Xiaohe Gu

Jingcheng Zhang

Peng Xu

Yingying Dong
email: dongys@nercita.org.cn

Yansheng Dong

Remote sensing recognition of paddy waterlogging using change vector analysis model

Keywords: Paddy Waterlogging, Change Vector Analysis, Decision Tree Classification, Remote Sensing

It is significant to monitor crop waterlogging range timely and correctly for later field management, agricultural insurance and yield prediction. The change regularity of paddy injured growth was analyzed and the sensitive parameters of growth stress were screened. The model of identifying the paddy waterlogging range based on change vector analysis (CVA) was developed by using the HJ-1/2 CCD images around waterlogging. At last the accuracy of the developed model was evaluated by in-situ sample data. Results showed that the waterlogged paddy mainly scattered around the Huaihe River system. The spatial distribution pattern was in conformity with the occurrence tendency of paddy waterlogging provided by local agricultural department as a whole. By evaluating the accuracy of the model with in-situ samples, the overall accuracy of the model developed in the study reached 87.5%, while the Kappa coefficient reached 0.737. The change vector analysis model could identify the waterlogged paddy and normal paddy correctly and efficient in Huaihe River Basin.

Introduction

Flood and waterlogging present such features as high frequency and wide range in China, which have greatly affected national economic development. The flood and waterlogging mainly locate around major rivers with rich soil and developed agriculture. According the statistics, every year more than 7.33 million hectares farmland suffered floods and waterlogging in China. It is significant to monitor crop waterlogging range timely and correctly for later field management, agricultural insurance and yield prediction. Most researches on loss assessment of flood and waterlogging mostly focused on statistical data such as population and economy, but lack of special loss assessment aiming at crop using remote sensing technology. The loss assessment of crop waterlogging not only needs to monitor the characteristics information of flood, but also needs to consider the factors such as crop variety, water-fertilizer, growing stage and soil texture. Li et al (2003) achieved damage evaluation of flood by using remote sensing and spatial economy database, including affected farmland area and affected population. Zhang et al (2007) developed the climate identifying index of drought and flood, analyzed the regularity of outbreak and its influence of crop yield. Recent research on evaluating waterlogging in relation to crop loss mostly focused on experience models, but rarely on mechanistic model [START_REF] Profeti | Flood Management through Landsat TM and ERS SAR data: a case study[END_REF][START_REF] Herath | Flood inundation modeling and loss estimation using distributed hydrologic model[END_REF]).

Because paddy field is usually flat and long-term in water, it is different to distinguish waterlogging region and natural water covering region through water spectrum change with the influence of paddy coronal. In addition, remote sensing images with appropriate resolution are hardly acquired for the cloudy weather when flood and waterlogging happens. So it is different to identify the paddy waterlogging directly using up-to-date images within the flood period. In this study the change regularity of paddy injured growth was analyzed and the sensitive parameters of growth stress were screened. By using the HJ-1/2 CCD images in the former and later periods of waterlogging, the model of monitoring the paddy waterlogging range based on change vector analysis (CVA) was developed. At last the accuracy of the developed model was evaluated by in-situ sample data.

Study area and materials

Study area

The Huaihe River Basin is chosen as study area, where locates at transverse zone of semi-humid area to humid area in the east of China. The weather caused flood in the Huaihe River Basin is very changeable and the space-time location of the precipitation is not well balanced. The average annual precipitation is 883 mm and mainly concentrates on the period from June to September [START_REF] Baogui | Analysis of precipitation and river f low in the Huaihe River basins during the summer of 2003[END_REF]. By analyzing the interannual variations, the precipitation of high flow years reaches five times more than that of dry years. The Huaihe River Basin is one of the highest frequency basins of flood in China. The variation law of summer precipitation with 2-3a cycles makes the basin suffering flood once every two or three years. In addition, because of low-lying land and inadequate ability of draining waterlogged fields, the Huaihe River Basin often holds out the situation of outside flood and inside waterlogging. For example, the flood happened in 2007 caused 1.25 million hectares of crop waterlogged in Anhui Province, including 360000 hectares of no harvest land and 44 hundred million yuan of agricultural loss. The paddy waterlogging is directly related with the weather condition, especially short-term precipitation. Near Huoqiu and Shouxian county of Huaihe River Basin suffered continuous heavy precipitation from 26 to 30 August 2002 on the occasion of mid filling stage of local paddy. According to the records of local weather stations, the precipitation reached 74.3 mm in a short period of time. The observations of local department of agriculture discovered the severe waterlogging locally for hysteretic drainage management.

Data acquisition and preprocessing

Three images of HJ-1/2 CCD with mid resolution were used in the study, of which the temporal included July 13, August 26, September 3 and September 5, 2009. The resolution and response feature of HJ-1/2 CCD image were very close to these of LandSat5 TM image, which could meet accuracy requirement of paddy identification, growth retrieval and yield loss evaluation. The covering cycle is two days with a width of 360 kilometers, of which the advantages of multi-temporal and large scale is help to monitor the crop growth.

Fig. 1. Response function of former four band between HJ CCD and TM image

The in-situ data used in the study included 42 samples surrounding Fuyang, Huainan and Bengbu city in Anhui Province, which collected from September 17 to 20 in 2009. The collecting indexes of sample parcels included GPS position, planting density and waterlogged level. The representative plant samples of three square meters were gathered, of which the yield were determined in the lab.

In addition, phenomena data in Anhui Province was used to analyze the spectrum feature of fall crops and provide prior knowledge for decision tree classification stat.

The preprocess steps of remote sensing image included geometric correction, atmospheric calibration and projection transformation. The method of atmospheric calibration used in the paper was FLAASH model. The geometric correction used the polynomial model of bilinear interpolation, which could insure the error less than 0.5 pixels. The projection type of all spatial data was Universal Transverse Mercator (UTM), while the geography coordinate system was WGS 84.

Method and technical process

Identifying spatial distribution of paddy

The decision tree classification was used to identify paddy by analyzing spectrum feature of multi-temporal HJ CCD images with in-situ sample point data. The confused crops in the study area mainly includes peanut. By consulting the former studies, the spectrum information and vegetation indexes of two temporal HJ CCD images were used to identify paddy, including the NDVI of HJ-CCD image of August 26 (named T 1 ), the reflectivity of the fourth band of August 26 image (named T 2 ), the reflectivity of the fifth band of August 26 image (named T 3 ) and the NDVI of July 13 image. The classification was carried in the Ecognition7.0.

Physiological mechanism of growth loss of paddy waterlogging

Water management of paddy field is of the utmost importance for growth and maturity of paddy. Paddy growing can not live without water during most growing periods, especially flowering stage and filling stage. Maintain reasonable water level from heading to mid filling stage is helpful for paddy field. The water caused by heavy rains is apt to flow into paddy field for the restraints of topography and terrain. On account of the limited ability of flood discharge, the paddy field is apt to suffer waterlogging, even the flag leaf and ear is submerged. When the inversion second and third leaf are submerged, paddy waterlogging is considered to be suffered. Because of the depressed photosynthesis and damaged rice embryos, the waterlogging suffered at the filling stage will reduce grain plumpness and affect the yield evidently. If the ears of paddy are submerged more than 48 hours, the grain will stop filling and make for aborted kernels probably.

The spatial distribution of paddy waterlogging in the study was identified by the change vector analysis model with the two HJ CCD images around flood. According the information provided by local agricultural department, the leaves of some submerged rice plants turned white and were apt to lodge after a week of waterlogging. These variations provided the basis for identifying spatial distribution of waterlogging paddy.

CVA model for identifying waterlogged paddy

The method of change vector analysis is widely applied for processing multi-bands images. The change vector could be used to describe the direction and amount of spectrum variety from one temporal to another temporal. Assuming the vector of pixel spectrum of temporal t 1 and t 2 represent G = ( g 1 , g 2 , …, g k ) T and H= ( h 1 , h 2 , …, h k ) T respectively, the change vector between t 1 and t 2 will be described as follows.

.

(1) The ΔG includes the all change information of two temporal image, of which the change intensity depends on the module of ΔG.

2 2 2 2 2 2 1 ) ( ) ( ) ( k k h g h g h g G          . (2)
According to the definition of change intensity, the value of G  discloses the difference of two images and the change possibility. By setting proper threshold values of G  , the changed and unchanged pixels could be distinguished. The method of change vector analysis is usually applied for identifying the pixels of land use and coverage change. The translated types of land use are depended on the direction of ΔG.

In the study, the difference of growth between waterlogged paddy and normal paddy coheres with the spectrum variation caused by waterlogging. For this reason the method of change vector analysis is introduced to identify the spatial distribution of waterlogged paddy.

Accuracy evaluation

Through the spatial overlay and analysis between in-situ samples and waterlogged paddy monitoring by remote sensing, the error matrix was established. The accuracy of change vector analysis model was evaluated with overall accuracy and Kappa coefficient.

Results analysis

On account of the degraded chlorophyll level and enlarged water information derived from submerged paddy, normalized difference vegetation index (NDVI), reflectivity of near infrared band (NIR) and greenness normal difference vegetation index (GNDVI) were input the model. The NDVI represented crop growth, which was declined with the waterlogging. The NIR and GNDVI were used to indicate water content of plant, which ascended for submerging plants. So the CVA model was transformed as followed function.

