Fengrui Chen

Guangxiong Peng

Wei Su

Yaochen Qin

Xi Li

Small-scale Evaluation of Tobacco Planting Suitability Based on Spatial Information Technology

Keywords: Spatial Information Technology, Planting Suitability Evaluation, Tobacco, Scale Analysis

Crop planting suitability evaluation is important for agricultural production. In this study, a comprehensive evaluation framework which combined geographical information system, remote sensing, global position system and geostatistics with other models was presented to evaluate the tobacco planting suitability on a small scale. Ten indicators from three respects of climate, terrain and soil nutrients were selected to build evaluation indicator system. The results show that 70% of study area is suitable for tobacco planting. However, the spatial distribution of current tobacco fields is some degree of irrationality, and 20% of fields are located in moderate or not suitable area. Through scale analysis of the arable land, the suitable fields are mainly distributed in the middle and southern parts of the study area, which is 6324.5 ha.

Introduction

Developing precision agriculture is an inevitable direction in the world [START_REF] Lund | Practical applications of soil electrical conductivity mapping[END_REF][START_REF] Mcbratney | Future directions of precision agriculture[END_REF][START_REF] Qi | Evaluating soil quality indices in an agricultural region of Jiangsu Province, China[END_REF][START_REF] Zhang | Spatial variability of soil nutrients and GIS-based nutrient management in Yongji County, China[END_REF]. The key of actualizing precision agriculture is spatial information technology, which involves spatial data acquisition, processing and decision-making [START_REF] Mesev | Integration of GIS and remote sensing[END_REF][START_REF] Burrough | Concepts and paradigms in spatial information: are current geographical information systems truly generic[END_REF][START_REF] Peng | Remote sensing monitoring of tobacco field based on phenological characteristics and time series image-A case study of Chengjiang County, Yunnan Province, China[END_REF]. Tobacco is a special agriculture crop which products excellent tobacco leaves only in regional cultivated land. In order to improve the tobacco quality and have a high market competitiveness, it is meaningful to evaluate tobacco planting suitability based on spatial information technology, and on this basis, tobacco production is redistributed rationally.

Geographic information system (GIS) which provides powerful spatial data management and analysis functions has been widely used for manage spatial resource  Corresponding author. [START_REF] Perotto-Baldiviezo | GIS-based spatial analysis and modeling for landslide hazard assessment in steeplands, southern Honduras[END_REF][START_REF] Kamusoko | Land use/cover change and landscape fragmentation analysis in the Bindura District, Zimbabwe[END_REF][START_REF] Lane | Diatoms as indicators of isolated herbaceous wetland condition in Florida, USA[END_REF][START_REF] Figueroa | Socioeconomic context of land use and land cover change in Mexican biosphere reserves[END_REF]. In recent years, tobacco planting suitability evaluation based on GIS has been presented [START_REF] Jingxun | Analysis and Comprehensive Evaluation of Soil Fertility Status for Tobacco-Growing Areas in the North-west of Hu'nan province[END_REF][START_REF] Chen | Comprehensive Evaluation of Tobacco Ecological Suitability of Henan Province Based on GIS[END_REF][START_REF] Zhang | Comprehensive Assessment on Ecological Adaptability of Flue-cured Tobacco in Shandong Province[END_REF]. Although they can guide tobacco production overall, the evaluation results can not meet the requirements of precision agriculture on small scale. For example, there is always a meteorological station per country [START_REF] Kui-Dong | Application of climatic factors' gridding technology in tobacco-planting regionalization in Hunan Province[END_REF] and several soil sampling points standing the whole area, which will be very rough for assessment. However, few studies have focused on small-scale tobacco planting suitability evaluation such as on the county and its below scale. Furthermore, modern tobacco agriculture demands that tobacco fields not only are distributed in suitable area, but also have a certain scale. All these pose challenges to the traditional evalution method.

The increasing needs of modern tobacco agriculture and the shortage of current evaluation method stimulate a need for sophisticated methods of suitability evaluation. In this study, according to the physiological characteristic of tobacco [START_REF] Liu | Tobacco Cultivation[END_REF] and the study area condition, ten evaluation indicators from three factors of climatic, terrain and soil have been chosen to build the tobacco planting suitability evaluation indicator system on small scale. Then, in order to attain accuracy evaluation indicators, remote sensing data was used to retrieve surface parameters, and soil sampling data based on global position system (GPS) was used to attain soil nutrient information by geostatistical analysis. On the basis of analysis above, GIS combined with analytic hierarchy process (AHP) and membership function model were applied to comprehensive evaluation of tobacco planting suitability, which could provide scientific bases for sustainable development of tobacco production.

