Junhua Bai

Jing Li

Shaokun Li
email: shaokun0004@sina.com

Monitoring the Plant Density of Cotton with Remotely Sensed Data

Keywords: Cotton, Plant Density, Remote sensing

PDC (Plant Density of Cotton) was an essential parameter for estimating the cotton yield and developing the zone-management measurements. This paper proposed a new method to retrieve PDC from the satellite remote sensing data. The thirteen fields of Xinjiang Production and Construction Corps (XPCC) (total 630 hm 2 ) were selected as the study area, where the sowing date, emergence date, and PDC were investigated. Based on the investigation data the linear models to estimate PDC are established using EVI and DEVI respectively. The results indicated that the difference of seedling size caused by the emergence time decreased the estimation accuracy of PDC. To improve the estimation accuracy the partition functions were established in terms of sowing date. DEVI is capable of reducing the influence of soil background significantly and it can bring the monitoring time forward from June 9 th to May 24 th in this research. The results indicated that the optimal time monitoring PDC would be from squaring to full-flowering of cotton growing period. A demonstration to monitor PDC was taken on June 9 th in the 148 th farm of XPCC. It can be concluded that the emergence time and the non-cotton background were the main factors affecting the monitoring accuracy of PDC, and the partition function with the emergence time could improve the estimation accuracy, and DEVI could make the monitoring time forward, and the optimal monitoring time was from the squaring stage to the full-flowering stage. This research provides an efficient, rapid and intact way to monitor PDC, and it is significant for operational application at a regional scale.

Introduction

PDC (Plant Density of Cotton) is one of key factors affecting cotton yield, and is the most important index for analyzing cotton growth and taking the management measurements. The cotton yield in Xinjiang accounts for about 30% in China and 8% in the international cotton trade. PDC is a key item in all measurements for the cotton farmland management (W. F. [START_REF] Zhang | Effects of plant density on canopy photosynthesis, canopy structure and yield formation of high-yield cotton[END_REF]; X. L. [START_REF] Hou | Effect of different nitrogen fertilization on yield and nitrogen using of super-high density cotton system[END_REF]. The low PDC weakens the applications of cultivation technology for increasing cotton yield.

Agriculture is one of important applications of satellite remote sensing technology, and the numerous researches have been done on the area estimation, yield estimation and growth monitoring (O. [START_REF] Wendroth | Predicting yield of barley across a landscape: a state-space modeling approach[END_REF]; P. C. [START_REF] Doraiswamy | Crop condition and yield simulations using Landsat and MODIS[END_REF]; X. M. [START_REF] Xiao | Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images[END_REF]. In cotton monitoring, the method of spectrum matching has been used to extract the cotton pixel in the regional scale(J. [START_REF] Li | Method research of pixel discrimination about CBERS-02 satellite image based on spectral knowledge[END_REF], and the image texture of geo-statistical has been introduced into monitoring cotton growth(C. J. [START_REF] Liao | Cotton growth monitoring during chemical control stage using geo-statistical image texture: a cases study of Shihezi[END_REF], and the hyper-spectrum can be used to inverse the biomass and leaf area index quantitatively (D. L. [START_REF] Bai | The Response of Canopy Reflectance Spectrum for the Cotton LAI and LAI Inversion[END_REF]; J. H. Bai, et al., 2007), V. [START_REF] Alchanatis | Weed detection in multispectral images of cotton fields[END_REF] used the hyper-spectrum image to distinguish the weeds from cotton field, and the action of water stress is compared with the root disease by infrared sensor (N. R. [START_REF] Falkenberg | Remote sensing of biotic and abiotic stress for irrigation management of cotton[END_REF]. And although PDC plays an important role in the farmland practices, few researches was done on PDC monitoring by satellite data.

Normal Difference Vegetation Index (NDVI) is one of the most popular parameters used to evaluate the crop growth condition (J. W. [START_REF] Rouse | Monitoring the vernal advancement and retrogadation (Greenwave effect) of natural vegetation[END_REF]; F. Baret, G. Guyot, 1989[START_REF] Baret | Potentials and limits of vegetation indices for LAI and APAR assessment[END_REF]. And some researches show that NDVI is susceptible to the influence of the non-vegetation background, and it saturates when the vegetation cover increases to the some extent (Q. [START_REF] Wang | On the relationship of NDVI with leaf area index in a deciduous forest site[END_REF]; A. E. K. [START_REF] Douaoui | Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[END_REF]. Other indices were constructed to make up for the inadequacy, and the Enhance Vegetation Index (EVI) is one of the most popular in them.

At present, the field investigation is almost the only way to obtain PDC. The disadvantage of this way is that it can only get the point data, and consumes much time and energy. In this paper, we analyzed the multi-temporal satellite images coupling the field investigation data from the emergence to full-flowering stage of cotton to (1) construct the linear model to estimate PDC, [START_REF] Baret | Potentials and limits of vegetation indices for LAI and APAR assessment[END_REF] analyze the main factors affecting the monitoring accuracy of PDC and perfect the model, (3) obtain the optimal period to monitor PDC, (4) demonstrate the PDC monitoring method in the regional scale.

Methods

Study area

The study area is located in the148 th farm of XPCC, beside the Manas river valley ((45°00′N 86°11′E; 44°46′N 86°23′E). The climate type is the typical continental-arid climate, and the sunshine rate is about 80% during the cotton growing period. The cotton planting proportion accounts for over 90% in the farm.

Generally it seeds from the early April to the middle April, and Squaring at the early of June, flowers at early July. The row space is usually 55cm+20cm+55cm and plants space is usually 9.6cm. At the middle of July, the vegetation coverage fraction rises to over 80%, and the top-ending measurement was taken to control the unlimited growing character of cotton. In 2007, the sowing date is mostly from April 13 th to 19 th .

Investigation and computation for PDC

PDC was investigated from June 5 th to 15 th , in 2007 and 2008. In order to make the sample data representative, we chose the investigation points far from the field patch boundary to avoid the boundary effect on the crop growing. Three sampling units were investigated in every investigation points. The sampling points were positioned by GPS. The area of sampling units is approximately 26.7m 2 . We visited the landowner of each sampling fields to record the seeding and emergence date. The sowing time was divided into three partitions including the early, middle, and late of the sowing date 52 sample points are obtained through investigating thirteen fields (630 hm 2 ), including 40 sample points from the middle sowing date, 4 from the early sowing date, and 8 from the late sowing date. The data consisting of three sowing date are employed evenly into the establishing and testing model. PDC could be calculated as:

1 2 3 3 7 5 3 u n n n N + + = × L (1)
Where N u was PDC per hectare(×10 5 plant﹒hm -2 ), (i = 1, 2, 3) was the number of plants in the sampling unit.

Vegetation index

1 2 nir red nir red blue E V I G C C L ρ ρ ρ ρ ρ - = × + × - × + (2)
Where ρ nir , ρ red , ρ bule was reflectance in near-infrared (NIR), red and blue waveband respectively. C 1 and C 2 were the correction coefficients of atmospheric resistance in red and blue; L is the canopy background brightness correction factor and G is the gain factor. The coefficients adopted in the EVI algorithm are, L=1.0, C 1 =6.0, C 2 =7.5 and G (gain factor) =2.5 [START_REF] Huete | Development of vegetation and soil indices for MODIS-EOS[END_REF][START_REF] Huete | A comparison of vegetation indices over a global set of TM images for EOS-MODIS[END_REF].

To further explore the suitable remotely sensed parameters, DEVI is the difference of EVI between the growth stage and the bared soil stage:

i e D E V I E V I E V I = - (3)

The evaluation of estimation accuracy

The Relative Extremenum Predication Error (REPE) is used to evaluate the estimation accuracy of PDC, REPE is calculated as

m ax m in U U R M SE R E P E N N = - (4) 2 1
( )

1 n u u i N p N t R M SE n = - = - ∑ L (5)
N u max , N u min is the maximum and minimum value in the testing models, respectively; N u p, N u t is the estimation and measured value, respectively.

Results

PDC estimation with EVI

PDC estimation based on the data from three emergence stages. As shown in Fig. 2, from May 24 th to July 11 th , EVI rose with the cotton growing. The average EVI for each growing stage is 0.11, 0.27, 0.53 and 0.82. On May 24 th , the range of EVI distribution is narrow and it shows insignificant linear relationship between EVI and PDC. However, EVI distribute more widely on June 9 th and June 25 th , and it shows the obvious linear relationship. EVI on June 25 th is higher than on June 9 th , the correlation degree isn't kept increasing tendency as time goes on, EVI difference on July 11 th is less than on June 25 th and the linear isn't significant. From the above analysis, we could deduce the emergence time caused the difference of seedling size, which affects the linear between PDC and EVI. Furthermore, the mixed pixels with non-cotton information decrease the relation on May 24 th and July 11 th , and the relations aren't nearly interrupted on July 9 th and July 25 th .