2

( ) G ST NDVI ST Nir ST GNDVI        . ( 3
)
The ST∆NDVI, ST∆Nir and ST∆GNDV indicated the differences of three normalized eigenvectors. The normalization was intended to eliminate the difference from dimension of eigenvectors and improved the comparability. The normalization function was as follows. [START_REF] Herath | Flood inundation modeling and loss estimation using distributed hydrologic model[END_REF] 1

xx ST F    . ( 4
) Here, the x 1 means the difference values of eigenvectors of every pixel. The x means the average difference values of eigenvectors of every pixel. The σ means standard deviation. The differences of three eigenvectors from two temporal images were calculated by followed function.

∆NDVI=NDVI 0826 -NDVI 0903-05 .

(5) ∆Nir=Nir 0826 -Nir 0903-05 .

(6) ∆GNDVI=GNDVI 0826 -GNDVI 0903-05 .

(7) The spatial distribution of ‖ΔG‖ was mapped as figure 2. Because two temporal images were used in the study, the change vector had only two directions, including magnifying and reducing. According to the magnifying modulus, the waterlogged paddy and normal paddy were identified by the threshold. In the study, the threshold was determined with statistical analysis of pixel values of two temporal images, which attributed to improve the generality of the method with different region and images. Because paddy submerged in small scale in study area, the mean and standard deviation value (SD) were used to set threshold through statistical analysis of paddy pixels.

G SD     * 2 Mean Threshold G .
(8) If the modulus of change vector was above the threshold, the change extent of waterlogged paddy pixels exceeded that of normal paddy pixels, that is Mean‖Δ G ‖± SD‖Δ G ‖ . Because the direction of change vector in the study was fixed and conservative, the threshold was determined as Mean±SD. The threshold setting was 5.76, which was used to map the waterlogged paddy as figure 3. The figure 3 showed that the waterlogged paddy (colored red) scattered everywhere in the study, mainly around the Huaihe River system. The waterlogging in Shouxian County was comparatively serious. The spatial distribution pattern was in conformity with the occurrence tendency of paddy waterlogging provided by local agricultural department as a whole.

By analyzing the attribute of 42 in-situ sample points, except for 2 point of nonpaddy, 25 points located in normal paddy range, while 15 points in waterlogged paddy range. Through spatial overlay and analysis of the waterlogged paddy distribution with sample data, the conversion matrix was established according to waterlogging and normal data as follows. By analyzing the actual output of 40 samples, the average value of normal paddy was 10662 kg/hm 2 , 45 percent over that of waterlogged paddy. According the t-test of independent samples, the p-value was below 0.0001, which reached extremely significant level. Results showed that paddy waterlogging reduced the local yield considerably. It also indicated that the change vector analysis model could identify the waterlogged paddy and normal paddy correctly.

Conclusion and discussion

Identifying crop waterlogging range timely and correctly is essential for field management afterwards, yield prediction and agricultural insurance. Paddy submerged at filling stage will reduce grain plumpness and affect yield evidently on account of depressed photosynthesis and damaged rice embryos. In this study the change regularity of paddy injured growth was analyzed and the sensitive parameters of growth stress were screened. By using the HJ-1/2 CCD images in the former and later periods of waterlogging, the model of identifying the paddy waterlogging range based on change vector analysis (CVA) was developed. At last the accuracy of the developed model was evaluated by in-situ sample data. Results showed that the waterlogged paddy scattered everywhere in the study, mainly around the Huaihe River system. The spatial distribution pattern was in conformity with the occurrence tendency of paddy waterlogging provided by local agricultural department as a whole. By evaluating the accuracy of the model with in-situ samples, the overall accuracy of the model developed in the study reached 87.5%, while the Kappa coefficient reached 0.737. According the t-test of independent samples, the p-value reached the extremely significant level. Results showed that paddy waterlogging reduced the local yield considerably. These results indicated that the change vector analysis model could identify the waterlogged paddy and normal paddy correctly and efficient in Huaihe River Basin.

The current study requires some further improvements. First, the model of change vector analysis need to test the efficiency of identifying waterlogged paddy in the other region. Second, because the waterlogging scale was relatively small in the study. The accuracy of the method developed in the study needs to be test in great scale of waterlogging.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Spatial distribution of G 

Table 1 .

 1 Conversion matrix derived from in-situ samples and RS identification

	In-situ samples	Remote sensing identification Waterlogged paddy Normal paddy	total
	Waterlogged paddy	13	3	16
	Normal paddy	2	22	24
	total	15	25	40

Table 2 .

 2 Yield analysis of in-situ samples of waterlogged paddy and normal paddy

	Sample type	Sample number	Average yield	standard
			(kg/hm 2 )	deviation
	Waterlogged paddy	15	5812	384
	Normal paddy	25	10662	562

Acknowledgements. This work is financially supported by the National Natural Science Foundation of China (41001199).


Yuli Chen

Hongxin Cao
email: caohongxin@hotmail.com

Yan Zhu
email: yanzhu@njau.edu.cn

Yan Liu

Weixin Zhang

The Simulation Models of Nitrogen Accumulation and Partitioning in Plant for Protected Cultivated Tomato

Keywords: protected cultivated tomato, nitrogen in plant, accumulation and partitioning, simulation models

【Objective】 In order to quantify the nitrogen accumulation and partitioning of protected cultivated tomato plant. 【 Method 】 The field experiments on cultivars and fertilizer were conducted in 2009 and 2010, and cultivars: B1, B2, and B3 were adopted.【Result】 The simulation models of nitrogen accumulation and partitioning for protected cultivated tomato plant were built by analyzing the relationship between partitioning index and product of temperature and light in accordance with the tomato growth characteristics in this paper. The models were validated using independent experiment dataset, the results showed that RMSE, X de , and R 2 between simulated and measured values of nitrogen accumulation in above-ground plant and root were 0.546 g•m -2 , 0.447 g•m -2 , and 0.956; 0.041 g•m -2 , 0.033 g•m -2 , and 0.942, respectively.

Introduction

Nitrogen is one of the necessary nutrient elements for crop growth and development.

The accumulation of nitrogen have an important effects on the chlorophyll content, leaf area, photosynthetic rate, photorespiration, solar energy use efficiency, yield, quality, and so on [START_REF] Marcelis | Simulation of biomass allocation in greenhouse-A review[END_REF] . Therefore, the systematical study for nitrogen accumulation and partitioning has an important practical significance for guiding the nitrogen application in high yield cultivation [START_REF] Liu | Rules of N assimilation, accumulation and distribution of spring wheat[END_REF] . Nowadays, the studies about nitrogen accumulation and partitioning mainly were involved in the field crop, and as a basis research for protein predicting model. Chen et al. [START_REF] Chen | Modeling plant nitrogen uptake and grain protein accumulation in rice[END_REF] , Xu et al. [START_REF] Xu | A model for predicting grain protein content in barley[END_REF] , and Shi et al. [START_REF] Shi | Modeling dynamic grain protein composition in wheat[END_REF] studied the nitrogen accumulation and partitioning of rice, barely, and wheat, respectively, and built their predicting models for protein content, respectively.

However, little research on the model of nitrogen accumulation and partitioning in plant in horticultural crops were reported. The objective of this study was to build the models of nitrogen accumulation and partitioning in plant for protected cultivated tomato based on various cultivars, and fertilizer levels field experiments.

Materials and Methods

Materials

This study used 3 tomato cultivars representing wide variation in maturing characteristics, and they are: (B1) American mole 1(100 ~ 110days), (B2) Chaoshijifanqiedawang (109~119days), and (B3) American 903(106~116days).

Methods

The experiments were conducted in plastic shed with 80 m long, 9. FN was the normal nitrogen level: compound fertilizer (N:P:K=16%:16%:17%) 750 kg• hm -2 . The planting density was 5.88 plants/m 2 , the pruning method of continuous decapitation was adopted in the experiments of spring crop and autumn crop, and 3~

4 and 2~3 clusters were left in spring crop and autumn crop, respectively.

Data Collection

After planting, the representative samples were taken every 7d from seedling to flowering and every 14d during fruit period. Three representative plants selected in each treatment were separated into parts after purification and dried by the filter paper, dried in 30 min. at 105℃, then, at 80℃ until reaching a constant weight, measured using a 0.001 g electro-level. Then, the nitrogen contents of various organs were measured by using the micro-kjeldahl method. HOBO-H8 was placed in three different positions in the plastic shed to collect environment elements automatically every 10 s, including air temperature, relative humidity and dew point, the absolute humidity, and light intensity, etc. In that average values every 15 minutes were recorded.

Model Verification

In this study, Excel.2007 and SPSS V 16.0 were used to analysis experimental data.

The experiment data in 2009 were applied to model establishment and parameter determination, and the experiment data in 2010 were applied to model verification.

The model precision was verified using root mean squared error (RMSE), mean absolute error (X de ), the determined coefficient (R 2 ), and 1:1 plotting between measured values and simulated values. The calculation formula of RMSE and Xde can be expressed as follows:

where OBS i is measured values, SIM i is simulated values, de is absolute error, and n is sample numbers.

