number of expenditures, various related indices and so on. Overall, the indicators of system show a large quantity, a wide variety of sources, complex formats and other features.

Taking agricultural economic base data as an example, data are from the Ministry of Agriculture, national statistical offices and various types of information and internet dynamic snapshots, due to the differences in database platform and type of various departments, there will be heterogeneous database problems. These databases include SQL Server, Oracle, Access, etc. As different databases have different data types, storage and data retrieval methods, how to convert and integrate data so as to access to information from the huge data resources, has become a hot topic in the current database research field, especially the data conversion of heterogeneous database has become a pressing issue now.

The study of agricultural product quantity safety management system is based on the establishment of agricultural product quantity safety-related databases, while the most important issue for establishing agricultural product quantity safety databases is the conversion of various types of data, which means in accordance with the unified format requirements of agricultural product quantity safety database, to convert different types of data into agricultural product quantity safety databases in the format required. Therefore, how to convert various sources of data into a unified data structure to enter the agricultural product quantity safety database, requires creating a data conversion specification to uniform data formats, types, and conversion methods.

Specification is defined as standards of conduct established by groups, the content of specification document is binding and regulating people's behavior in nature. To establish the data conversion specification in agricultural product quantity safetyrelated research, is to define data formats and standards of conduct for the relevant data conversion. The meaning of establishing specification is to unify the conversion method, format and process of agricultural product quantity safety data, that is, to keep access to the data of agricultural product quantity safety management system under a unified agreement, so as to ensure various types of data convert into system use in accordance with system requirements and unified steps , in order to protect the quality of the data.

The Current Main Methods of Data Conversion

Data conversion means the process of converting data structure, form of expression and file format from one into another. Taking agricultural product quantity safety database as an example, data are accessed through a variety of sources, the database structure and storage form of data are not the same, as the amount of data increaseing, the original unreasonable data structure cannot meet various requirements. The replacement of database and data structure requires the conversion of data themselves. Only by data conversion to generate data and document exchange of heterogeneous system, can data structure of database be unified in accordance with the requirements of agricultural product quantity safety research, and data be used for research to play their real role. Currently there are a variety of ways of data conversion for different information systems.

Conversion based on the timeliness of data

Based on timeliness, data conversion consists of real-time and non-real-time (delayed treatment). Real-time data conversion is a system as a data source providing data call service for another system. Receiving service conducts real-time conversion and processing of the data accessed to provide data services for the system. This real-time data conversion is usually found in the network. For example, the agricultural product quantity safety management system calling the content of a particular website that provides data services, or the system calling some of the external programs, are all real-time conversion, which requires filtering, extracting, computing, converting and sorting the data or program called from the website by definition, then adding the converted data in accordance with certain requirements of form to the database of agricultural product quantity safety management system. This processing is required to be completed within a very short time, that is, the timeliness of data access. Non-real-time data conversion refers to data that are difficult to convert and do not need to immediately convert after data extraction between two information systems, such as databases, office software, etc. Through some technical means, such as the application of some intermediary format, the data has been converted into a common data format, and after data conversion, it can be directly applied to different information systems. Or by programming, specially write a program for the two data systems, to convert data extracted from the two systems so that it can be applied to another system.

According to the conversion process, it can be divided into intermediate format conversion and direct conversion between source data and target data.

Comparing the two methods, we can find direct conversion between two different data formats is more efficient and of less negative effect (noise brought about, a small amount of data forced to discard, the degree of structure forced to reduce), which means the mapping from original format of data to target format of data will be maintained better. However, the possibility of direct conversion with ready-made means is not big, very often requiring special programming for converting from the source format to the target format.

Conversion based on different data structure

Through studying heterogeneous databases and making efforts to develop solutions to convert heterogeneous database data, we can achieve centralized management and uniform use of data in a unified environment. Heterogeneous database conversion is relatively complex in techniques, and currently it is still difficult to use a generic DBMS (Database Management System) to solve this problem. The common language specification XML (Extensible Markup Language) defines data structures in an open self-described manner, which can highlight the description of the structure while describing the data content, so as to reflect the relationship between the data, therefore is the most widely used normative language for data conversion. XML can achieve the separation of data representation with data content, allowing seamless integration of data from different sources and different processing of the same data. XML language uses XML Schema to describe the structure of the document, meanwhile XML Schema has rich data types and powerful ability to express constraints, making it feasible and convenient to take XML schema as the common data model to establish a data conversion tool between unified pattern of relationships. With the development of XML technology and XML'sadvantages in data applications such as easy to express, cross-platform, semi-structured data description, good scalability, separation between content and form, etc., favorable conditions have been provided for heterogeneous database data conversion. The heterogeneous database data conversion based on XML is not only beneficial for data sharing, making savings in software development investment, but also conducive to better management and use of data, laying the foundation for government and enterprise applications of information.