Materials and Method

Study Area

The research was conducted in one part of Huili County (E 102°13′-102°24′ and N 26°33′-26°48′ ), located in the southwest of Sichuan province, China (Fig. 1). It is one of the main tobacco production regions of Sichuan province. The highest elevation of the study area is 2390 m and the lowest is 1680 m. There are many kinds of landform types such as mountain, hill, plain and basin, which results into redistribution of light, heat and water resources, arable land mainly distribute in plain and basin area. The local climate is classified as temperate continental with annual average rainfall of 1150 mm, annual mean temperature of 15.1℃, annual sunshine of 2398 h and annual frost-free days of 240. There is big different in temperature between day and night here, and is rich in water, heat and good natural conditions, therefore, it is very suitable for high-quality tobacco production.

Data Source

Solar radiation is an important impact factor for crop growth. In order to attain high accuracy spatial distribution of solar radiation, a series of models and methods have been established and developed [START_REF] Javier | Vectorial algebra algorithms fox calculating terrain parameters from DEM and solar radiation modeling in mountain terrain[END_REF][START_REF] Lifen | Simulation of solar radiation on ground surfaces based on 1 km grid-cells[END_REF]. In this paper, the model proposed by Fu and Rich [START_REF] Fu | A geometric solar radiation model with applications in agriculture and forestry[END_REF] was used to calculate the solar radiation of the study area, which has been applied widely. The tobacco growing season is from May to July, so the mean solar radiation of this period was calculated (Fig. 2).

Owing to the sparse distribution of meteorological station, about a station per country, so it is difficult to obtain high accuracy climate data from observation site at country or its below scale. Remote sensing is an effective method, which can acquire surface parameters with high spatial resolution in any region, and compensate for the loss caused by the absence of observation stations [START_REF] Campbell | Introduction to remote sensing[END_REF]. Moderate resolution imaging spectroradiometer (MODIS) which has high temporal and spectral resolution, moderate spatial resolution and abundant products was used to retrieve land surface parameters. MODIS products in May, June and July from the year of 2005 to 2010 were adopted, and these data were downloaded from the website of national aeronautics and space administration (NASA) of the United States.

Land surface temperature (LST) is obtained from MOD11 products of MODIS with the spatial resolution of 1 km. Its average error is smaller than 0.5 K [START_REF] Zhengming | Land-surface temperature measurement from space: Physical principles and inverse modeling[END_REF][START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF]. Fig. 2 shows the mean LST from May to July in the study area.

Soil moisture is derived from temperature vegetation dryness index (TVDI) based on normalized difference vegetation index (NDVI) products and land surface temperature (LST, MOD11) products of MODIS [START_REF] Sandholt | A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[END_REF]. It can effectively reflect the spatial distribution of relative value of soil moisture, and has high correlation with the soil moisture. The value of TVDI is from 0 to 1. The soil moisture increases with the decrease of TVDI. The spatial distribution of TVDI is shown in Fig. 2. Soil sampling was carried out in April 2009, immediately after harvest of wheat and cole. In total 268 soil samples at the depth of 20 cm were taken and their precise locations were recorded by a GPS receiver (Fig. 1). Before analysis, all soil samples were air-dried and passed through a 2-mm sieve prior to analysis. Soil pH value was determined by the method of electrical potential; organic matter (OM) content was measured by dilution heat method with potassium bichromate; and available nitrogen (AN), available phosphorus (AP) and available potassium (AK) were measured by alkaline hydrolysis diffusion, molybdenum stibium anti-color method and flare photometer, respectively.

In addition, elevation and slope indicators are derived from DEM data with the spatial resolution of 20 m. Arable land is derived from the digital of thematic map with the scale of 1:50000.

Geostatistics Method

Geostatistics was used to estimate and map soil nutrients in unsampled areas [START_REF] Goovaerts | Geostatistics for natural resources evaluation[END_REF][START_REF] Isaaks | An introduction to applied geostatistics[END_REF]. Among the geostatistical techniques, 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. That is, if data appear to be highly continuous in space, the points closer to those estimated receive higher weights than those farther away [START_REF] Cressie | Statistics for spatial data[END_REF].