Results and Analysis

Models Description

The Determination of Product of Temperature and Light (E)

According to the multi factor effect principle [START_REF] Chen | Research on tomato fruit development and yield formation model under facility condition[END_REF] , the effect of temperature and light could be expressed by the product of temperature and light (E):

(

) 1
where E is product of temperature and light, T is effective accumulative temperature, and P is the accumulated light intensity which higher than light compensation point for tomato. T could be expressed as follows:

(2)

(3

)
where T mean is day mean temperature, T b is minimum temperature for tomato growth, and the value is 15℃ [START_REF] Ni | Simulation of leaf area and dry matter production in greenhouse tomato[END_REF] in all development stages, besides the seedling stage is 10℃ [START_REF] Chen | Research on tomato fruit development and yield formation model under facility condition[END_REF] , and T 1 , T 2 , T 3 , •••, and T 24 is the plastic shed temperature from 01:00 to 24:00, respectively.

Referring the definition of effective accumulative temperature, P could be expressed as follows:

(4)

(5

)
where P mean is day mean light intensity, P b is light compensation point for tomato, and the value is 2klx [START_REF]Vegetable cultivation to learn each theory (north this) the third edition[END_REF] , and P 1 , P 2 , P 3 , •••, and Pm is the plastic shed light intensity of integer point time from sunrise to sunset, m is the number of integer point time, and the time of sunrise and sunset were computed by the method of Jing et al. [START_REF] Jing | Implementation of the times for sunrise and sunset algorithm in urban streetlight supervisory control system[END_REF] . The other symbols were the same as above.

The Models of Nitrogen Accumulation for Protected Cultivated Tomato

Plant

The changes in nitrogen accumulation with the dry matter accumulation in protected cultivated tomato plant were a Logistic curve in accordance with the data of 2009 (Fig. 1).

where NB E is the total nitrogen accumulation in protected cultivated tomato plant at E in g•m -2 ; NB max is the latent total nitrogen accumulation in protected cultivated tomato plant in g•m -2 , and it is a model parameter; DW E is the total dry matter accumulation in protected cultivated tomato plant at E in g•m -2 ; and A, k, and B are model parameters (table 1). Note: The asterisk "*", and "**" denoted the significant level at P<0.05, and P<0.01, respectively. The same below.

3.1.3

The Models of Nitrogen Partitioning in Protected Cultivated Tomato Plant

The Simulation of Nitrogen Partitioning of Above-ground Plant and Root in Protected Cultivated Tomato Plant

The nitrogen partitioning index to above-ground plant (root) in protected cultivated tomato plant could be expressed as follows:

(7) ( 8
)
where NPIS is the nitrogen partitioning index in above-ground plant, NPIR is the nitrogen partitioning index in root, NWSH is the nitrogen accumulation of above-ground plant in g•m -2 , and NB is the total nitrogen accumulation in plant in

g•m -2 .
According to the relationship between the nitrogen partitioning index in above-ground plant or root and E (Fig. 2), the trend could be expressed as follows:

(9) ( 10
)
where NPIS E and NPIR E is the nitrogen partitioning index in above-ground plant and root at E, respectively. NPIS max is the latent nitrogen partitioning index in above-ground plant, and it is a model parameter. A, k, and B are model parameters (table 1).

The Simulation of Nitrogen Partitioning for Different Organs of Above-ground Plant

The partitioning index of nitrogen in various organs (leaf, stem, and fruit) could be expressed as follows:

(11)

(12) ( 13
)
where NPIL, NPIST, and NPIF is the nitrogen partitioning index leaf, stem, and fruit, respectively. NWL, and NWST is the nitrogen accumulation of leaf, and stem in g•m -2 , respectively. NWSH is the total nitrogen accumulation of above-ground plant in

g•m -2 .
The data of 2009 showed: the leaf nitrogen partitioning index decreased with the adding of E; the stem nitrogen partitioning index increased firstly, and then decreased with the adding of E; and the fruit nitrogen partitioning index increased with the adding of E (Fig. 3). According to the relationship between nitrogen partitioning index of leaf, stem, and fruit and E, and the trends could be expressed as follows:

(14) (15)
(16

)
where NPIL E is the leaf nitrogen partitioning index at E, NPIST E is the stem nitrogen partitioning index at E, NPIF E is the fruit nitrogen partitioning index at E, NPIL max and NPIST max is the latent nitrogen partitioning index of leaf and stem, respectively, and it is a model parameter. A, k, and B are model parameters (table 1).

The Simulation of Nitrogen Accumulation for Different Parts in Protected Cultivated Tomato Plant

The nitrogen accumulation of different parts of above-ground plant could be expressed as follows:

(17) (18) (19) (20)
(21

)
where NB E , NWSH E , NWR E , NWL E , NWST E , and NWF E are the nitrogen accumulation of total plant, above-ground plant, root, leaf, stem, and fruit in g•m -2 , respectively. NPISH E , NPIR E , NPIL E , NPIST E , and NPIF E are the nitrogen partitioning index of above-ground plant, root, leaf, stem, and fruit, respectively.

Model Verification

The Model Verification of Nitrogen Accumulation in Above-ground

Plant and Root for Protected Cultivated Tomato

The model of nitrogen accumulation in above-ground plant and root for protected cultivated tomato were verified by independent data in 2010 (Fig. 4). The figure 4 showed: RMSE, X de , and R 2 of between measured and simulated values of nitrogen accumulation in above-ground plant and root for protected cultivated tomato were 0.546g•m -2 (n=36), 0.447g•m -2 , and 0.956; 0.0421g•m -2 (n=36), 0.033g•m -2 , and 0.942, respectively. The correlation coefficients (r) were 0.978 (r 0.01,34 =0.424) and 0.971 (r 0.01,34 =0.424) with 0.01 significant level, respectively.

3.2.2

The Model Verification of Nitrogen Accumulation in Leaf, Stem, and

Fruit for Protected Cultivated Tomato

The model of nitrogen accumulation in leaf, stem, and fruit for protected cultivated tomato were verified by independent data in 2010 (Fig. 5). The figure 5

Discussion

The amounts of nitrogen accumulation in leaf, and stem of protected cultivated tomato had a tendency to increase before topping. After topping, the amounts of nitrogen accumulation in leaf, and stem changed into little, but it increased rapidly in fruit, and roots absorbing nitrogen had little changes in the amounts of nitrogen accumulation during the total growth and development processes (Fig. 6). Therefore, we could not solve the nitrogen transfer in different parts of protected cultivated tomato plant by using the thought that applied to the field crop. The nitrogen contents measured in different organs were used as the nitrogen accumulation, and the nitrogen transfer in different organs for protected cultivated tomato wasn't be studied in detail in the paper. In order to improve the applicability, accuracy, and precision of the model, the water impact factor should be considered in the model in the further studies.

Conclusions

The simulation models of nitrogen accumulation and partitioning for protected cultivated tomato were built by analyzing the relationship between nitrogen accumulation, partitioning, and biomass and E in accordance with the field experiment data of protected cultivated tomato in 2009 and 2010. These models were verified by using independent experiment data, the statistical analysis (RMSE, X de , R 2 , and r) and 1:1 diagram.

Fig. 1 .Fig. 2 .

 12 Fig.1. The relationship between the plant nitrogen accumulation and dry matter

Fig. 3 .Fig. 6 .

 36 Fig.3. The relationship between nitrogen partitioning index of stem, leaf, and fruit and E

  showed: RMSE, X de , and R 2 of between measured and simulated values for protected cultivated tomato leaf, stem, and fruit nitrogen accumulation were 0.165g•m -2 (n=36), 0.132g•m -2 , and 0.839; 0.146g•m -2 (n=36), 0.099g•m -2 , and 0.945; 0.246g•m -2 (n=36), 0.166g•m -2 , and 0.845, respectively. The r values were 0.916 (r 0.01,34 =0.424), 0.972 (r 0.01,34 =0.424) and 0.919 (r 0.01,34 =0.424) with 0.01 significant level, respectively.

Fig. 4 .Fig. 5 .

 45 Fig.4. Comparison of simulated with observed nitrogen accumulation values of above-ground plant and root

Table 1 . Analysis of variance for all models and its coefficient test

 1

	Models	Correlation	F	A	k	B
		coefficient
	Nitrogen contents	0.880 **	96.847 **	0.008 **	0.005 **	322.688 **
	Above-ground plant nitrogen	0.881 **	120.973 **	3.3396 **	0.146 **	0.112 **
	partitioning index Stem nitrogen partitioning index(1)	0.887 **	42.985 **	1.787×10 5**	0.894 **	0.106 **
	Stem nitrogen partitioning index(2)	0.967 **	64.083 **	0.523 **	-5.046 **	5.198 **
	Leaf nitrogen partitioning index	0.952 **	30.859 **	3.661×10 -7**	-1.389 **	0.066 **

Fund of China (CX (10)221).