The importance of information system lies in the information exchange between different information systems. The information stored in separate information systems is not complete and single, only by exchange of information can information be enriched and completed. However, with the ubiquitous presence of information systems, their development is towards diversification and specialization. Therefore, information sharing and data and document conversion between information systems has become a subject to be solved now. Only with exchange of information, as well as timely and accurate information conversion, information is useful. A data conversion specification for agricultural product quantity safety should be established, so that various types of data conversion is in accordance with the requirements of system design, exchange of different types of information is smooth, information resources are fully utilized, in order to ensure the research work of agricultural product quantity safety be carried out smoothly.

The Functional Structure of Agricultural Product Quantity Safety Data Conversion

The functional structure of data conversion of agricultural product quantity safety management system

Based on the data sources and structure of agricultural product safety management system, through analyzing and researching, the functional structure of data conversion is established, consisting of database operation module, database source extraction module, data structure conversion module and data key-in module according to functions. The functional structure chart of data conversion is as shown in Figure 1:

Figure 1 The functional structure chart of data conversion of agricultural product safety management system

The main functional module features of data conversion:

Database operation module

This module creates different classes respectively for different databases to achieve connection between a variety of databases, data query, insert, delete, modify and other operations. When you need to add a database type, you can just add the corresponding class to improve the scalability of the tool.

Data extraction module

This module sets data extraction conditions according to the needs of data conversion to extractthe source database data as XML data files and XML structure files.

Data structure conversion module

This module implements the data structure conversion between source database and target database, including two data conversion solutions, the relocation type and the merge type, to solve the existing naming conflict, data type conflict, structural conflict and semantic conflict in two data conversion solutions, achieve multi-table conversion under complex conditions, and finally generate data structure conversion files for controlling the key-in of XML data from the source database to the target database.

Data key-in module

According to the corresponding data insert statement generated by the data structure conversion files generated by data structure conversion module, this module inserts and updates the data of XML data files generated by data extraction module from the source database to the target database. In the data key-in process, sub-data key-in can be selected according to the data file size; when sub-data key-in is selected, XML data file will first be cut into several smaller XML data files, and then each XML data file will be circularly processed to complete the key-in of XML data to the target database. Meanwhile, the data key-in module can also complete data format adjustment based on the actual situation.

The Basic Data Conversion Process of Agricultural Product Quantity Safety

The data conversion process divides data conversion into two cases: relocation-type data conversion and merge-type data conversion. The relocation-type data conversion refers that the target database is empty, just needing to completely relocate the data to be converted from the source database to the target database, which approach requires to establish the target database in full accordance with the source database structure; the merge-type data conversion means the target database already exists and there is data, needing to establish the corresponding relationship between source database structure and target database structure, inserting and updating the source database data into the target database according to the structural correspondence setting.

The conversion process chart from the source database to the target database is as shown in Figure 2.

First, data extraction, which is the starting point of data conversion, is responsible for accessing information from the source database to match the target database, specifically including:

 calling the database operation module  setting the conversion data according to needs  calling the data extraction module, to extract the setting conversion data as XML data files, and extract the structural information of conversion data as XML structure files.  extracting the target database structure as XML structure files to prepare for the conversion between the source database and target database data structure. Then comes data structure conversion, the data structure conversion between source database and target database is the core of data conversion process, which sets the corresponding relationship between source database and target database structure based on the XML structure files of source database and target database, to resolve the semantic conflict, naming conflict and type conflict in heterogeneous database data conversion, and generate data structure files to control the updating and inserting of source database data into the target database. Finally, data key-in is achieved, according to the corresponding data insert statement generated by data

The Exchange Format Design for Basic Data and Shared Data of Agricultural Product Quantity Safety

Extraction and standardization of data elements

Data source extraction

Using the logical analysis of natural language, the basic data objects of agricultural product quantity safety that need exchange format design are analyzed in terms of role, location, thing, event and time, of which the thing and event, each also contains two aspects: identification and characterization. Detailed steps are as follows:

-Make sure all relevant details of participants; -Determine the identity elements, such as: ID numbers and names fall into this category; -Make sure all the details related to the event. Event must have a date / time and a type. In general, there should be a location / position where event takes place; -Define the relevant participants. Party A and Party B are participants related to the registered event; -Define the location / position. There are three basic types of location: geographical address, physical address (latitude and longitude) and mailing address.