The correlation coefficients between PDC and EVI haven't reached significant level on May 24 th and July 11 th and reached significant level on June 9 th and June 25 th , and correspondingly R 2 is 0.23 on June 9 and 0.30 on June 25. Therefore, the estimation models on PDC with EVI as below:

PDC estimation with DEVI

PDC estimation based on the data from three emergence stages EVI on May 24 th , June 9 th , June 25 th and the July 11 th , respectively minus EVI on April 22 th get DEVI on the corresponding time. It is shown in Fig. 3, the determination coefficients of the linear equation between PDC and DEVI are 0.34 reaching the great significant level on May 24 th , and on the contrary the linear disappears when EVI estimated PDC on May 24 th . In addition, the determination coefficients rise from 0.23 to 0.27 on June

The validation of estimated models and its application

The validation of estimation models As Table 1 shows, the veracity of regression models from ( 6) to ( 14) is tested, all determination coefficients between the estimated and measured value are validated, but it is noticeable that the determination coefficients of Ⅲ and Ⅳ from DEVI on May 24 th also pass through the significant testing. The RMSE on PDC is 0.301×10 5 plant﹒hm -2 and REPE is 27.1%, which indicates that DEVI would bring the monitoring time forward , on the contrary, when EVI is used to estimate PDC on June 9 th and June 25 th , RMSE is 0.280 and 0.253×10 5 plant﹒hm -2 , respectively, and REPE is 25.7% and 23.2%, respectively. However, the estimation accuracy of models is greatly improved on May 24 th , June 9 th and June 25 th when the data of middle emergence stage is used to estimate PDC. Among them, RMSE and REPE of Ⅰon June 9 th is the lowest value. The demonstration for monitoring PDC with LANDSAT-5 It is concluded from analysis above that DEVI can make the PDC monitoring time bring forward to the late May, and the accuracy on June 25 th is higher than on June 9 th and May 24 th .

However, Ⅰand Ⅲ on June 9 th (Table 1) have the double properties with the earlier time and the higher veracity, andⅠ has the advantage of only using the single-time image, which will enhance the feasibility of estimating PDC. Therefore, Ⅰ on June 9 th can be used as the optimal plan to estimate PDC. Monitoring PDC in the 148 th farm by Ⅰ, the results from Fig. 4 indicate that PDC has the strongly spatial correlation and the distributed continuity. The farmland area for cotton in the 148 th farm in 2007 is approximate 15811.7 hectare, and 17.1% for ≤1.20×10 5 plant﹒hm -2 , 10.2% for 1.20~1.35×10 5 plant﹒hm -2 , 13.1% for 1.35~1.50×10 5 plant﹒hm -2 , 13.6% for 1.50~1.65×10 5 plant﹒hm -2 , 12.6% for 1.65~1.80×10 5 plant﹒hm -2 , 10.9% for 1.80~1.95×10 5 plant ﹒ hm -2 , 8.7% for 1.95~2.10×10 5 plant ﹒ hm -2 , 6.0% for 2.10~2.25×10 5 plant﹒hm -2 , 3.8% for 2.25~2.40×10 plant﹒hm -2 , 4.0% for ≥40×10 5 plant﹒hm -2 . Farmlands with the different PDC can be distinguished (Fig. 5).

Discussion

The above analysis shows that two situations may affect the monitoring accuracy. Firstly, when the coverage fraction of bared soil was high, the reflectance signal of vegetation was weak, the soil properties dominated the difference of pixel reflectance, and it was difficult to retrieve PDC accurately. Secondly, the cotton coverage increased from zero to almost 100 percent when the growth stage developed from the seedling stage to full-flowering period. Under the condition of the high cotton coverage, the contribution of soil to the pixel reflectance decreased, and the saturation of vegetation index happened.

As shown in the results on May 24 th EVI is not sensitive to the PDC difference significantly. DEVI could remove the noise of the soil background and highlight the vegetation information. We construct the relationship between PDC and DEVI to monitor PDC, which brings up the PDC monitoring time. Compared with on May 24 th , the cotton coverage on June 9 th and June 25 th was increased remarkably, the proportion of cotton signal rose gradually, and the PDC could be monitored through the green index of cotton canopy population.

The analysis also indicated that it was impossible to estimate PDC effectively in any way when the cotton canopy population in the full-blooming stage had the highest land cover, and especially the cotton coverage rose to over 80%, and the canopy population was fuzzy between pixels because of the saturation phenomenon. Moreover, it was difficult to monitor PDC by remote sensing because of another reason. The canopy population was controlled by PDC before the flowering stage, and yet after the cotton grown into the full-flowering stage, the compensation effect of cotton plants appeared by a series of management measures of top-ending, fertilizerwater and chemical controlling, and so the population with the low PDC might develop into the big one, that meant the population with the low PDC had the higher leaf area index and biomass than the population with the high PDC. As a result, the cotton canopy population was simultaneously controlled by the plant size and PDC.

Therefore, PDC couldn't monitored successfully after the full-flowering stage (at July 11 th ), which was decided by the defect of remotely sensed technology and the characteristic of cotton growth. The cotton farmers couldn't seed at the same time because of the limitations of human and mechanism, which could cause the difference of cotton plant size in the early stage of cotton growth, the result added the difficulty of estimating PDC with the high accuracy, the information of sowing date should be known to increase the monitoring accuracy.

Conclusions

This paper proposed a method to monitor PDC by satellite remote sensing images. The results showed that satellite images can be used to monitor PDC at the regional scale although the sowing date and the spectrum saturation have an influence on the accuracy of PDC monitoring. The analysis indicates that the optimal PDC monitoring period is late May by DEVI. The optimal growing stage with the highest estimation accuracy to monitor PDC is from the squaring stage to the early-flowering stage, especially the late-squaring stage could be satisfied with the estimation accuracy and time required by the farmland management measurements simultaneously. The method to monitor PDC by satellite images could be a potential way to investigate PDC at the regional scale.

Fig. 1

 1 Fig.1 Location of the experiment

i n 2 . 3

 23 Remote sensing images and pre-processingLANDSAT-5/TM images in April 22 th , May 24 th , June 9 th , June 25 th , and July 11 th , 2007 were used. The geo-location was done by ENVI4.2 with the geo-referenced panchromatic waveband image from LANDSAT-7 as the basic image. The atmospheric correction was done using the AgRSIS software. The atmospheric radiation transferring model uses 6S model and the aerosol optical thickness was inversed by the algorithm of dense dark vegetation. The pixel size was 28.5m×28.5m.

June 9 6 )

 96 th : N u =2.61EVI+1.22 R 2 =0.23 ** (June 25 th : N u =2.41EVI+0.65 R 2 =0.30 *coefficient of determination from three sowing dates and middle sowing date. the late sowing; × the early sowing; ■ the middle sowing; at the edge of farmland, and the same as △ ◇ below.

Fig. 2

 2 Fig.2 Relationship between PDC and EVI PDC estimation based on the data from middle emergence stage As shown in Fig.2, coefficient of the linear equation between PDC and EVI rise to 0.75 for June 9 th and 0.63 for June 25 th when the data from middle emergence stage is only used, but unfortunately, the determination coefficient hasn't still reach the significant level on May 24 th and July 11 th . The estimation models on PDC with EVI as below: June 9 th : N u = 4.364EVI+0.686 R 2 =0.75 ** (8) June 25 th : N u = 3.185EVI+0.148 R 2 =0.63 ** (9)

9Fig. 3

 3 Fig.3 Relationship between PDC and DEVI

  shows the testing result based on EVI from the early, middling and late emergence stage; Ⅱ shows the testing result based on EVI form the middling emergence stage; Ⅲ shows the testing result based on DEVI from the early, middling and late emergence stage; Ⅳ shows the testing result based on DEVI from the middling emergence stage.

Fig. 4

 4 Fig.4 Monitoring PDC with remotely sensed data from LANDSAT-5 in the 148 th farm of Xinjiang, china

Table 1

 1 Testing about the different models of estimating PDC

	Time	The estimating methods	The regress equations between measured and estimated values	RMSE REPE
		Ⅲ	y = 0.184x+1.507 (R 2 = 0.181 * )	0.301	27.1
	May 24	Ⅳ	y = 0.454x+0.939 (R 2 = 0.418 ** )	0.248	22.7

Acknowledgments

The research was supported by the National High Technology Research and Development Program of China (2006AAl0302, 2006AA102207, and 2006AA102272), National Nature Science Foundation of China (40801143), and Nature Science and Technology Innovation Foundation of Shihezi university (ZRKX200748).