Hui Liu

Zhijun Meng
email: mengzj@nercita.org.cn

Hua Wang

Min Xu

{hui Liu

Min Xu}

Systematic Random Deployment for Wireless Sensor Network in Agricultural Sampling-Interpolation Applications

Keywords: Precision farming, Wireless sensor network, Sensor nodes deployment

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

Introduction

Precision farming demands soil, microclimate, crop and other field data for spatial and temporal variability analysis [START_REF] Zhang | Precision Agriculture-a worldwide overview[END_REF] . But limited samples could not explain exactly the variability and support farming decision. The application of Wireless Sensor Network (WSN) technology could provide the optimal and integrated solution for real-time data acquisition, reliable transport, aggregation, in-network processing and statistics [START_REF] Wang | Wireless sensors in agriculture and food industry-Resent development and future perspective[END_REF] . The potential benefits of using the WSN technology resulted in the appearance of a large number of academic and industrial R&D projects in agriculture. Lofar Agro project resulted in an experimental WSN installation in a potato field. The purpose of the WSN is to measure the microclimate factors essential to resist the phytophtora disease [START_REF] Baggio | Wireless sensor networks in precision agriculture[END_REF] . Camalie vineyard currently had an advanced soil moisture monitoring system named Camalie Net. It used twenty Mica2dot motes of Crossbow Inc. to establish the network. These motes sent data of temperature, soil moisture and soil temperature to a central computer once every 10 minutes [START_REF]Camalie Net: Wireless sensor network at Camalie Vineyard[END_REF] . It is obvious that wireless sensor networks could act as a distributed sampling system in precision farming applications, which aim at estimating the spatiotemporal variability.

Sensor nodes deployment is one of the primary issues for any WSN application. Most researches on deployment concentrate on event detection applications, which aim to detect when and where particular event takes place such as the start of a fire. The communication range and the sensing range are introduced to design the deployment in such applications. However, WSNs in precision farming are mostly kind of continuous sampling-interpolation applications different from event detection. The goal of the network is to carry out continuous spatiotemporal sampling and build field data distribution map by interpolating the values of the field where no sensor reading exist. The sensing range is largely meaningless in this case unlike in the event detection class of sensor networks. In this paper, the sampling range is proposed for WSN deployment instead of the sensing range. A kind of systematic random deployment based on regular patterns to achieve both coverage and connectivity is discussed.

Systematic Random Deployment

Wireless sensor nodes could be generally placed in three methods including random deployment, regular deployment and customized deployment [START_REF] Tilak | Infrastructure tradeoffs for sensor networks[END_REF] . Taking the agricultural scenario, sensors could be mostly possible installed during terrain preparation. It is a good choice for sensor node deployment referring to traditional sampling methods.

The most commonly used sampling design for spatial data is systematic sampling in agricultural experiments because it is easy to implement. A Square grid, an equilateral triangle grid or a regular hexagonal grid is commonly chosen in systematic sampling. Liu et al. show how to deploy sensor nodes in regular patterns in farmland environmental monitoring applications [START_REF] Liu | Sensor Nodes Placement for Farmland Envionment Monitoring Applications[END_REF] . But Systematic sampling does have a few disadvantages for spatial correlation analysis. There is a danger that the systematic grid might coincide with a systematic feature of the landscape, especially in man made landscapes. Systematic random sampling is another type of sampling spreading the data in space in a similar fashion as systematic sampling. One sampling unit is selected at random in each grid cell. It is more efficient for spatially correlated data than systematic sampling and simple random sampling.

Consequently we present systematic random deployment approach for wireless sensor network in agricultural sampling-interpolation applications. However, it is different from pure sampling that the network connectivity has to be maintained in the sensor nodes deployment at the same time.

Firstly, some definitions and the model description in this paper are as follows.

Definition 1: Let {x 0 ,…,xn-1} be the locations of n sensor nodes spread over a 2-D monitoring area.

Definition 2:

Let there be m environmental variables to be sampled by each sensor node in the monitoring network.

Definition 3: Each environmental variable has the spatial correlation. It has a sampling range r i s , which could be determined according to historical data, experience results or some literature. r s is defined as the sampling range for the whole network. Then

) min( i s s r r  m i , , 1   (1)
Definition 4: Each active node has a communication range of r c , and two active nodes can communicate reliably at a distance of r c or less. In addition, all nodes have the same communication ranges.

Systematic random deployment is designed based on three popular regular patterns including an equilateral triangle pattern, a square pattern and a regular hexagonal pattern (shown in Fig. 1). We need to address the following two questions. How to determine the optimal grid size for these systematic random deployment patterns? (2) Which is the best pattern on the efficiency among these systematic random deployment patterns? We explained the first question in Section 3 and the second in Section 4. To achieve connectivity, the communication range r c must be also considered. In Fig. 1a, x 0 is the sensor node with three neighboring nodes x 1 , x 2 ,x 3 . Let x 0 locate in △ABC. Considering connectivity requirement, at least one of the respective distances from x 0 to x 1 ,x 2 ,x 3 is less than r c . Hence, we must have

          c T c T c r l r l r

Efficiency Comparison of Different Deployment Patterns

Which is the best pattern on the efficiency among these systematic random deployment patterns? For the same field, the number of sensor nodes could be calculated using the area of grid cell. The bigger the grid cell area, the fewer required nodes. A T max ,A S max ,A H max are denoted the maximum area of grid cell in equilateral triangle, square and hexagonal patterns respectively. Then

  2 max max 4 3 T T l A  (9)   2 max max S S l A  (10)   2 max max 2 3 3 H H l A  (11)
Using ( 6) and (9), then 12), ( 13) and ( 14), the maximum area A max of the corresponding patterns is computed for all values of r c /r s . Fig. 2 shows the results from which we make the following observations. When r c /r s ≤1.755, the square based pattern to achieve both coverage and 1-connectivity is optimal because it has the maximum grid area to ensure the best efficiency. While requiring more connectivity, the hexagon pattern which provides coverage and 3-connectivity should be chosen for this range of r c /r s . 2. When 1.755 ≤ r c /r s ≤ 2.081, the triangle based pattern to achieve both coverage and 1-connectivity is optimal because it has the maximum grid area to ensure the best efficiency. If requiring more connectivity, the hexagon pattern which provides coverage and 3-connectivity should be chosen for this range of r c /r s . 3. When 2.081 ≤ r c /r s ≤ 2.632, the triangle based pattern to achieve both coverage and 1-connectivity is optimal because it has the maximum grid area to ensure the best efficiency. If requiring more connectivity, the square pattern which provides coverage and 4-connectivity should be chosen for this range of r c /r s . 4. For r c /r s ≥ 3, the triangle based pattern to achieve both coverage and 3-connectivity is optimal because it has the maximum grid area to ensure the best efficiency.

                        2 2 2 2 max ) , 3 min( 12 3 )
                                   

Conclusion

In this paper we discussed the problem on the sensor nodes deployment in agricultural sampling-interpolation applications. We proposed systematic random deployment approach to achieve coverage and k-connectivity based on three popular regular patterns-equilateral triangle, square and hexagon. We also compared the efficiency of different regular patterns of systematic random deployment, which enabling to make a better deployment decision for different values of r c /r s .

Fig. 1

 1 Fig. 1 Systematic random deployment

  max denote the maximum grid side length of the equilateral triangle pattern. Using (2) and (5), then be used for the other two patterns. Let l S max , l H max denote the maximum grid side length of the square and hexagon pattern respectively. Hence, we obtain

  be used for the other two patterns. Then, we obtain

Fig. 2

 2 Fig. 2 Efficiency Comparison of different deployment patterns 1. When r c /r s ≤1.755, the square based pattern to achieve both coverage and

Corresponding Author: Zhijun Meng

Acknowledgement

This project is supported by National Nature Science Foundation (31101078), Beijing Natural Science Foundation (Connectivity and coverage for wireless sensor network in agricultural applications, 4112013) and Scientific Research Common Program of Beijing Municipal Commission of Education (KM201110028018).


Xue-Chun Wang
email: xuechunwang@swust.edu.cn

Jun Li

Shishun Tao

Using EPIC model to determine a sustainable potato/cereal cropping system in the arid region of the Loess Plateau of China

Keywords: The Loess Plateau, Arid region, Potato, Rotation, EPIC model

Potato has become one of the primary cash crops in the arid region of the Loess Plateau.

However, what and how cereal crops to rotate with potato is better for the farmer's income and environment is unclear now. 11 potato based rotation systems were simulated in Yulin, using EPIC model based on local information. Results showed 1) Though mean potato yield decreased from 6.84 to 5.38 t/hm 2 , the mean value of farmer's gross income increased from 12807 to 20185 Yuan/hm 2 , when the frequency of potato increased from 0.25 to 0.50 for potato based rotation sequence. 2) By comparing, spring maize was better to increase potato yield, while it used more water than foxtail millet and broomcorn millet; Foxtail millet was one of the better preceding crops for potato, and its effects on potato yield was depended on its sequence in potato based rotation system. 3) Comparing with spring maize/potato system, foxtail millet/potato and spring maize/potato/foxtail millet/potato were two better rotation systems to use soil water sustainably and to increase farmer's gross income for a long period in the arid region of the Loess Plateau.

Introduction

Potato has become one of the primary cash crops in the arid regions of the Loess Plateau. As a shallow root crop, potato uses less water than other deep root crops [START_REF] Wang | Evaluation of crop yield and soil water estimates using the EPIC model for the Loess Plateau of China[END_REF]. This character increased farmer's interest to plant potato in the arid areas (annual rainfall was not more than 450mm) of the Loess Plateau. Another reason is the great difference of temperature (10-16℃) between day and night is better to accumulate more dry material for potato in the north region of the Loess Plateau. According statistical data [START_REF]Statistical yearbook of Shaanxi Province of 2011[END_REF], potato has been planted more than 2.4×10 5 hm 2 (33% of the grain crop planting area), and contributed about 26.7% of total agricultural cash receipts in Shaaxi Province of China.