In geostatistical analysis, the semivariogram was calculated for each soil variable as follows:

       ) ( 1 2 ) ( ) ( ( 2
1 ) ( h N i i i h x z x z h N h r ) (1)
where z(x i ) is the value of the variable z at the sampled location x i , h is the distance lag in meters and N(h) is the number of pairs of sample points separated by h. For irregular sampling, it is rare for the distance between the sample pairs to be exactly equal to h. Therefore, h is often represented by a distance interval. For the distance lag h, the semivariance is r (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 estimates of regionalized variables at unknown locations. The spatial prediction of the value of a soil variable z at an unknown point x 0 is calculated as a weighted average:

   n i i i x z x 1 0 * ) ( ) ( z  (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 neighbourhood 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.

Comprehensive Suitability Evaluation

According to the principles of locality, similarity, diversity, integration and practicality, an evaluation indicator system based on three factors including ten indicators was built. The flow chart and processes method of this study is as Fig. 3 .

Indicator Scoring

             L 1 . 0 L U 0.1 L - U L 9 . 0 U 1 ) ( x x x x x f SR(kJ/m 2 )
S-type

150 00 -- -- 23000 AK(mg/kg) S-type 80 -- -- 180 TVDI Z-type 0 -- -- 1              L 1 . 0 L U 0.1 L - U L 9 . 0 U 1 ) ( x x x x x f
Elevation(m) parabola-type 800 1400 1800 2400 Because of different indicator units, a standard scoring function was used to score suitability indicators. The scoring of evaluation indicators on growth suitability of tobacco can be described with the fuzzy methods. The interaction between growth and quality of tobacco was different. Referring to the existed research results [START_REF] Liu | Tobacco Cultivation[END_REF][START_REF] Hu | Nutrition Principle of Flue-cured Tobacco[END_REF][START_REF] Xi | Potassium content of flue-cured tobacco from different tobacco production areas[END_REF] and the actual situation of the study area, different types of membership functions, including parabola-type, Z-type and S-type, were chosen to express their relations (Table 1). The membership degree which is between 0.1 and 1.0 can be calculated according to these membership functions, which reflects the degrees of membership.

U , L U 1 U 1 L L 1 U 1 L 0.1 0.1 1 U - U U1 - 9 . 0 0.1 L - 1 L L 9 . 0 1 ) (                        x x x x x x x x f Slope(°) parabola-
The upper limit 1.0 is referred to as the best situation of indicators which is suitable for the growth of tobacco crops, while the lower limit 0.1 is referred to as such that there is a serious deficiency in that indicator.

Weight Assignment

The indicator weight is the contribution degree of the effects of evaluation indicators to tobacco growing. There are many methods to assign indicator weight for tobacco planting suitability, such as the Delphi, AHP, regression analytical method and principal components analysis. In order to weaken subjective influence, the application of the AHP method, developed by Saaty [START_REF] Saaty | The analytic hierarchy process: planning, priority setting, resource allocation[END_REF] for environmental assessment, has been used extensively [START_REF] Lai | Group decision making in a multiple criteria environment: A case using the AHP in software selection[END_REF][START_REF] Rezaei-Moghaddam | A multiple criteria evaluation of sustainable agricultural development models using AHP[END_REF] and was used to derive the weights of indicators, which are shown in Fig. 3. The results of AHP analysis are presented in Table 2. The consistency test for single and general hierarchy storing was conducted by calculating the average random consistency index (RI). The results indicated that all RIs for single and general hierarchy storing were lower than 0.1, which means all the matrixes in the hierarchy A, B and C were logically constructed. In addition, the maximum of the eigen value and its eigen vector should be calculated and verified by the coherence indexcoherence ratio (CR). If the CR value is less than 0.1, it deems that the variance should be allowed. Or else, the matrix must be adjusted once again. In this study, CR value was 0.04, which was less than 0.1. Therefore, the analysis results were acceptable.