Huaping Shang
email: huapingshang@sina.com

Quantification Research on Different Load Weightbearing Running Biochemical Indexes of Rats

Keywords: weight-bearing running, exercise performance, quantification

Research Objective: To discuss exercise performance and biochemical reactions of different load weight-bearing running of rats. Research Methods: 24 male 3-month-old SD rats were randomly divided into 3 groups (n = 8), namely, a none weight-bearing running group, a 35% maximum load weight-bearing running group, a 75% maximum load weight-bearing running group. After weight-bearing running training for 6 weeks, the maximum weight-bearing capacity, the blood lactic acid value and serum CK value of rats were tested and compared. Research Results: 30m/min speed running and 45m/min speed running respectively enabled the lactic acid value to generate first and second inflection points; weight-bearing running increased the serum CK-M value with the increase of bearing weight and load, and the blood lactic acid value and the serum CK value were significantly increased. Conclusion: 30m/min is the anaerobic threshold intensity of rat running; maximum load capacity of rats is about 65% of sole weight; weight-bearing running with appropriate load can not damage muscle fibers and can effectively mobilize Ⅱ type muscle fiber, 75% maximum load weight-bearing running can mobilize Ⅱ type muscle fiber to be fully raised, but can cause muscle fiber damage.

Research Objective

Rat resistance training is internationally and widely adopted in biological adaptation mechanism of researching on strength training, the load running training still has many problems in experiments as an effective method of resistance training. Since weight-bearing running training comprises two exercise load modes of weight-bearing and running, the load effect of running is mainly to improve the functions of respiratory and circulatory system, it is also still effective in improving muscle endurance simultaneously, and weight-bearing training has the main value of developing muscle maximum strength and power endurance. In weight-bearing training, the size of load strength may have different influences on muscle functions and even different types of muscle fibers. Therefore, When training plans are formulated in rat weight-bearing run experiments, the quality and quantification features of running and gravity load must be made clear, simultaneously, the boundary among maximum weight-bearing ability, aerobic exercise and anaerobic exercise of rats should be comprehended. The experiment aims at discussing the exercise performance and biochemical reactions of running with different loads and bearing weights of 3-month-old rats, thereby providing quantitative basis of exercise load control for rat weight-bearing running experiments, and also providing reference for rat related exercise experiment in the future.

Materials and Methods

Experiment Object

24 clean male 3-month-old SD rats were provided by Animal Center of Jiangxi Medical College and were fed in Animal Room of Animal Center of Jiangxi Medical College. Breeding conditions comprised: free water and food feeding, natural lighting, room temperature of 18DEG C to 22 DEG C, humidity of 40% to 45%; normal feedstuff feeding, and the feedstuff was provided by Animal Center of Jiangxi Medical College.

Experimental Grouping

24 rats were randomly divided into 3 groups: a none weight-bearing running group, a 35% maximum load weight-bearing running group, a 75% maximum load weightbearing running group with eight rats in each group.

Instruments and Reagent

(

Experimental Methods

(1) Rat maximum load capacity test: heavy objects were applied on waists and backs of rats (iron half-cylinder bodies with the diameters of 2cm and 0.6cm with the length of 3cm, the weights were 80 grams and 10 grams respectively), the weight was progressively increased, and the increase grades were 80 grams, 40 grams and 10 grams respectively. The weight which enabled rats to be incapable of walking was defined as the maximum load intensity in rats.

(2) Determination of anaerobic threshold in rats: the treadmill was inclined for 0 degree. 8 rats were made to run for 3min at the speed of 25m/min, tail blood was sampled at the moment's notice, 1min, 1.5min, 2min, 2.5min and 3min before and after running for blood lactic acid analysis, thereby measuring the time of rat muscle lactic acid and blood lactic acid to reach equilibrium. 8 rats were made to warm up for 2min at the speed of 10m/min, then, the rats were made to run without stop for 2min under gradual increasing load intensity of 25m/min, 30m/min, 35m/min, 40m/min, 45m/min, 50m/min, 55m/min and 60m/min, and the tail blood was immediately sampled at the end of each grade load respectively.

(3)Comparison of blood lactic acid values after weight-bearing and non weightbearing running of rats: rats were respectively made to bear no weight, and to bear 75% maximum load bearing weight and 35% maximum load bearing weigh (n = 8) and were made to run for 2min at the speed of 25m/min, and blood was sampled in 1.5min after running for testing blood lactic acid values. Blood lactic acid tests adopted YSIl500 SPORT lactic acid analyzer, and testing process was operated according to kit instructions.

(4) Comparison of serum CK and CK-MB values of rats after exercise under different loads and bearing weights: rats were respectively made to bear no weight, and to bear 75% maximum load bearing weight and 35% maximum load bearing weigh (n = 8) and were made to run for 2min at the speed of 15m/min, the tail blood was sampled immediately after running for CK and CK-MB test. Test methods adopted electrooptic colorimetry and could be finished on TU-1810 UV-Vis spectrophotometer. Testing process was operated according to kit instructions.

Statistical Analysis

Measurement information and data were represented with mean ± standard deviation, data statistics was processed through adopting SPSS15.0 statistical software, and statistical methods adopted LSD multiple comparison method for analysis.

Results and Discussion

Experimental Results

(1) Maximum load capacity of rats. In the incremental load test, it could be obtained from table 1 that the even maximum load capacity of rats was 65% of sole weight through computing according to rat weight percentage. (2) Testing Result of Rat Anaerobic Threshold .The time for muscle lactic acid and blood lactic acid to achieve balance in the experimental test was 1.5 minutes. In order to carry out rat weight-bearing running training, changes of blood lactic acid of rats in the process of running with different intensities should be firstly comprehended, thereby effectively controlling aerobic and anaerobic components in rat weightbearing running training. In the continuous run process, with the increase in the intensity of running, anaerobic components of energy metabolism was gradually increased, showing an increase in muscle lactic acid production. Gravity load also changed energy metabolism forms in the muscle work process. In order to distinguish causes of generating lactic acid in rat weight-bearing running training, the effect direction of load intensity of running should be comprehended, thereby determining the influence of loading on muscle energy metabolism in rats. The experiment results showed that the blood lactic acid of rats was gradually increased with the increase of load intensity, the inflection point appeared in the speed point of 30m/min. When the speed was increased to 45m/min, the second inflection point of blood lactic acid increase appeared. The first inflection point appeared between running speed of 30m/min and 35m/min, blood lactic acid was increased by 1.46mmol / L and increased very significantly. The second inflection point appeared between the running speed of 45m/min and 50m/min, blood lactic acid was increased by 1.80 mmol / L, and the increase extend was further enlarged. In Table 2, Treadmill the first inflection point 30m/min speed of blood lactic acid changes in rat increasing load running could be defined as rat anaerobic threshold speed. 4, it could be seen that with the increase in weight-bearing load, CK values were increased significantly, while the prominence level of differences among different groups was also increased. CK-MB was also increased, but the increase was not obvious, especially the changes between small weight-bearing group and the big weight-bearing group were not obvious, and there was no significant difference. * Different prominent levels between none weight-bearing group and the small load group; ** Different prominent levels between small load group and big load group.

Analysis and Discussion

(l) The average maximum load capacity of rats was about 65% of their sole weights, the determination standard treated the load that rats could essentially stand up for support and could simply walk as limit load, this movement mode did not always reflect the overall features of rat muscle function. In addition, the rat loading mode used in this study was to apply heavy objects on waists and backs of rats, load was dispersedly acted on rat's limbs and trunk, this load capacity embodied rat systemic comprehensive endurance. The results of this experiment only provided reference basis for load control of weight-bearing run training of 3-month-old rats, its change rules of comprehensive weight-bearing capacity with age needed further study.

(2) When rat lactic acid threshold speed was 30m/min and the speed achieved 45m/min, the increase in blood lactic acid generated the second inflection point. In this study, twice blood lactic acid inflection points in rats were results of twice changes of anaerobic metabolism process, the first inflection point marked that the anaerobic glycolysis functional systems began to be greatly employed, temporary equilibrium between energy consumption and supply occurred immediately after employ, with further increase of exercise intensity, the second inflection point occurred, which marked that the potential of body anaerobic glycolysis power supply system functions was further developed. Scientists often treated the time between the appearance of the second inflection point to the end of exercise as important indexes for measuring tolerance fatigue ability of athletes, and the second inflection point was used to control intensity of anaerobic capacity training of athletes.

Two inflection points obtained in this study were only inflection points of blood lactic acid curves, and had certain correspondence with two inflection points of exercise physiology states in human physiology, the appearance mechanisms of two inflection points may have certain same mechanisms in twice changes of physiological function states obtained in studies in human physiology, and it was concrete performance of body function capacity change on blood lactic acid value in rat exercise. The blood lactic acid change form of the experiment can provide reference data of exercise intensity control for future rat exercise experiments.