However agricultural technology of potato production still needs to improve in the arid region of the Loess Plateau. Liu [START_REF] Liu | Effects of different alfalfa-crop rotation patterns on water recovery of degradation alfalfa grassland[END_REF] and Li [START_REF] Li | Simulation of autumn grain crop productive potential on Loess Plateau region of China[END_REF] reported that potential yield of potato in northern Shaanxi was more than 15 t/hm 2 ), however actual potato yield in the field was less than 5.9 t/hm 2 [START_REF] Wang | Effects of additional water and increased soil temperature on potato yields in Plastic-covered Ridge Rainwater-harvesting system[END_REF]. Most reports believed that rotating with cereal crops was one of the better agricultural technologies to increase potato yield [START_REF] Jatoe | Economic and environmental impacts of introducing land use policies and rotations on Prince Edward Island potato farms[END_REF]. Over the years, increasing costs of acquiring suitable agricultural land and irrigation equipment promoted many farmers to limit rotation of other crops with potatoes. Now, spring maize/potato and broomcorn millet/potato have become the primary potato based agricultural systems in the arid region of the Loess Plateau. However If the present potato-grain rotation system was reasonable, what crop and what sequence of such crop is reasonable and sustainable are still not clear in the arid region of the Loess Plateau.

Field experiment is a better and direct method to determine a sustainable potato-cereal rotation sequence. However, it is a long-term endeavor that is both expensive and time-consuming. With the development of crop model, using computer model to simulate soil water content and crop yield based on local information has become another better approach to answer this question. One such model is the EPIC model which simulates soil water and crop yield simultaneously under the help of its two sub models (growth model and hydrology model). Wang and Li reported that EPIC model can estimate soil water and crop yield well with the new database for the Loess Plateau [START_REF] Wang | Evaluation of crop yield and soil water estimates using the EPIC model for the Loess Plateau of China[END_REF].

Objectives of this paper were to determine a sustainable potato/cereal cropping system in the arid region of the Loess Plateau, and to point out some advices for farmers in the homogeneous areas of the world.

Material and method

Site description

Yulin (107.5~111.3° E, 36.9~39.6° N), as a northern city of Shaanxi Province in China, is located at the junction of the Loess Plateau and the Mu Us Desert. It is an arid continental monsoon climate zone and is a representative rain-fed area of China. Its mean annual precipitation is 400mm, with the mean annual temperature of 10℃, and the mean frost-free period of 152 d. Its predominant soil type, used for potato, is Miansha soil.

Rotation sequence

11 potato based cropping systems (tale 1) were established based on the following information: 1) Potato was one of the primary cash crops for farmers in Yulin. 2) Foxtail millet and broomcorn millet were the main crops used to build rotation sequence in the arid region of the Loess Plateau. 3) Spring maize was the primary crop to produce food for farmers. Where spring maize/potato and broomcorn millet/potato were two widely practiced potato based agricultural system in Yulin.

Soil moisture grade

In this research, based on Soil Desiccation Index (SDI) [START_REF] Li | Effects of deep soil desiccation on artificial forestlands in different vegetation zones on the Loess Plateau of China[END_REF], soil moisture were classified into 6 grades (table 2). To build up a decision table for soil moisture grade, the range of soil water content for each soil moisture grade was calculated using equation 1.

SWW

) SWW SSM SDI MSW     ( (1)
Where MSW is Soil Water Content calculated from SDI, SSM and SWW. SDI is Soil Desiccation Index, SWW is Soil Water content at Wilting point, and SSM is Stable Soil Water content.

Potato yield and gross income

According calculating method used by State Statistics Bureau of China [START_REF]Statistical yearbook of Shaanxi Province of 2011[END_REF], potato yield was calculated into grain yield using equation 2.

Y pc =0.2×Y p (2)
Where Y pc was the potato yield calculated from fresh tuber yield of potato, Y p was the fresh tuber yield of potato.

Gross income for different cropping system was calculated by equation 3 based on the average price of spring maize, broomcorn millet, foxtail millet and fresh potato from

2001 to 2010.      T 1 t I 1 i ) ( GI it it i C Y P (3)
Where GI was the gross income, P i was the average price of crop i from 2001 to 2010; Y it was the yield of crop i in year t; C it was the variable costs associated with crop i in year t. Variable costs of different crops included expense for seed, plowing, fertilizer, seeding, operating machinery.

Effects of preceding crop on potato yield were calculated using equation 4.

    N 1 n pc pc it N 1 i Y Y - YP E (4)
Where N was the times of potato planted after crop i during 1965 to 2000. YP it was the potato yield after crop i in year t. Y pc was the actual mean potato yield in Yulin from 2008 to 2010 (5.9 t/hm 2 ).

Results

Crop yield for different rotation cropping systems

Taking mean yield and gross income into account, MMMP and MFMP were better rotation cropping system within group A . Mean potato yield of MFMP reduced 0.52 t/hm 2 comparing with that of MMMP and increased 0.14 and 1.20 t/hm 2 comparing with that of MBMP and FMBP. Mean maize yield of MFMP was 0.45 t/hm 2 higher than that of FMBP, and was 0.10 and 0.05 t/hm 2 lower than MMMP and MBMP. Mean broomcorn millet yield of MFMP increased 0.46t/hm 2 , comparing with that of FMBP. From 1965 to 2000, farmers got more gross income by MFMP rotation system, which increased 9.0% and 16.6% comparing with that by MBMP and FMBP. For MFMP rotation system, 8.4% of gross income produced by potato, which was higher than that of MMMP (6.0%) and MBMP (6.6%) rotation system and was lower than that of FMBP (9.5%).

MMP and MFP generated more yield and more gross income, comparing with other rotation systems within group B. MMP rotation system produced the highest yield of maize (1.66 t/hm 2 ) and potato (6.89 t/hm 2 ). From 1965 to 2000, MMP cropping system generated the highest gross income of 16026 Yuan/hm 2 , MFP was the second with the value of 15321 Yuan/hm 2 . For MMP rotation system, potato contributed 89.7% of the gross income, which was significantly higher than that for MFP (84.7%).

Within group C, MPFP rotation system got the highest yield of maize (1.56 t/hm 2 ) , foxtail millet (1.89 t/hm 2 ) and potato (6.00 t/hm 2 ). From 1965 to 2000, gross income of spring maize, foxtail millet and potato in MPFP cropping system was 580, 1035, 18782 Yuan/hm 2 respectively, which was significantly higher than that in MP and FP cropping system. For MPFP cropping system, potato contributed 92.1% of the gross income, which was significantly higher than that for MP (94.8%) and was significantly lower than that for FP (90.0%).

Effect of preceding crop on potato yield

Effect of preceding crop on potato yield was affected by crop species and its rotation sequence in potato based cropping system (table 3). Effect index of spring maize on potato yield was the highest in MMMP rotation system, while was the lowest in MP rotation system. MFP was the best cropping system with the effect index value of 0.054 for foxtail millet to increase potato yield, however, effect index of foxtail millet in FP indicated that foxtail millet had a negative impacts on the production of potato yield. By comparing among all the simulated crops, as a preceding crop, spring maize was better to increase potato yield, while broomcorn millet has a negative impacts on potato yield. respectively. FP was the best cropping system to use soil water sustainably, with the lowest reducing of soil water from 1965 to 2000. FBP was the second and FMBP was the third rotation system, considering the sustainable use of soil water.

Soil water in different soil layers for different cropping systems

Steady dry soil layers built in 2.0-4.0m soil, and soil moisture changed with the change of rotation sequence and rainfall in different years. Steady dry soil layers built in 2.0-5.0 m soil for MMMP, MMP and MBMP cropping system, in 2.0-3.0 m soil for FP cropping system, in 2.0-4.0m soil for other cropping systems (table 4). Crop specie was an important factor to impact the change of soil water for potato/cereal rotation system. Simulation results showed the depletion of soil water in spring maize field was maximum, in broomcorn millet field was the second, in foxtail millet field and potato field was the following. Soil water reduced in 0-1.5m soil when potato or foxtail millet was planted, and it reduced in 0-2m soil when spring maize was planted.

Rain fall was another factor to influence the changing of soil water in potato based rotation field. When annual rainfall was less than 200mm, dry soil layer developed into the deeper soil both in spring maize field and in broomcorn millet field. When annual rainfall was more than 400mm, soil water recovered in 0-1.5m soil both in foxtail millet field and in potato field. When annual rainfall was more than 500mm, soil water recovered in 0-1.0m soil both in spring maize field and in broomcorn millet field.