Calculation of Comprehensive Evaluation Values

According to the character of study area, the size of evaluation unit for tobacco planting suitability is 30 * 30 m. All the indicators should be resampled into grid unit with the spatial resolution of 30 m before comprehensive evaluation. After all indicators were scored and weighted, the integrated quality index (IQI) [START_REF] Doran | Defining and assessing soil quality[END_REF] model was used to calculate the tobacco planting suitability level in study area:

   n i i i N W IQI 1 (3)
Where W i is the assigned weight of evaluation indicator, N i is its score, and n is the number of evaluation indicators. The higher the IQI value is, the more suitable the unit is for tobacco planting. The descriptive statistics of soil pH, organic matter, AN, AP and AK for study area are shown in Table 3. In general, the soil was suitable for tobacco planting, and the means of pH, OM, AN and AP were in their optimal value ranges (Table 1). The CVs of OM, AN, AP and AK were similar, all exceeding 0.4, which indicated considerable spatial variability. However, the CV of pH was much smaller, only 0.11, which indicated significant spatial constant. The skewness of AK was more than 1, so it was necessary to transform its non-normally distributed raw data into data with an approximately normal distribution. We found that the log transformation of AK significantly improved their normality, therefore, log(AK) were used for the geostatistical analysis.

Results

1 Descriptive Analysis of Soil Nutrients

The Spatial Analysis of Soil Nutrients

The best-fit semivariogram models and parameters for pH, OM, AN, AP and log(AK) are listed in Table 4. We used variable distance lags to compute the experimental semivariogram to see how the semivariograms behave near the origin and when the distance lag increases. As listed in Table 4, the exponential model gave the best fit to the pH, AN, AP and log(AK) experimental semivariograms, except OM. For OM semivariogram, a gaussian model (C 0 =29.1, C=29.57 and range = 1720 m) produced the best fit (Table 4). Table 4 also indicates a distinctly different degree of spatially structured variations of the five nutrients. Spatial continuity for a regionalized variable can be characterized by its range. The longer the range is, the better spatial continuity becomes. Among the five nutrients, the range of log (AK) (2970 m) was the longest, so it had the best spatial continuity. In addition, spatial structured variation for a regionalized variable can be divided into three categories: strong, moderate and weak spatial dependence, corresponding to a nugget-to-sill ratio C 0 /(C+C 0 ) of < 0.25, 0.25~0.75 and > 0.75, respectively. In this study, all five variables demonstrated moderate spatial dependence, and AP had the strongest spatial dependence due to its lowest nugget-to-sill ratio (0.39). Although pH and log (AK) had similar nugget to sill ratios (0.5 and 0.52), their spatially structured variations appeared at different spatial scales as their ranges were significant different (1820 m for pH and 2970 m for log(AK)). Fig. 5 shows the spatial patterns of the five soil nutrients generated from ordinary kriging analysis based on their semivariogram parameters (Table 4). Owing to the smooth effect of kriging, all predicted value ranges of five nutrients were smaller than the corresponding sampling data. Cross validation was carried out to evaluate the accuracy of the ordinary kriging interpolation. As a result several indices, mean prediction error (ME), mean absolute error (MAE), root mean squared error (RMSE) and correlation (r) between observed and predict value were adopted. Table 5 summarizes the cross validation of ordinary kriging interpolations for pH, OM, AN, AP and AK. The results indicted that AN and AK were slightly overestimated and OM and AP slightly underestimated. According to the correlation coefficients r, pH was predicted best (r = 0.77), then AP(r = 0.72), and the worst AK (r = 0.55). The relatively low r for AK may be due to its predicted value which was transformed from the predicted value of log (AK). The spatial distribution of pH was characterized by an increasing trend from south to north, pH in most parts of the north is less than 6.0 while is more than 6.5 in most parts of the south. AP and AK have similar spatial distribution, in contrast with pH, they show decreasing trend from south to north. In addition, it is difficult to discern a trend in OM and AN. OM content in the north border is higher, and AN content is higher in the centre of study area.

Evaluation Result

Based on the method of AHP and fuzzy mathematics, the tobacco planting suitability of study area is classified into four grades. The high suitability of IQI ranges from 3.4 to 4, while the suitability of IQI ranges from 2.9 to 3.4, the moderate suitability of IQI ranges from 2.4 to 2.9, the not suitability of IQI ranges is less than 2.4. As shown in Fig. 6, the high suitable tobacco planting parts are mainly distributed in the southern part of the study area, accounting for 26.1% of the whole investigated areas. The suitable parts are 53% of the whole areas, mainly distributed in the northern and middle parts of study area. In addition, the moderate and not suitable parts are mainly distributed in the northeast of the study area, which are of 16.4% and 4.5% of the whole area, respectively. Fig. 6. The evaluation result of tobacco planting suitability and scale analysis.