(3) The blood lactic acid highest value was increased with the increase of bearing weight and load after running at the speed of 25m/min under no bearing weight, small load bearing weight and big load bearing weight, and as the increase of load intensity, the increase extend of blood lactic acid had increasing trend. This study showed that when rats run under the speed of 25m/min, the rat body energy metabolism was basically in aerobic metabolism, after running, the blood lactic acid was little increased, thereby, the increase of blood lactic acid after bearing weight could be considered to be caused by changes in muscle working status. The blood lactic acid in the exercise process was mainly generated by anaerobic glycolysis in muscles, the contents and activities of amber mitochondrial malate dehydrogenase in different types of muscle fiber were different, the contents and activities in II-type fiber were higher than that of I type fiber, thereby it can be inferred that when the II type fiber was more used, the blood lactic acid contents after exercise was increased considerably. It could be further inferred that 75% load running can effectively mobilize II type muscle fiber recruitment.

In weight-bearing training, the effect directions of different load intensities were always important tissues of sports training study, according to features of different sports games, athletes need to develop different muscle strength qualities and develop different types of muscle fibers; athletes of speed and strength type need to develop II type muscle fibers, the neurons for dominating II type muscle fiber is large, excitation threshold is high, only high-intensity resistance load can effectively enable II type muscle fiber to be fully collected for participating in contraction. In this study, the blood lactic acid results of different load weight-bearing running trainings support this view. Namely, large weight-bearing load running more fully uses II type muscle fiber, the main form of II-type muscle fiber energy metabolism is glycolysis, and thus increase extent of blood lactic acid after running is higher than that of big load weight-bearing group and none weight-bearing group.

(4)75% maximum load weight-bearing running could result in a certain degree of damage in muscle fibers, while 35% maximum load weight-bearing running could not cause injury. Two kinds of maximum load weight-bearing running could not cause obvious damage of myocardial fibers.

In experimental studies in mammals, CK-M and CK-B in serum CK were frequently applied, CK-M and CK-B 22 were combined to form CK-MM, CK-MB and CK-BB, CK-MM was usually a skeleton muscle source, CK-MB was a cardiac source, whereas CK-BB was a brain source. In the studies, the activity of three isozymes was often used to compare with total CK activity to determine the skeletal muscle, heart muscles, or brain injury and its extent.

CK was commonly found in muscle cells, muscle fiber micro-injury may lead to increased permeability of fibrous membrane, and thereby CK could be very vulnerable to enter into the blood through the muscle cell membrane, so that CK was significantly increased. Field of sports medicine treated increased activity of CK and its isoenzymes as a sensitive indicator of muscle damage judgments, but in animal experiments, quantitative criteria to judge these indicators were still deficient. Su Quansheng [START_REF] Quansheng | Phasic Changes of Blood Interleukin -6, Creatine Kinase and Isoenzyme Thereof after Rat Exercise Skeletal Muscle Damage[END_REF] studied and found that when rat CK and CK-MM were above 409 U / L, the injury symptoms of muscle fibers were more obvious. Cui Yupeng [START_REF] Yupeng | Skeletal Muscle Damage, Plasma CK and Isoenzyme Activity Level Changes of Rat after Different Load Swimming Sports[END_REF] studied and found that when rat CK was up to 600U / L, the skeletal muscle damage and CK were significantly correlated. The results of this study were compared, it could be determined that the rat CK value reached 690U / L after weight-bearing running of 75% the maximum load, it was shown that the weight-bearing running under load intensity may cause certain damage to muscle fibers. However, CK value was 301U / L after weight-bearing running of 35% maximum load, it was shown that the load and weight-bearing running could not cause significant damage in skeletal muscle.

Summary

Through different load weight-bearing running experiments of 3-month-old SD rats on 0 degree slope treadmill, it is showed that: 30m/min speed running is the rat anaerobic threshold intensity; the rat maximum load capacity is about 65% of their sole weight. Load can prominently increase blood lactic acid of rats under aerobic running intensity, the weight-bearing running with appropriate load can not damage muscle fibers and can effectively mobilize Ⅱ type muscle fiber; 75% maximum load weight-bearing running makes II muscle fiber to be more fully raised, but may cause minor damage to the muscle fibers.

( 4 )

 4 Rat serum CK and CK-MB Value Testing Results of Running with Different Loads and Bearing Weights. From Table

  1) ZH-PT-type Animal Experiment Treadmill produced by Anhui Huaibei Zhenghua Bioinstrumentation Equipment Co, Ltd; (2) YSIl500 SPORT Blood Lactic Acid Analyzer produced by United States Gimcheon Instruments Inc; (3) TU-1810 UV-Vis Spectrophotometer produced by Beijing Persee Instrument Co., Ltd; (4) MP200A Type Electronic Balance produced by Shanghai Second Balance Instrument Factory;(5) Creatine Kinase Kit produced by Beijing Biosino Bio-technology and Science Inc; (6) Blood Lactic Acid Test Kit produced by Nanjing Jiancheng Biological Technology Co, Ltd.

Table 1 .

 1 Rat Weight and Average Maximum Load Capacity (n = 8)

	Group	Average BodyWeight (g)	Maximum Load Capacity (g)
	None weight-bearing group	209±15.12	136.18±47.2
	35% maximum load weight-bearing running group	235±9.13	154.18±10.3
	75% maximum load weight-bearing running group	198±13.25	128.18±30.4

Blood Lactic Acid Value of Rats after Running with Different Loads and Bearing Weights and after Non-weight Bearing Running

  . In Table3, the afterrunning blood lactic acid highest value was correspondingly increased with the increase of bearing weight and load of rats after 75% maximum load weight-bearing group (big load group), 35% maximum load weight-bearing group (small load group) and none weight-bearing group running. The small load weight-bearing group was averagely increased by 3.63mmol / L compared with none weight-bearing group. The big load weight-bearing group was averagely increased by 4.12mmol/L compared with small load weight-bearing group. It was indicated that running with different loads and bearing weights had extremely prominent influence on blood lactic acid values.

	Speed(m/s)	25	30	35	40
	Blood Lactic Aci(mmol/L)	3.15±0.37	3.63±0.49	5.09±0.38	5.32±0.41
	Speed(m/s)	45	50	55	60
	Blood Lactic Acid(mmol/L)	5.79±0.64	7.59±0.33	8.17±0.69	8.58±0.47
	(3)
	Table2. Blood Lactic Acid Values during Rat Increasing Load Running (n = 8)

Table 3 .

 3 Blood Lactic Acid Value of Rats after Running with Different Loads and Bearing Weights (n = 8)

	Group	Blood Lactic AcidValue(mmol/L)	P Value
	Big load group	11.09±2.09	* P< 0.05
	Small load group	6.97±1.12	** P< 0.01
	None weight-bearing group	3.34±0.43	*** P< 0.05
	* Big load group was compared with the small load group; ** big load group was compared
	with the none weight-bearing group; *** small load group was compared with none weight-
	bearing group.

Table 4 .

 4 Rat Serum CK and CK-MB Values (n = 8) of Running with Different Loads and

	Bearing Weights
	Group	CK	CK-MB	P Value	P Value
		(U/L)	(U/L)	(CK)	(CK-MB)
	None weight-bearing group Small load group group Big load	150.65±18.39 301.03±28.56 690.05±87.05	27.89±35.78 66.01±18.52 63.29±31.77	*P< 0.05 **P<0.01	*P< 0.05 **P> 0.05


Linyi Li
email: linyi_li@hotmail.com

Jingyin Zhao

Tao Yuan

Study on Approaches of Land Suitability Evaluation for Crop Production Using GIS

Keywords: Land Suitability Evaluation, Crop Production, Climatic Potential Productivity, Grid Segmentation, GIS

In this paper, an approach to assessing ecological suitability of crop growth was developed using GIS, through a case study of maize planting in Jilin Province, in the northeast China, in order to improve the accuracy and objectivity of the evaluation, promote the rationality of crop distribution, and increase the efficiency of the agro-resource utility. Climate and soil conditions in the region were taken into consideration in the research. The crop potential productivity under the climatic condition of 46 meteorological stations in the region, namely photo-temperature-water climatic potential productivity, was calculated based on the meteorological data of the precipitation, temperature, wind speed and sunshine hours from 1980 to 2006, and the crop potential productivity was used as index to evaluate the climatic suitability for crop growth. The point data were interpolated in a GIS environment and formed climatic suitability map. The assessment of soil suitability for crop growth was derived from soil fertility map drawing on the basis of information on 12 parameters, such as terrain, depth of soil body, thickness of black earth, soil erosion and organic mater. The ecological suitability map is completed using methods of spatial overlay in GIS. Comparing with the crop yield map that based on the statistical yield of 30 years in each county, the result of ecological suitability assessment is consistent with the yield. Therefore this method is feasible to assess the ecological suitability in the region.

Introduction

Land suitability evaluation is the process of assessment of land performance when land is used for specified purposes. Adaptation of crop growth to the potentialities and constraints of local agroecologies is a key principle of sustainable land management. The principal objective of land evaluation is to select the optimum land use for each defined land unit and the conservation of environmental resources for future use. Detailed objectives can vary considerably according to the purpose and scale of land evaluation [START_REF] Van Ranst | Land Evaluation Part I, Principles in Land Evaluation and Crop Production Calculation[END_REF].