Discussion

Rotation system can help break pest cycles, reduce pesticide use, and help control weeds [START_REF] Edward | Compost, rapeseed rotation and bio-control agents significantly impact soil microbial communities in organic and conventional potato production systems[END_REF]. In addition, rotation systems that include cereals can help improve soil organic matter level, thereby improve soil structure and productivity [START_REF] Nelson | Assessment of changes in soil health throughout organic potato rotation sequences[END_REF]. Results of this paper showed potato yield increased with the reduction of potato frequency in rotation system. Mean potato yield of group A was the highest with the value of 6.84t/hm Depth and distribution character of root in soil results the different capacity of different crops to use soil water, and therefore results the different ability of different potato based rotation system to use soil water [START_REF] Xiang | Effects of plant growth regulators on physiological and biochemical characteristics of potato roots[END_REF]. Results of simulated soil water showed, within group B, available soil water of FBP was the highest, with the value of 1073mm; MFP was the second (1010mm), and MBP was the third (1008mm), MMP was the lowest (981mm). Considering only from sustainable use of soil water, foxtail millet was the best crop to rotate with potato, and Broomcorn millet was the second. However table 3 showed, as a preceding crop, spring maize was the best crop, and foxtail millet was the second to increase potato yield. This may be a result of nutrition cycle, pest cycle and fungi development from preceding crops to potato within a rotation sequence [START_REF] Peters | Developing disease-suppressive soils through crop rotation and tillage management practices[END_REF].

To determine whether one rotation system was a best and sustainable cropping system, economic factor and environmental factor must be considered simultaneously. Soil water, was one of the key environmental factors to influence the sustainable development of agriculture in the arid regions of the Loess Plateau [START_REF] Chen | The effect of land cover/vegetation on soil water dynamic in the hilly area of the loess plateau, China[END_REF]. Jatoe et al. believed that rotation length, rotation sequence and crop specie together results the effect of rotation on environment and famer's income of potato based agricultural system [START_REF] Jatoe | Economic and environmental impacts of introducing land use policies and rotations on Prince Edward Island potato farms[END_REF]. Results of this paper showed, 1) FP and MPFP rotation system was better to produce more food for the farmer; 2) MP, FP and MPFP was better to increase farmer's gross income; 3) MP, FP, MPFP and FBP was better to utilize soil water sustainably. Considering from food production, sustainable utilization of soil water and gross income together, FP and MPFP rotation system was the better potato based rotation system for famers in the arid region of the Loess Plateau.

Conclusion

1) By comparing, spring maize was better to increase potato yield, however it used more water than foxtail millet and broomcorn millet; Foxtail millet was better to sustainable use of soil water, however its impacts on potato yield were depended on its sequence in potato based rotation system.

2) Spring maize/potato was not a sustainable potato based rotation system in the arid region of the Loess Plateau, foxtail millet/potato and spring maize/potato/foxtail millet/potato were two better rotation systems to use soil water sustainably and to increase farmer's gross income for a long period.

Table 1

 1 Information for the simulated potato based rotation system in Yulin

	group	Rotation system	Rotation sequence
		M/M/M/P	spring maize / spring maize / spring maize /potato
	A	M/B/M/P F/M/B/P	spring maize / broomcorn millet/ spring maize /potato foxtail millet/ spring maize/ broomcorn millet/ potato
		M/F/M/P	spring maize/foxtail millet/ spring maize/potato
		M/M/P	spring maize / spring maize /potato
	B	M/F/P M/B/P	spring maize / foxtail millet /potato spring maize /broomcorn millet/potato
		F/B/P	foxtail millet/broomcorn millet/ potato
		M/P	spring maize /potato
	C	F/P	foxtail millet /potato
		M/P/F/P	spring maize/ potato / foxtail millet /potato
	1.3 Method

Table 2

 2 Decision table of soil moisture grade for Miansha soil in Yulin

	Soil moisture grade	Soil water content by weight(SM) (%)	Soil Desiccation Index (SDI)
	Ⅰ	SM≥14.0	SDI≥1.00
	Ⅱ	14.0>SM≥11.5	1.00>SDI≥ 0.75
	Ⅲ	12.5>SM≥11.0	0.75>SDI≥0.50
	Ⅳ	11.0>SM≥9.5	0.50>SDI≥ 0.25
	Ⅴ	9.5>SM≥8.0	0.25>SDI≥0.00
	Ⅵ	SM<8.0	SDI<0.00

Table 3

 3 Effects of preceding crops on potato yield in different rotation systems in Yulin

	Preceding crops	Rotation sequence	Effect index of preceding crop
		MMMP	0.286
		MFMP	0.198
	Spring maize	MMP	0.168
		MBMP	0.154
		MP	-0.003
		FMBP	-0.005
	Broomcorn millet	FBP	-0.034
		MBP	-0.049
		MFP	0.054
	Foxtail millet	MPFP	0.017
		FP	-0.024
	2

.3 Soil water for different cropping systems 2.3.1 Available soil water for different cropping systems

	FMP, MBP and FBP respectively; 200, 47 and 171mm for MP, FP and MPFP
	Different cropping systems have different capacity to use soil water in depth and
	extension, therefore, the difference of available soil water between different rotation
	system was significant. Mean available soil water in 0-10m soil were 1016, 1018 and
	1034mm for group A, group B and group C respectively. FMBP, FMP and FP was the
	best rotation system within group A, group B and group C respectively, with the mean
	available soil water of 1039, 1073 and 1072 mm respectively .
	Available soil water in 0-10m soil reduced with the increasing of cultivation years
	of different rotation cropping systems in Yulin. Comparing with that during 1965-1976,
	available soil water in 0-10m soil during 1989-2000 reduced 250, 201, 161 and 197mm
	for MMMP, MBMP, FMBP and MFMP respectively; 193, 187 188 and 40mm for MMP,

Table 4

 4 Stable dry soil layers and required years for soil moisture grade reach to Ⅴ in different potato based rotation system in Yulin

  2 , group C was the lowest with the value of 5.38 t/hm 2 . Jatoe et al. reported that rotating other crops with potato reduced farm returns above variable costs in potato based rotation system[START_REF] Jatoe | Economic and environmental impacts of introducing land use policies and rotations on Prince Edward Island potato farms[END_REF]. Our results indicated that

farmer's gross income increased, when the frequency of potato decreased in the potato based rotation sequence. Mean gross income of group A was the highest, while group C was the lowest.

Acknowledgement:

This study was sponsored by the Foundation of SWUST university for new teachers with doctor degree (No. 11zx7140 ). We are grateful to Wu-Qiang Li who had provided many information about potato production in the northern region of Shaanxi Province.


Fengrui Chen

Shiqiang Chen

Guangxiong Peng

Using Sequential Gaussian Simulation to Assess Geochemical Anomaly Areas of Lead Element

Keywords: geochemical anomaly, geostatistics, sequential Gaussian simulation

Mapping geochemical anomaly is essential for prospecting. Kriging is often used to characterize the spatial variability of geochemistry. However, due to its smooth effects, it is incapable of detecting multi-location uncertainty of geochemistry estimate. Sequential gaussian simulation (SGS) can model the spatial uncertainty through generation of several equally probable stochastic realizations. In this study, SGS is used to map Pb spatial distribution and to provide a quantitative measure of its spatial uncertainty in particular. The results show that the spatial pattern of Pb is well recognized using the SGS. The different among realizations is used to provide a quantitative measure of spatial uncertainty of Pb estimate. Knowledge of spatial uncertainty is helpful to evaluate anormal areas of Pb element.

Introduction

With the development of exploration geochemistry, the evaluation of regional geochemical anomaly such as regional stream and secondary halo attract more and *Croesponding author more attention [START_REF] Ruiting | Facts and problems of the evaluation of regional geochemical anomalies[END_REF]. How to evaluate effectively the geochemical anomaly has been the focus for many exploration geochemists [START_REF] Xueqiu | Exploration geochemistry:past achievements and future challenges[END_REF]. For geochemical elements carry some mineralization information. The fine spatial analysis of elemental geochemistry can reveals the tracing of mineralization directly or indirectly. The study of geochemical element distribution is one of the important ways to reveal the spatial variation, mineralization and enrichment of element [START_REF] Shuyun | The method o f moments and its application to the study of mineralization in Shao guan district, north Guangdong , China[END_REF][START_REF] Qiuming | Multifractal theory and geochemical element distribution pattern[END_REF]. Geochemical anomaly analysis is the important work for prospecting, whose core content is to find, filtrate and evaluate anomaly.

For better understanding of geochemical anomaly, it is very important to predict the spatial distribution of geochemistry over the study area. Geostatistics has been used for spatial variability characterization and prediction of geochemistry. The most well-known geostatistics estimation technique is ordinary kriging (OK) called also the "best linear unbiased estimator" [START_REF] Goovaerts | Geostatistics for natural resources evaluation[END_REF][START_REF] Isaaks | An introduction to applied geostatistics[END_REF]. However, the smoothing effect of OK results in less variation in the estimated values than in the observed values [START_REF] Pardo-Iguzquiza | Modelling the semivariograms and cross-semivariograms required in downscaling cokriging by numerical convolution econvolution[END_REF][START_REF] Kyriakidis | A Geostatistical Framework for Area-to-Point Spatial Interpolation[END_REF][START_REF] Goovaerts | Kriging and semivariogram deconvolution in the presence of irregular geographical units[END_REF]. The estimation at each unsampled location includes an estimation error or uncertainty. This uncertainty should be quantified and accounted for in subsequent decision-making. For geochemical anomaly analysis, assessing the reliability of delineating an area as "normal" or "anomaly" requires the knowledge of estimation uncertainty at several locations simultaneously. Geostatistics simulation has the ability to not only predict the spatial distribution of the property at hand but also to assess both local and spatial uncertainty. The main objective of this study is to assess the uncertainty of delineating geochemical anomaly. For this the feasibility of using sequential Gaussian simulation (SGS) [START_REF] Castrignan | Geostatistical stochastic simulation of soil water content in a forested area of south Italy[END_REF][START_REF] Dimitrakopoulos | Generalized sequential Gaussian simulation on group size ν and screen-effect approximations for large field simulations[END_REF][START_REF] Delbari | Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content[END_REF]is examined.