Standardization of data elements

As for the data elements extracted, experts in the related field of agricultural product quantity safety basic data sharing will standardize the data elements in accordance with the basic principles and methods and other relevant standards of data element standardization, so as to form reusable data elements that can directly compose of syntax-independent data exchange format.

Data classification and coding

Based on the data elements formed, code processing is needed for the data elements which require coding in accordance with the methods described in the basic principles and methods of data classification and coding and other relevant standards, so as to form the basic data classification and coding for agricultural product quantity safety, the code mapping of code-type data elements is the attribute in XML Schema.

After combing the business process, relevant information model is formed, how to convert these information models into syntax-independent, data element-composed data exchange format, can be illustrated in the following two cases: 5.2.1 Relevant data elements are readily available If a data element directory has been established in specific areas of agricultural product quantity safety basic data sharing, related data elements in the directory should be used with a priority, for the standardized processing of data in the information model, so as to form data element-based, syntax-independent data exchange format with mapping to any syntax corresponding to the information model. 5.2.2 Relevant data elements are not readily available If there is no established data element directory, or there are no reusable data elements in the established data element directory to meet the needs of data exchange, you need to extract data elements based on the information model, in order to develop a data element directory in the field. On the basis of the directory, as described in 5.2.1, further form data element-based, syntax-independent data exchange format with mapping to any syntax corresponding to the information model.

In accordance with the mapping rules provided by this specification, the data element-based, syntax-independent data exchange format is mapped to XML Schema, and finally will form XML Schema that can achieve unambiguous exchange between homogeneous or heterogeneous systems.

The composition of data exchange format

Data exchange format consists of three parts, including head of document, body of document and end of document.  Head of document includes document identification information, such as: document number, document date, role information and so on.  Body of document includes the specific business information of data exchange format, such as: the registered data content.  End of document is some descriptive information, which can be omitted sometimes.

Due to space limitations, the XML mapping rules of data exchange format are described in detail in the article.

Summary

To ensure the research of agricultural product quantity safety management system, the data accuracy, timeliness and effectiveness of database system must be ensured, for data quality determines the success or system. As already mentioned, due to large volumes of data and a wide range of data sources, data conversion is the basic work.

In order to ensure the quality of the data, it is necessary to establish a data conversion specification in line with the requirements of agricultural product quantity safety management system. This specification includes the definition of data formats, data conversion functions and data conversion processes, is one of the most important work of the system database. The same can be said that the data specification will directly affect the statistical analysis, model operations and decision-making results of the system. The data conversion specification research is a useful attempt to use modern information technology management methods in the research field of agricultural macroeconomic policy-making system, and is also an inevitable trend of the continuous development of modern agricultural information system R&D work. The development of data conversion specification itself is an important part of the research of agricultural product quantity safety management system, which is basic research. In the previous study of agricultural information technology, very few norms and standards were involved; therefore, the study of norms and standards is a new attempt. In addition to data conversion specifications, we have also studied and established data access and data processing specifications, the establishment of which not only improved the level of agricultural information technology research, but also created a better development environment for the study of agricultural information system, so that agriculture information technology research was carried out in strict norms, promoting the standardization, industrialization and modernization of agricultural information system research. It can be said that the establishment of various norms have injected new content into the development of agricultural information technology research, with such norms and standards, agricultural information technology will be able to better serve macroeconomic policy analysis, serve agricultural production and life of the peasants, as well as serve agricultural modernization in China.

Figure 2

 2 Figure 2 Data conversion process chart

Acknowledgement

This research was supported by National Scientific and Technical Supporting Programs Funded by Ministry of Science and Technology of China(2009BADA9B02-3).