Large area lands are used for grain crop production in the northeastern China. The degree of land suitability for crop production is quite different by reason of the diversity of climate, complexity of soil and variety of terrain. Land suitability is the fitness of a given type of land for a defined use, but large-scale crop production can not arrange optimum crop for each land unit. Proper land suitability classification approaches are needed to meet the demand of evaluating land suitability for crop growth and large-scale crop production management.

Photo-temperature-water climatic potential productivity, calculated based on the meteorological data of the precipitation, temperature, wind speed and sunshine hours, etc., was taken as indicator for climatic suitability evaluation, and soil fertility composite index based on terrain, depth of soil body, thickness of black earth, soil erosion, organic mater, etc. was taken as index for soil suitability evaluation. Taking maize production in Jilin province as a case study, this research brought forward an approach to evaluating land suitability for large-scale crop production using the spatial analysis and overlay function of geographic information system.

Methods

Index and method for climatic suitability evaluation

The crop climatic potential productivity (photo-temperature-water potential productivity) is the highest theoretical productivity of the crop when crop makes full use of the present climatic resources in this area with the most appropriate technology and managerial techniques. The climatic potential productivity is not the real productivity in the area, but it could be taken as the indicator for evaluating climatic suitability for crop production. The higher the climatic potential productivity is, the more suitable the climate in the area is for the crop growth.

Climatic potential productivity was calculated by the formula as follows [START_REF] Deng | Study on Light and Temperature Resources and Climatic Productivity of China[END_REF]:

) ( ) ( ) ( W f T f Q f Y = (1)
Where: Y is Climatic potential productivity f(Q) is photosynthetic productivity and is the utmost productivity of the crop when crop makes full use of solar radiation in this area with the most suitable temperature, water, soil fertility, technology and managerial techniques.

1 ) 1 ( ) 1 )( 1 ( ) 1 )( 1 ( ) ( - - - - - Φ - - = ∑ H X a Q Q f ω γ ρ ε
(2) ΣQ is total radiation per unit area and per unit time casting on the area, ε is the ratio of photosynthetically Active Radiation(PAR) to total solar radiation, αis plant absorption rate for PAR, ρ is absorption rate of nonphotosynthesis organs, γ is limitation rate of photosaturation, Φ is quantum efficiency, ω is loss rate of respiration, X is ash content in the product harvested , H is water content in the product harvested. f(T) is temperature reversionary function

⎪ ⎪ ⎪ ⎩ ⎪ ⎪ ⎪ ⎨ ⎧ ≥ < < ≤ - < ≤ < ≤ - < ≤ - = C T C T C C T C T T C C T C T C T C T T f o o o o
⎪ ⎩ ⎪ ⎨ ⎧ ≥ - < - < - = m m m ET R C ET R C ET R C W f ) 1 ( 0 ) 1 ( 0 ) 1 ( ) ( (4) 0 ET k ET c m =
(5) R is precipitation, C is runoff coefficient, ETm is crop water requirement, ET0 is reference evapotranspiration, Kc is crop coefficient.

ET0 was calculated by modified Penman equation.

Index for soil suitability evaluation

Some soil elements that influenced crop production such as organic mater, total nitrogen, available N, available P, depth of soil body, depth of black earth, terrain and soil texture were derived to evaluate the suitability of soil. The range of each element is divided into 3-5 level scales on the basis of their degrees of suitability or limitations to the crop growth. The different levels are defined as follows: Level 5: the characteristic (quality) is optimal for crop growth. Level 4: the characteristic is nearly optimal for crop production. Level 3: the characteristic has moderate influence on yield decrease and benefits can still be made.

Level 2: the characteristic decrease the yield and the land becomes marginal for crop growth.

Level 1: the characteristic may prohibit the use of soil for crop cultivation. The suitability of each soil unit was evaluated on the basis of soil fertility composite index, which accumulates all the level numbers of each element of this soil unit. The higher the accumulated numerical value is, the more suitable the soil is for crop growth.

Land evaluation methods

Classification of climatic suitability.

Corresponding to each meteorological station, there is a numeric value of climatic potential productivity calculated. Classification of climatic potential productivity was made using spatial interpolation method and a classification map was formed in geographical information system. The map indicated that the region with high value had more suitable climate for crop growth.

Classification of soil suitability.

For large scale area, complex landform and soil quality determined that there are big differences among the suitability of soil units. The problem is how to classify soil units according to their fertility. The methods we used were: first, to form the polygon map of soil fertility composite index on the basis of soil type map by calculating soil fertility composite index of each soil unit in GIS platform. Next, segment the study area by rectangular grid of a certain size. The size of grid was determined by the scale of the study area and research objective. Then, calculate the weighted average soil composite fertility index of each grid taking the area of the soil with different types in the grid as weighting, and assign the value of the average fertility on the center of the grid. Finally, form the map of soil suitability classification on the basis of the values of fertility in each grid by spatial interpolation method.

Synthetical evaluation of land suitability.

Overlay the climatic and soil suitability map to synthetically evaluate the land suitability.

The GIS platforms used were Arcview 3.2 and ArcGIS 9.0.

Case Study

Study Area

Jilin province is located in the central part of northeast China(122-131°E , 41-46°N ), covering the area of 187,400 square kilometers, The eastern part of the province is the mountainous area of the Changbai Mountains with an elevation of over 1,000 meters and the Jidong hilly land about 500 meters above sea level. The western part of the province is the Songliao Plain, whose low and level western section is the grain producing area of the province. The soil type changes from dark-brown earth, black soil to chernozem from east to west. The annual time of sunshine is 2,200-3,000 hours. The annual average effective accumulated temperature is 2,700-3,600℃. The precipitation of the province in a year is 550-910 mm and the frost-free period lasts 120-160 days. Jilin is one of the most important commodity grain producing areas in China. It abounds with soybean, corn, sorghum, millet, rice, small red bean, wheat, tuber, sunflower seed, beet and tobacco. Compared with the test result, the result calculated by penman equation was more accurate than that of the other methods. We used this value of Kc in this research to calculate water requirements.

Data

Climatic potential productivity was calculated using the methods mentioned above. The higher the value of climatic potential productivity was, the more suitable the climate was for crop growing. The classification of climatic suitability in Jilin Province was made by equally dividing the range of the numerical value of climatic potential productivity of all counties into 8 level and mapping in GIS software. The evaluation map is presented in Fig. 1.

Fig. 1. Classification of climatic suitability for maize production in Jilin Province

From the view of climatic conditions, mid-eastern Jilin Province is the most suitable region for maize production, including most part of Ji'an, Tonghua, Liuhe, Meihekou, Dongfeng, Liaoyuan County and a part of Yongji and Jiaohe County. Light, heat and water resources are abundant in the farming season in this region. The climatic suitability gradually decreases from this region to the east and the west region respectively. The eastern part of Jilin Province is full of water resources, but is lack of light and heat resources. Maize production in the western part is restricted by the water condition despite the abundance of light and heat resources.

Soil suitability evaluation.

Soil elements which influence crop production were used to analyze soil fertility, including: organic mater, total nitrogen, available N, available P, depth of soil body, depth of black earth, thickness of strata feature, terrain, soil texture, soil moisture, obstacle factors, soil erosion, and unit production. The irrigation was not taken into consideration because maize planting is rainfed in Jilin Province. The different level scales of suitability or limitation were defined in table 1[6]. Soil suitability was divided into 8 levels on the basis of soil fertility composite index in Jilin Province. The evaluation result was presented in Fig. 2. The most suitable region for maize production expresses zonary distribution from Yushu, Nong'an to Changchun in Jilin Province. In general, the soil in the eastern humid semi-mountainous area and the western part of central semi-humid liable-drought plateau area, with sufficient or moderate soil moisture, plain or slightly uneven terrain, thick soil body and black earth, no or mild erosion and rich nutrients, is suitable for maize production. In the eastern part of central semi-humid liable-drought plateau area, soil is moderately eroded and the soil nutrients in these areas are less nourishing than those in the region mentioned above. In the western semi-humid semiarid transition plain region and semiarid plain region, the surface soil is liable to erosion and black earth layer is thin due to the rugged or long gentle slope terrain, and soil condition restricts crop production.

>250 >200 >150 >120 250~200 200~150 150~120 120~90 200~150 150~120 120~75 90~60 150~120 120~75 75~30 60~30 120~75 <75 <30 <30 1 2 3 4 Available P (mg/kg) >30 >25 >20 >15 30~25 25~20 20~15 15~10 25~20 20~10 15~10 10~5 25~20 10~5 10~5 5~3 <10 <5 <5 <3 1 2 3 4 Depth of soil body (cm) >100 100~50 50~30 <30 Depth of black earth (cm) >50 50~30 >30 >20 30~20 30~20 <20 <20 <20 Yellow exposure (1) (2) (3) Thickness Burying depth Depth of calcic horizon >200 >50 >30 200~100 100~50 <50 <30 (4) (5) (6)

Synthetical evaluation of land suitability.