Materials and method

Study area and data source

The study area is located in the Mohailaheng Pb-Zn deposit of Zaduo County, Qinghai Province, China. It is located in 95°33′-95°59′E and 32°39′ -32°58′N, near the North of Sanjiang metallogenic belt (Fig. 1). Numberous of Pb-Zn-Ag-Cu ore deposits with significant economic values occur in Lanping basin and Yushu and Tuotuohe areas on the eastern and northern margins of Tibetan orogenic belt. Being hosted by sediments and having no obvious affinity to igneous activities, these deposits occur in continent-continent (Indo-Asian) collisional orogen and are controlled by thrust-nappe structures, thus obviously different from the well known sediments-hosted base metal deposits in the world [START_REF] Zengqian | Thrust-controlled,sediments-hosted Pb-Zn-Ag-Cu deposits in eastern and northern margins of Tibetan orogenic belt. Geological features and tectonic model[END_REF]. They were formed in Tertiary

Kriging

Kriging is a linear interpolation procedure that provides a best linear unbiased estimation for quantities, which varies in space, Kriging estimates are calculated as weighted sums of the adjacent sampled concentrations [START_REF] Webster | Geostatistics for environmental scientists[END_REF][START_REF] Wackernagel | Multivariate geostatistics: an introduction with applications[END_REF]. That is, if data appear to be highly continuous in space, the points closer to those estimated receive higher weights than those farther away.

In geostatistical analysis, the semivariogram is used to quantify the spatial dependence of the Pb concentration:

       ) ( 1 2 ) ( ) ( ) ( 2 1 ) ( h N i i i h x z x z h N h r (1)
where h is the distance between both locations, x i and x i +h, and N(h) is the number of pairs for x i and x i +h. The experimental semivariogram was then fitted with a suitable theoretical model: spherical, exponential or gaussian. The models provide information about the spatial structure as well as the input parameters for kriging interpolation.

Kriging is considered as the optimal spatial interpolation for making best linear unbiased estimation of regionalized variables at unknown locations. The spatial prediction of the value of Pb at an unknown point is calculated as a weighted average:

   n i i i x z x z 1 0 * ) ( ) (  (2)
where z*(x 0 ) is the value to be estimated at the location x 0 ; z(x i ) is the known value at the sampling site x i and λ i is the weight. There are n sites within the search neighborhood around x 0 used for estimation, and the magnitude of n will depend on the size of the moving search window and on user's definition. Kriging differs from other methods, such as inverse distance weighted, in that the weight function λ i is no longer arbitrary, and is calculated from the parameters of the fitted variogram model with unbiasedness and minimized estimation variance for interpolation.

Sequential Gaussian simulation

The basic idea of sequential simulation is that the conditional distribution of the observed variable can be used for the simulation of subsequent grid points. The SGS is based on multi-Gaussianary assumption of a random function model [START_REF] Goovaerts | Geostatistics in soil science: state-of-the-art and perspectives[END_REF]. The data requires then a prior normal score transformation to ensure the normality of at least the univariate distribution of data. The normal score transformation [START_REF] Goovaerts | Geostatistics for natural resources evaluation[END_REF] for a continuous variable z at location x a , a =1, ..., n, is given as: The SGS consists in defining a regularly spaced grid, covering the region of interest and establishing a random path through all grid nodes, such that each nodes is visited only once in each sequence. For every grid node the normal score conditional cumulative distribution function (CCDF) is estimated according to a simple kriging system conditioned to all Pb known data and previously simulated grid nodes within a neighborhood of the location to be simulated. In this stage the semivariogram model of Pb normal scores is used. Afterwards a simulated value is drawn from the estimated CCDF and then added to the conditioning data set to be used for simulating other grid nodes. This procedure will be continued until all the grid nodes are simulated. The simulated normal score CCDFs then need to be back transformed to obtain the CCDFs of Pb in original space. These sequential steps build up only the first realization, which is only one of equally probable models of Pb spatial distribution. To generate multiple realizations, this sequence should be repeated with different random paths passing through all nodes for each realization. Indeed the only difference among the several individual simulations is the random-number seed, which initiates the simulation process and affects the random drawing of a value from the CCDF used to generate each simulated value. Therefore, in different realizations different values will be assigned to the same location. Pixel-by-pixel averaging of generated maps of Pb spatial distribution provides the so-called E-type [START_REF] Deutsch | GSLIB: Geostatistical software library and user's guide[END_REF] estimate at each grid node. The corresponding conditional variance, which represents the spread of CCDF and the probability of exceeding or not exceeding a critical threshold of Pb at each grid node also can be produced through post processing.

  n a x z F G x y a a , , 1 )) ( ( ) ( * 1     (3)

Results and discussions

descriptive statistics and spatial autocorrelation analysis

Descriptive statistics and the histogram of Pb observations are presented in Table 1 and Fig. 2, respectively. The study area has a large range of Pb element, and the maximum and minimum Pb elements are 6.2 and 944.2 g/t. The skewness of Pb observations is 14.15, which indicates that the data is anormal distribution. Therefore, before the implementation of kriging, the Pb data should be transformed. The skewness of ln(Pb) is 0.8, and the ln(Pb) data are almost normal distribution. In Fig. However, its partial sill/sill (0.74) is larger than that of former (0.63), which indicates that ln(Pb) has more spatial correlation. SGS individual realizations well preserve the range of variation of the measured Pb data compared to OK map where the minimum value is overestimated while the maximum value is underestimated. This reveals the smoothing effect, a typical property of kriging. This is also evident from the kriging variance, which is much smaller than the actual variance. OK dose not reflect the true variability, and hence is not appropriate for the underlying outlook to this study. Summary statistics of E-type are similar to those for the kriging map. This is an expected result in the sense that it is well-know that when the number of realization is large enough, the mean of the realizations converges to the estimation result. As mentioned before, the critical value is 124.5 g/t, which is determined by mean plus two times STD for Pb data. The OK estimation and Probability map of exceeding critical value is in Fig. 5. The OK map is the unique value, therefore, it critical area for OK map is certainty. However, there must be some degree of uncertainty for estimation. For example, an estimated value in OK map is larger than critical value; if it has a smeller probability of occur (0.001), then this value can be assigned to critical area. SGS can solve this problem, it provide the uncertainty of estimated value by all simulation realizations. From Fig. 5(b), the critical area is not certainty, and it is the represent of probability. The larger the value, the higher it belong to critical area. The critical area can be determined by setting a certain threshold which can take into account the situation of economic, social and management.

Conclusion

Mapping geochemical anomaly is essential for prospecting. Traditional methods often make use of the interpolation results to attain anomalistic areas, without considering the uncertainty of estimate. In this study, SGS is used to evaluate the anomalistic areas of Pb element. The simulated maps are summarized to provide the probability map of Pb exceeding the critical value. The probability map is used to delineate the anomalistic areas. The difference among 300 realizations provides a quantitative measure of spatial uncertainty of Pb estimate. Eventually SGS is recommended as a powerful and convenient simulation-based algorithm for not only spatial prediction but also for evaluating both local and spatial uncertainty, which should be incorporated in many decision-making processes.

  foreland basins developed from Paleo-Tethyan rifting basins and Mesozoic depression basins. During mineralization, thrust-nappe structures juxtaposed Mesozoic strata over Tertiary strata through a series of parallel reverse/ thrust faults in the basins. The deposits are strictly controlled by thrust-nappe structures. The rock types of magmatic are complete with frequent magmatic activity, while the exposed area is small with scattered distribution. Sampling points get from geochemical data of stream sedimentation in Yushu region of South Qinghai in 2009. This data set includes 864 Sampling points with 13 elements of Ag, As, Au, Ba, Bi, Cd, Cu, Hg, Mn, Mo, Pb, Sb, Sn, W and Zn.

Fig. 1 .

 1 Fig. 1. Geological map of south Qinghai and the spatial distribution of sampling points.

  where G -1 (.) is the inverse Gaussian cumulative distribution function (CDF) of the random function Y(x), and F* is the sample CDF of z. A back transformation of the normal scores to the original space is achieved by applying the inverse of the normal score transform introduced in equation (3).

2

  (b), the Pb element data have been transformed by a normal score transformation.The statistics (i.e. the mean value of 0 and the variance of 1) of transformed data check the correctness of the transformation.

Fig. 2 .

 2 Fig. 2. Histogram of the raw and normal score transformed data for Pb

Fig. 3 .

 3 Fig. 3. Experimental semivariogram and best fitted model of the ln() and normal score transformed data for Pb.

Fig. 4 .

 4 Fig. 4. The spatial distribution of Pb by sequential Gaussian simulation and OK over the study area

Fig. 5 .

 5 Fig. 5. OK estimation and probability map of exceeding 124.5 g/t for Pb.

Table 1 .

 1 Summary statistics of Pb data and its transformation

		mean	STD	variance maximum minimum skewness
	Pb	31.9	46.3	2144.4	6.2	944.2	14.15
	Normal score of Pb	0	1	1	3.24	3.24	0
	ln(Pb)	1.4	0.224	0.05	0.79	2.98	0.68

Table 2 .

 2 The semivariogram analysis of ln() and normal score transformed data for Pb

		model	nugget	range	sill	partial sill/sill
	Normal score of Pb	Exponentia	0.29	412	0.7	0.63
		l		5	9
	ln(Pb)	Exponential	0.01	308	0.039	0.74
				4

Table 3

 3 represents summary statistics on Pb output result by both SGS and OK.

Table 3 .

 3 Basic statistics of Pb output results

		mean	minimum	maximum	variance
	Actual	31.9	6.2	944.2	2144.4
	Realization#3	32.1	6.2	944.2	2205.1
	Realization#67	31.6	6.2	944.2	2123.7
	Realization#178	31.5	6.2	944.2	2189.4
	Realization#274	32.3	6.2	944.2	2167.6
	E-type	31.5	9.1	683.7	1653.4
	OK	32.2	9.1	723.4	1878.3


Yeping Zhu

Yongsheng Cao

Study on Bee product quality control chain based on Agent

Keywords: Bee products, Agent, Coordination, BPQ control chain Department Quarantine

In China, the bee farms are only organized loosely, so human factor has a great impact on bee product quality (BPQ). At the same time, the quality control methods related to information and intelligence technology did not give a useful support on BPQ. As a result, it is not easy control BPQ during production process of bee products. Therefore, how to control BPQ safety effectively always is a hot topic in BPQ control research field.The BPQ control chain is proposed in this paper. BPQ control and emergency mechanism is designed based on Agent technology, and the Agent logical architecture, the data mapping and conversion model, work data flow are detailed. In the architecture, the mapping relationship of Agent and BPQ control objects and attributes is defined. The strategies of control BPQ are also proposed.

Introduction

In China,Bees industry and bee products output is one of the traditional and the unique industry. At the same time, we should also be awareness of the dynamic and complex bee products industrial chain exist some adverse factors, directly or indirectly caused many problems of BPQ, and affect China's export of bee products and domestic market efficiency. For example, In 2002 the European Union and some countries to our country to stop the honey sales into the trade barriers, to our country bee products caused a loss, affect the normal development of the our country bee products industrial. These events cause supervision departments of BPQ high attention concern (Zhao Jing, 2011).

One of the unquestionable the situation is: There are many influence factors about BPQ in China, include production factors, environmental factors, human factors and so on, directly led to the management control difficulties. From the perspective of industry, bee industry is information degree lowest industry in China, popularization of computer and network is still relatively low in bee production area and production enterprise, production process of data and files often lost or none at all. At the same time, in the BPQ control need to coordinate unit very much, operation and maintenance costs expensive. With China's bees industry quality improvement and the rapid development of modern information technology, we fully capable of using modern information technology to development of bee quality control system.

Bee products quality control

According to the BPQ control, because of a long chain of bee products, there are many influence factors of the BPQ, so BPQ safety management and control needs further research in China.

Fig.1. BPQ control platform

Government Departments Supervision

From the graph, we can look at: bee industrial chain structure is complex, the main link includes: production links (bee field), purchase link (acquisition business), processing link (factory) and sales link (supermarkets, shops), and so on. Each link has its own function, between each link on each other, form complex open, distributed system. The bee products production and consumption system tends to be complicated and internationalization, any link to appear BPQ problem caused by the quality and safety of bee problems and harm consumers. Complex structure and characteristics of the system produce great resistance about BPQ control problem solving.

The influence factors on the BPQ, also have technical, economic level, also have the exogenous factors or endogenous system of joint action. These factors are in different levels influence the quality of bee. Therefore, we must first have a clear understanding of factors influencing the quality of bee products, and then we can research the quality control system of bee products.

(1) From the point of view of breeding bees,, there are many feed materials in the process of the bees feeding, such as sugar and honey, the quality of the bee medicine, bees epidemic prevention, and related the hygiene of instruments, water quality, air quality, soil quality, keepers feeding technology skills and so on, all of this influence the quality and safety of bee products and harm consumers health.

(2) From the monitoring, Bee product quality control method and risk monitoring means of the government must improve in bee product quality control chain.

(3) From bee industrial chain observation, chain node is various. It included production links (bee field), purchase link (acquisition business), processing link (factory) and sales link (supermarkets, shops). In the whole bee industry chain, have linear nodes, also have mesh nodes, the whole process complex and coordinate and management difficult.

Through the in-depth analysis, it is found that bee products traceability system could realize the problems of product recall with modern information technology, but still has the following problems remain to be solved: the quality and safety of bee products risk analysis to be further strengthen, while an bee products harm accident, we must scientific evaluation and carry out emergency measures. Therefore, in view of the current BPQ control, it could risk diagnosis and security warning before the event, harm emergency when event occurs, and assessment after the event. This paper proposes the BPQ control based on Agent and spread of related research work.

Bee product quality control chain based on Agent

The BPQ control has the characteristics of complex, the quality safety emergency occurrence, development, change also has a certain dynamic, many event of evolution characteristics (Zhang Dong Ling, 2009). When a certain level or links occur BPQ events, it must affect other links different physical entity operation and production with the events associate and product a chain reaction. That is means the BPQ emergencies event are not completely isolated in the BPQ control chain, the event has direct contact and may have an impact with other links of BPQ control chain.

The BPQ control chain based on the Agent, according to the function is divided into quality monitoring layer, information analysis layer, emergency command layer, emergency directors and communication coordination of five levels. Monitoring layer Agent called monitoring Agent, such as the production monitoring Agent, sales monitor Agent and so on.

(2)Analysis layer is mainly responsible for accept data from monitoring layer in the Agent, and according to the types of data, nature, carries on the analysis, the assessment and to determine the detection of links (or critical control point) presence quality safe hidden trouble, whether serious harm (determine harm degree level), whether start quality emergency measures, etc. Analysis layer also contains morel analysis Agent, such as abnormal data analysis Agent, harm rating Agent and so on. It plays the role of quality evaluation experts.

( (4)Execution layer mainly responsible for specific emergency measures of performance, such as quality tracking, accident liability sure, release emergency report and so on. Execution layer contains different function executive Agent, according to emergency plan, independently run to finish the task of distribution, and will be submitted to the related information of management department, direct responsibility department, etc.

(5)Communication coordination layer is mainly responsible for control interaction communication between all of Agent. For each one of the specific task, it can product a communication Agent. Communication Agent is equivalent to a station, the relevant data of the emergency response, information, task, and the results will be submitted to the communication coordination Agent, it is equivalent to a resource sharing platform, will be responsible for the upload information issued.

Because other related links in the BPQ control based on Agent has similarities, therefore, mainly in bee products production process as an example, this paper describes a BPQ control process based on Agent.

Fig.3. BPQ Emergency process based on Agent

(1) The user input corresponding critical control points (elements) parameters, such as bee drug.

(2) And then the system automatically start the corresponding quality control Agent, forecast Agent and assessment Agent, input data parallel detection processing.

(3) If the value in safety assessment scope, on the one hand, to users test information; on the other hand, do not product the alarm behavior.

(4) The system of the related Agent to be automatic cancellation, or place to hang up state; the user testing the loop ends.

(5) If, in (2) executive process, found the quality safety problems, immediately start emergency Agent.

(6) According to the quality safety emergency Agent event situation, on the one hand, to convey quality event information users; On the one hand, to be automatic or user input of the related information of physical objects, such as the other bees fields, the business, the processing factory, sales, pharmaceutical factory and so on.

Conclusion

The BPQ control chain based on the Agent is an organic whole include relevant organization, personnel, communication, information and so on. It is provide decision-making information, knowledge for the BPQ management and control. In the intelligent decision-making system, based on each link collection data related information, use of computer, Agent, network information technology help managers monitor BPQ, once found BPQ safety problems, through the coordinated decision to de whether start the emergency response mechanism and how to choose a specific emergency plan measures for BPQ control of the effective implementation.

Fig. 2 .( 1 )

 21 Fig.2. BPQ control group based on Agent

)

  Command layer responsible for the concrete implementation of BPQ emergency disposal measures, and control related executed Agent. Usually, a task for emergency disposition, only one emergency command Agent. Specific tasks including the emergency disposal scheme determination, task decomposition, coordination strategy formulation, implementation and the monitoring of the implementation of the Agent. Command layer is central nervous of BPQ control chain.

( 7 )( 8 )( 9 )

 789 Start decision-making Agent, evaluation Agent and Quality safety evaluation of other aspects. To the corresponding inspection institution, supervision department report analysis and test emergency report. To the user Agent submits information.The BPQ control chain contains two aspects: the earlier evidence collection, after the emergency period. In the system structure based on Agent, the earlier evidence collection main application of related business process analysis and simulation based on Agent. After the emergency period main application for emergency treatment measures harm the analysis, communication and rehabilitation evaluation based on Agent.

Acknowledgments

This research was supported by National Scientific and Technical Supporting Programs Funded by Ministry of Science and Technology of China (nyhyzx07-041).

National Natural Science Foundation about Agent-based quality control of agricultural products(60972154).