Mapping land suitability for maize production was made by overlaying the climatic and soil suitability in GIS platform. Result of evaluation was presented in Fig. 3.

Fig. 3. Classification of ecological suitability for maize production in Jilin province

Ecological condition is suitable for maize growth in the central part and mideastern part of Jilin Province. Light, heat and water resource are rich in the maize growing season. Soil condition satisfies the need for maize growth. In fact, there are several national commodity grain bases in the region. Most of eastern mountain area is covered with forest because the cool and cold climate is not suitable for crop cultivation. In the western part of Jilin Province, the poor soil condition and deficiency of water restrict the utility of light and heat resources, therefore the ecological condition is unsuitable for maize production.

Conclusion

Comparing with the crop yield map which is based on the statistical yield of 30 years in each county, the result of ecological suitability assessment is approximately consistent with the yield. Crop production is determined by two major aspects, the aspect of land physical resources such as soil, topography, and climate, and the aspect of socioeconomic resources such as management level, market position, and other human activities. The former are relatively stable properties, while the latter are much more variable and dependent on social and political decisions (E. [START_REF] Van Ranst | Land Evaluation Part I, Principles in Land Evaluation and Crop Production Calculation[END_REF]. This method is feasible to assess the ecological suitability in the region. More socioeconomic factors should be taken consideration during crop structure arrangement.

The crop climatic potential productivity (photo-temperature-water potential productivity) is the highest theoretical productivity of the crop when crop makes full use of the present climatic resources in this area with the most appropriate technology and managerial techniques. The climatic productivity could properly evaluate the climatic suitability for crop growth by establishing the relationship between crop product and the light, heat and water resources.

Analyzing spatial data in large scale area, it is difficult to extract the regularity among data due to the complexity of distribution of spatial attribute. Geographic information system is increasingly applied in the analysis of complex spatial data by its powerful function of data management, spatial analysis and graphics display. In this research, grid segmentation method is used to extract the regularity of soil fertility in large area in GIS. The analysis result is more rational and reliable.

In the paper an approach to assess the ecological suitability of land is suggested. Parameters in the method such as crop coefficient, grid size and the number of classification grades should be adjusted according to the crop species, scale of area and purpose of analysis.

1

 1

1 : 1

 11 500,000 soil map and attribute data were provided by Soil and Fertilizer Station of Jinlin Province. The data of crop yield, planting area of 30 years in each county were obtained from Statistics Bureau of Jilin Province. Meteorological data of precipitation, temperature, wind speed and sunshine hours from 1980 to 2006 in 46 meteorological stations were provided by Meteorological Bureau of Jilin Province. Climatic suitability evaluation. The value selection of the parameters in the photosynthetic productivity calculation formula were as follows [2,3,4]: ε=0.49,α=0.48,ρ=0.10,γ=0.01,Φ=0.224,ω=0.30,X =0.08,H =0.14 Y. Shan calculated the water requirements of maize in the Bajiazui irrigation area in Qingyang city, Gansu Province. Modified Penman equation was used to calculate reference evapotranspiration, and crop coefficient Kc was obtained by experiment[2].

Fig. 2 .

 2 Fig. 2. Classification of soil suitability for maize production in Jilin Province

Table 1 .

 1 Definition of level scale of soil evalution elements

	Levels	5	4	3	2	1	Note
	Organic	>6	6~5	5~4	4~3	3~2	1
	mater	>5	5~4	4~3	3~2	<2	2
	(%)	>4	4~3	3~2	2~1	<1	3
		>3	3~2.5	2.5~1.5	1.5~0.6	<0.6	4
	Total	>0.3	0.3~0.25	0.25~0.20	0.20~0.15	0.15~0.1	1
	nitrogen	>0.25	0.25~0.20	0.20~0.15	0.15~0.10	<0.1	2
	(%)	>0.20	0.20~0.15	0.15~0.10	0.10~0.05	<0.05	3
		>0.15	0.15~0.125	0.125~0.075	0.075~0.03	<0.03	4
	Available N
	(mg/kg)

Acknowledgements: Research funded by project from STCSM (08DZ2210600, 08QA14058) and National Key Technology R & D Program (2008BAD96B01). Many thanks to the staff in Soil and Fertilizer Station, Meteorological Bureau, Jilin Province for providing soil and meteorological data.


* Cfd

Liming Chen

Liming Xu

Simulation Simulation Simulation Simulation and and and and Design Design Design Design of of of of Mixing Mixing Mixing Mixing Mechanism Mechanism Mechanism Mechanism in in in in Fertilizer Fertilizer Fertilizer Fertilizer Atuomated Atuomated Atuomated Atuomated Proportioning Proportioning Proportioning Proportioning Equipment Equipment Equipment Equipment Based Based Based Based on on on on Pro/E Pro/E Pro/E Pro/E and and and and CFD* CFD* CFD

Keywords: Precision agriculture, Fertilizer Proportioning, Mixing mechanism, Multiple-fan-type paddle, CFD fluid dynamics analysis

Precision agriculture is the developing trend of modern agriculture, and the rational utilization of fertilizer is one of the key technologies in the precision agriculture, which needs to fertilize variably according to the crop needs and soil fertility conditions. Thus a fertilizer automated proportioning equipment is developed to Proportion the three fertilizers: Nitrogen fertilizer, Phosphorus fertilizer and Kalium fertilizer. The fertilizer will take greater effect after mixing sufficiently, so this paper mainly researched the mixing mechanism. The simulation and analysis of the velocity field and flow field of the fertilizer for the two paddles worked, the spiral-type paddle and multiplefan-type paddle, are conducted respectively using Pro/E(Pro/Engineer) and CFD(Computational Fluid Dynamics) fluid dynamics analysis software. During simulation, the paddle models are meshed using the Gambit software. Then the multiple-fan-type paddle is determined to be the more suitable one through the FLUENT software.

Introduction Introduction Introduction Introduction

Agriculure is the foundation of our country's economy, but the rough-type form of agriculture is no longer able to meet the development requirements of modern agriculture. Therefore, the development of precision agriculture has become an inevitable trend. Fertilizer, as one of the Production materials in agriculture can increase the crop yield. But for a long time, the extrude Problem of fertilization is illogical fertilizer Proportion and low utilization ratio, high fertilizer input, especially the phosphorus fertilizer input, which results the nutrient input imbalance and the increasing fertilizer input cost . From 1978 to1995, the national fertilizer consumption has increased by 97%, but grain output has increased by only 36% while the crop yield increased by 1%, and the fertilizer amount applied increased by almost 3%. The average fertilizer utilization ratio is less than the developed countries by more than 10%. The utilization ratio of Nitrogen is 30%, Phosphorus is from 10% to 25%, Kalium is from 40% to 50%. The increasing fertilizer consumption with low utilization ratio results the decreasing fertilizer returns, serious soil pollutions and groundwater, food contamination. If the fertilizer utilization ratio is enhanced by 10%, 100Mt fertilizers will be saved, which comes up to saving about 10 billion RMB Therefore, the reasonable fertilizer amount according to the crop need can enhance the fertilizer utilization ratio and reduce the environmental pollution.

The precision variable-fertilizing technology determines the usage amounts for different kinds of fertilizers according to the soil fertility condition and the crop need.

The Proportioned fertilizers should be mixed sufficiently so as to achieve the best effect. Thus it is necessary to design and simulate a mixing mechanism for different kinds of fertilizers.

The key component of the Proportioning mechanism is the stirring paddle, which is closely related to the mixing time and the mixing effect for different kinds of fertilizers. For mechanical mixing,the paddle is the only source of momentum and has close relationship with the fluid flow in the mixing tank. Thus it is very important to design the paddle. This paper mainly designs and simulates two types of paddles in order to choose a more suitable one.

Many researchers have achieved a lot on the variable fertilization technology. The "SOILECTION" fertilization system has been developed by America Ag-chen Equipment Company. It could be applied to solid and liquid fertilizers. This system applied air-seeder and no-tillage seeder can change the used amounts of seeds and fertilizers, even it can change the Proportion of three kinds of fertilizers or seeds. The "SOILECTION" has the advantages of simple, easy to control, high precision and reliability, but it is expensive (Zhao Wuyun et al., 2007). The ST820 variable ratio fertilizer machine with the plant function has been developed by Case Company, it could use the IHAFS software to get the prescription map with computer and then generate prescription file which will be stored in the PCMCIA card. The PCMCIA card could be inserted into the variable ratio controller to Provide preparations for the fertilizer machines to fertilize automatically (Zhao Jun, 2004).

The fertilizer machines and equipments for plant Protection in France have the highest automation level among all the agricultural machineries. The French AMASAT variable ratio fertilizer control system has been applied to various types of the centrifugal fertilization machines (Zhang Xiaohui et al., 2002).

At present, there are two variable ratio fertilizer machines at home, 2F-VRT1 variable ratio fertilizer machine and 1G-VRT1 rotary variable fertilizer machine. The two machines control the fertilizing amount mainly according to the preferences set by users or the prescription map calculated by the upper computer. The machines can receive the GPS location information and the operating speed signals while they are working in real time, then the rotation speeds of the fertilizer-driven systems are adjusted automatically to achieve the purpose of variable ratio fertilization. The machines also support the manual and automatic control modes, and they can broadcast fertilizers evenly on the soil surface. The two machines are applicable to the fertilization work before sowing seeds, the variable fertilization work for the striking root fertilizer of the winter wheat and the variable fertilization work for forages (Wang Xiu et al., 2008). There are many researches on the variable ratio fertilization, but the researches are mainly focused on the liquid fertilizer or just for one kind solid fertilizer. Thus, there are few researches focused on the mixing mechanism for the fertilizer automated Proportioning equipment. In China, the solid fertilizers are used mostly currently, therefore, it is very important to develop a mixing mechanism used in the variable ratio fertilizer machine for several kinds of fertilizers.

The The The The Variable Variable Variable Variable Ratio Ratio Ratio Ratio Fertilizer Fertilizer Fertilizer Fertilizer Mechanism Mechanism Mechanism Mechanism for for for for Precision Precision Precision Precision Agriculture Agriculture Agriculture Agriculture

An automated matching mechanism for three kinds of fertilizer (N, P, K) is developed in this paper, Fig. 1 In order to gather the fertilizers on both sides to the center, the spiral paddle (Fig. 3) moves in two-opposition-way, and the fertilizers is decentralized when the rotation direction of the spiral paddle changes The lines with deep color in Fig. 4 are path lines of certain point on the paddle.

Fig. 4 shows that when the spiral paddle rotates reversely, the path line is outward, so it indicates that the spiral paddle can achieve the effect of decentralizing fertilizers.

When the spiral paddle rotates forward, the path line is inwards, so it indicates that the spiral paddle can achieve the effect of gathering fertilizers. After the models were meshed and the mesh quality was checked, the models should be exported in the form of mesh file, and be read into FLUENT to finish the numerical simulations. FLUENT software is designed for fluid analysis, which can simulate fluid floe with complex from incompressible to highly compressible. As a result of a variety of solving methods and multi-grid convergence acceleration technique, the FLUENT software can achieve the best convergence speed and accuracy.

The standard k-ε turbulence model is used to simulate the single-phase flow fields, and the relationship between the paddles movement and the static wall of the mixing tank is apProached using MRF. The area of fluid1 is set to be motive while the area of fluid2 as static. The paddle is set to be moving wall with the movement of rotation, in other words, the paddle rotation is synchronized with fluid2.The momentum exchange and energy exchange between fluid1 and fluid2 are achieved through the interface.

Fig. 11 and Fig. 12 are the convergence curves of the two types of paddles. From the two figures, it can be seen that the residuals of the parameters are convergence, which indicates that the used algorithm is feasible. From the above simulation and analysis, it can be seen that the two types of paddles both can gather up fertilizers into the center, on the contrary, they also can decentralize fertilizers. Thus, as long as there is one paddle installed in the mixing tank with certain rules to rotate, the fertilizers can be mixed sufficiently.

The two types of paddles both can mix fertilizers sufficiently according to the analysis of the speed cloud maps and the flow field maps. Considering the high manufacturing cost of the spiral stirring paddle, and it causes the high-cost mix mechanism, the multiple-fan-type paddle is chosen to be the stirring paddle for the automated Proportioning equipment.

Conclusion Conclusion Conclusion Conclusion

Precision variable ratio fertilization can increase the fertilizer utilization ratio and decrease the environment pollution. Fertilizer automated Proportioning equipment can Proportion the three kinds of fertilizers: Nitrogen fertilizer, Phosphorus fertilizer and Kalium fertilizer, according to the crop needs and soil fertility conditions. The Proportioned fertilizers are spread into the soil after being mixed sufficiently. In the equipment, the stirring paddle is one of the key components. Thus,this paper mainly researched the models designed by PRO/E through motion simulation , comparing and analyzing the velocity fields and flow field by the CFD software. The research results supply enough evidences to determine the more suitable paddle. According to the paper, the following conclusions can be launched:

(1). Design the geometric structure and finish the motion simulation of the two paddles: the spiral paddle, the multiple-fan-type paddle by using PRO/E. The result shows that the two paddles can gather up or decentralize the fertilizers.

(2). Mesh the two models by using the GAMBIT software. In the meshing Process, the mesh size and grid type should be chosen correctly, which is closely related to the simulation results in the FLUENT software.

(3). Analyze the velocity fields and flow fields of the two paddles by using the FLUENT software. According to the analysis, the multiple-fan-type paddle is determined to be more suitable, which can make fertilizers mixed sufficiently.

The multiple-fan-type paddle is chosen through simulation and comparison. In further researches, this paddle could be manufactured and installed in the automated matching mechanism.

Acknowledgements

  In addition, Israel and China Agricultural Research Institution have developed the liquid fertilizer automated Proportioning machine, which can finish the irrigation work. The one developed by China Agricultural Research Institution mainly control the fertilizer Proportion by detecting the PH value and electrolytic conductivity of the Proportioned fertilizer liquid. In the Proportioning Process, it needs to adjust the flow of each kind of fertilizer manually. The machine is mainly used for the greenhouses.

1

 1 Fig. Fig. Fig. Fig. 1 1 1 1. . . . The work block diagram

Fig. Fig. Fig. Fig. 3 3 3 3 .

 3 Fig. Fig. Fig. Fig. 3 3 3 3. . . . The structure chart of the spiral paddle

Fig.

  Fig. Fig. Fig. Fig. 4 4 4 4. . . . The path lines of the spiral paddle

3.2 3 .2 3 .2 3 . 2 5 )4 4 4 4 Fig. Fig. Fig. Fig. 7 7 7 7 .

 3332547 Fig. Fig. Fig. Fig. 5 5 5 5. . . .The geometric structure of the multiple-fan-type paddle

Fig. Fig. Fig. Fig. 9 9 9 9 .

 9 Fig. Fig. Fig. Fig. 9 9 9 9. . . . The grid chart of the spiral paddl.

Fig. Fig.

  Fig. Fig. Fig. Fig. 10 10 10 10. . . . The grid chart of the multiple-fan-type paddle

Fig. Fig.

  Fig. Fig. Fig. Fig. 11 11 11 11. . . . The convergence curve of the spiral paddle

Fig. Fig.

  Fig. Fig. Fig. Fig. 13 13 13 13. . . . The speed cloud map of the spiral paddle

Fig. Fig.

  Fig. Fig. Fig. Fig. 15 15 15 15. . . . The flow chart of the spiral paddle

  The first author would like to acknowledge her tutor in College of Engineering in China Agricultural University for her help.

	Technology	Pillar	Program	during	the	Eleventh	Five-Year	Plan
	(2007BAD89B00).

. Acknowledgements. Acknowledgements. Acknowledgements.Funding for this research was Provided by National Science &


Lianming Xia

Xiangyou Wang

Duanyang Geng

Qingfeng Zhang
email: zhangqingfeng1844@163.com

Performance Performance Performance Performance M M M Monitoring onitoring onitoring onitoring S S S System ystem ystem ystem for for for for P P P Precision recision recision recision P P P Planter lanter lanter lanter B B B Based ased ased ased on on on on MSP430 MSP430 MSP430 MSP430-CT171 -CT171 -CT171 -CT171

Keywords: Monitoring system, Precision planter, photoelectric sensor, Singlechip microcomputer, Bluetooth technology, Stepper motor

Based on full consideration of the working conditions in the filed, a monitoring system for precision planter was designed using the technology of sensor, single-chip Microcomputer and wireless transmission. In this system, photoelectric sensor and MSP430 were used to detect the performance of the seeder-metering device and process the data respectively. It mainly detected the miss index, multiple index, seeding amount, seeding rate, the plant spacing and other performance parameters. In the sound-light alarm system, a indicator light was used to show the fault type and location, then the detected data and fault can be displayed on the LED. In order to acquire and analyze the data at realtime, a wireless transmission system based on Bluetooth was designed. FS-BT485Abluetooth module was used to communicate with the host computer. To ensure the uniformity of drilling and sowing, a stepping motor was used to control the seeding rate in the laboratory. The results showed that reliability of the sound-light alarm system was 100% and the photoelectric sensor could detect more than 95% seeds passed it.

Introduction Introduction Introduction Introduction

The seed-metering device is the core component of precision planter. Precision planting is defined as the placement of single seeds in the soil at desired plants spacing [START_REF] Singh | Optimisation of Design and Operational Parameters of a Pneumatic Seed Metering Device for Planting Cottonseeds[END_REF][START_REF] Datta | Development of Some Seeders with Particular Reference to Pneumatic Seed Drills[END_REF]. The seed-metering devices which are often used can be divided into two categories such as mechanical and pneumatic. Whether either seed-metering device is used, the flow process of the seeds is all invisible. So, we can not determine whether the sowing quality is good or not only by vision and hearing. With the sensors, the intelligent monitoring system for precision planter can monitor the sowing process and alarm in different ways. Once the faults happen, the system can tell the location and kind of the faults timely. So, it is of great economic and practical significance for studying the monitoring technology for precision seed-metering device. By far, we could monitor the metering quality by the methods such as manual monitoring, photoelectric effect, piezoelectric effect and high-speed photography [3, 4 and 5]. Based on the technologies of single-chip microcomputer, sensor, information and virtual instrument, the monitoring systems for precision planter have gained great progress in recent years. In this paper, a performance monitoring system for precision planter based on MSP430 is designed. In this system, the infrared photoelectric sensor is installed near the furrow opener. In order to better guarantee the accuracy of monitoring, three or four photoelectric sensors are installed on the seed tube symmetrically and evenly. Every seed tube represents one row and an indicator light is used to show the performance of the planter [START_REF] Gong | Development of Automatic Monitor System for Seed-cell Fill on Inside Precise Meter[END_REF][START_REF] Dong | The Single-chip Control Applied in the Seeding-machine[END_REF]. When the pulse signals produced when the seeds pass through the sensor are transmitted to single-chip microcomputer, it decides whether the pulse signals are normal or not. Sound and light alarm signals will be sent out when seeds absence, the grain box is empty and the metering devices are blocked. The indicator light is extinct when the planter is at normal operation and the light will blink when there is something wrong with the planter such as seeds absence [START_REF]MSP430 Universal synehronous Receive/Transfer communication interface[END_REF]. The schematic is showed in the Fig. 1.

Design Design Design Design of of of of the the the the M M M Monitoring onitoring onitoring onitoring S S S System ystem ystem ystem

The monitoring system is mainly composed of photoelectric sensor, alarm system, display system and wireless data transmission system.

D D D Design esign esign esign of of of of P P P Power ower ower ower C C C Circuit ircuit ircuit ircuit

In order to work under normal conditions, MSP430 must be supplied with 3.3 voltages by the system. Because of the unstable voltage of the 12v level on the precision planter, it is necessary to be converted to 3.3v. The power circuit is showed in Fig. 2.

.2 2 2 2 C C C Circuit ircuit ircuit ircuits s s s of of of of the the the the S S S Sensor ensor ensor ensors s s s

The photoelectric sensor system is composed of photoelectric coupled device and LM339. The coupled device is composed of infrared LED and phototransistor. LM339 is the integrated circuit of four voltage comparator. When the seeds pass through the range covered by the LED, the phototransistor will close and up level is output. Then the level will be low after filter by LM339.

The situation is contrary when there is no seed passing through the range covered by the LED. The output pulse signals are connected to port P2 which is the external interrupt of MSP430 [START_REF] Raju | UltraLow Power RC Timer Implementation Using MSP430[END_REF]. The circuit of the photoelectric sensor is showed in Fig. The system will alarm when the grain box is empty, the seed tube or the furrow opener is blocked by debris and the metering devices stop working. At the same time, the indicator light will blink to tell the driver that there is something wrong with the planter. The circuits are showed in Fig. 4 and Fig. 5. The function of the display circuit is to show the data and indicate faults. A LCD with better backlight effect is used in order to display the screen contents clearly. In this system, we choose LMC240128ZK as the display module. The LCD module has 8-bit parallel data interface and it is directly connected to the pin of MSP430. The pins of the module can be divided into data pin, control pin and power pin. Its circuit is showed in Fig. 6.

3.5 5 5 5 The The The The W W W Wireless ireless ireless ireless D D D Data ata ata ata T T T Transmission ransmission ransmission ransmission S S S System ystem ystem ystem

In this paper, a wireless data transmission system based on Bluetooth is used because of its high efficiency, reliability and security. The system is composed of signal collection and transmission units, MSP430, the hopper computer and the Bluetooth communication device. The key of the system is to choose the Bluetooth module. In this system, the Bluetooth module used is FS-BT485A serial adapter [START_REF] Lee | An Efficient Scheduling Scheme for Bluetooth Seattmets Using the Snif Mode[END_REF][START_REF] Petrioli | Degree-Constrained Multi-Hop Scatternet Formation for Bluetooth Networks[END_REF]. The block diagram of the wireless data transmission system is showed in Fig. 7. The function of the software system is:

To initialize the single-chip microcomputer and other chips; to call data collection sub-program; to call sound and light alarm sub-program when there is something wrong with the planter;

to detect whether the amount of sowing is appropriate; to adjust the sowing speed and metering speed to keep them consistently; to call the sound and light alarm sub-program; to display the data on the LCD for the driver to watch or reference; to analyze and store all the performance parameters; to read the data on the hopper computer freely; to call the sub-program of wireless data transmission. The flow chart is showed in Fig. 8.

Experimental Experimental Experimental Experimental T T T Test est est est and and and and A A A Analysis nalysis nalysis nalysis

Due to constraints, experiments are only conducted in the laboratory, and the experiments included data collection and alarm testing which were under artificial conditions.

The materials used in this paper were the monitoring system for precision planter, infrared photoelectric sensors, Bluetooth device, PC, a stepper motor, some maize and wheat seeds.

In the test, the performance of the monitoring system could be tested by counting the number of seeds passing through the sensors and the number of chosen seeds before the test. At the same time, we could test the working conditions of the monitoring device by making some faults under artificial conditions. The results were showed in Table1.

Table1. Table1. Table1. Table1.The test of the performance of the sound and alarm system In the alarm test, the furrow opener and the metering devices were blocked 180 times and 200 times respectively. At the same time, the stepper motor was constantly started and stopped 220 times in the case of seeds presence. The results of alarm tests showed that the reliability of the alarm system was 100%.

In the data collection tests, photoelectric sensors were used to detect the numbers of maize and wheat. The results were showed in table 2. Table Table 2 2 2

Table Table

2.The results of the data collection test

From the results, it could be concluded that the photoelectric sensors detected 95% wheat seeds and 96% maize seeds passing through it. It could be seen that the seeds detected were less than 100% because of the anti-dust properties and seed-tube coverage of the photoelectric sensor. And which reduced the precision and reliability of the monitoring system. So, it is necessary to improve the sensor's anti-dust ability and coverage of the seed tube. The following work is to solve these problems and this will improve the practical ability of the monitoring system.

According to current domestic and international planter monitoring systems' status and development trends, combined the theory of automation and the information technology, a design scheme using single-chip computer as the control center and Bluetooth wireless transmission for the secondary was proposed. For the present situation of precision planter, the performance of the existing planter can not fully meet the requirements of agriculture, so it is necessary to design monitoring systems with better performance. First of all, the monitoring system should have better stability, reliability and universal property. Secondly, integrated monitoring system equipped with compensator should be designed based on the technologies of mechatronics and automatic control [START_REF] Xia | The Testing Technique Research of Seed Absence on Precise Seed-Meter Based on Virtual Instrument[END_REF]. Monitoring system with automatic compensator is the main developing trend in the following years.

2 2 2 2 S

 2 S S Structure tructure tructure tructure and and and and W W W Working orking orking orking P P P Principle rinciple rinciple rinciple of of of of the the the the S S S System ystem ystem ystem

Fig. Fig. Fig. Fig. 1 . 1 . 1 . 1 .

 1111 Fig. Fig. Fig. Fig. 1. 1. 1. 1. Structure of the monitoring system

Fig. 2 .

 2 Fig.2. Fig.2. Fig.2. Fig.2. Power circuit of MSP430

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 3

Fig

  Fig Fig Fig Fig. . . .3 3 3 3. Circuit of the photoelectric sensor

Fig. Fig. Fig. Fig. 4 . 4 . 4 . 4 .

 4444 Fig. Fig. Fig. Fig. 4. 4. 4. 4. The circuit of sound alarm

Fig. Fig. Fig. Fig. 6 . 6 . 6 . 6 .

 6666 Fig. Fig. Fig. Fig. 6. 6. 6. 6. The circuit of display

Fig. Fig. Fig. Fig. 7 . 7 . 7 . 7 .

 7777 Fig. Fig. Fig. Fig. 7. 7. 7. 7. The block diagram of the wireless data transmission system

Fig. Fig. Fig. Fig. 8 . 8 . 8 . 8 .

 8888 Fig. Fig. Fig. Fig. 8. 8. 8. 8. Flow chart of the software system