Then, we analyzed the suitable condition of the current tobacco fields on the basis of the evaluation results above. In summary, most of the current tobacco fields were suitable for tobacco planting. 79.8% of which were located in suitable and higher suitable areas. However, there were more than 20% of tobacco fields which were not suitable for tobacco planting; these would result into the decline of tobacco quality. Beyond that, the size of 22.4% of tobacco fields were less than 6.7 ha, which didn't meet the demand of the large scale in modern tobacco agriculture. Therefore, it is necessary to reset the spatial distribution of tobacco planting in study area.

In modern tobacco agriculture, the tobacco fields not only are suitable for tobacco planting, but also have a certain scale, which can improve the quality and standardization of tobacco leaves. Considering the tobacco planting condition and arable land in study area, 6.7 ha was selected as the scale criterion of tobacco fields, the analysis chart and its result are shown in Fig. 2 and Fig. 6, respectively. There were 6324.5 ha of tobacco fields meeting the requirement of modern tobacco agriculture, which were mainly distributed in the middle and southern parts of the study area.

There are two main reasons for this. 1) The current layout of tobacco planting is based on the planning of the 1980s. Subject to conditions at the time, the suitability evaluation was performed at larger small, and the indicators selected were simple and parts of these were qualitative description. In addition, owing to the rapid development of China's economic, the environment conditions have changed a lot in past 30 years. 2) The tobacco planting is mainly based on family unit in this country, the family can planted tobacco according to the distribution of their field optionally; therefore, some tobacco was planted in a little field without considering the demand of scale production.

Discussion and Conclusion

The integration of GIS, RS and GPS provided a powerful tool for this study in terms of tobacco planting suitability evaluation. Ten indicators from three respects of climate, terrain and soil nutrients were selected to build evaluation indicator system. In order to measure the relation between indictors and tobacco planting accurately, the membership function and AHP method were adopted to determine the suitability of indicators and their weights. Then, tobacco planting suitability evaluation of study area was calculated by IQI model. The results show that most parts of study area were suitable for tobacco planting.

General, traditional suitability evaluation is on provincial level or state level, counties or townships are often used as the basic evaluation units. In China, most of suitable areas for tobacco planting are located in mountainous region, where the environment variables such as air temperature, precipitation, sunshine, soil nutrients and so on have significant difference in micro-region. Large-scale evaluation is very difficult to reflect these differences and not meet requirements of modern precision agriculture. While small-scale evaluation can solve these problems, it can reflect the differences of crop growth environment micro-region and makes the evaluation results more accurate.

The distribution density of meteorological stations is about 50*50 km per station in China, so it is difficult to attain the accuracy spatial distribution of meteorological data by observation station. Remote sensing can acquire surface parameters with high spatial resolution in any region, which can compensate for the loss caused by the absence of observation station.

In order to enhance market competitiveness of tobacco, it should be improve the quality of tobacco leaves. The current tobacco production is mainly based on family unit, and each family adopted his planting mode, which is difficult to attain this target. The scale planting is an effective way to deal with this problem. It can be conducive to mechanized production, improve farm management level, and save labor and reduce production costs, which will help to achieve standardization of tobacco production. However, the current evaluation method which only evaluates the suitable level of each location is powerless to do these. In this study, through integrating suitability evaluation and arable land, the scale analysis was developed by spatial analysis of GIS.

After analyzing the current tobacco fields, it was found that more than 20% of tobacco was planted in moderate and not suitable area, therefore, the spatial distribution of tobacco planting needed to be adjusted. Based on scale analysis for arable land, we found that the tobacco fields which were suitable for tobacco planting and had a certain scale mainly distributed the middle and southern parts of study area, accounting for 75.3% of arable land.

Fig. 1 .

 1 Fig. 1. The location of study area and the spatial distribution of soil sampling.

Fig. 2 .

 2 Fig. 2. The evaluation indicators of elevation, solar radiation, temperature and TVDI.

Fig. 3 .

 3 Fig. 3. The flow chart and processes method of this study.

Fig. 4 .

 4 Fig. 4. Hierarchical structure for the indicator weight assignments.

Fig. 5 .

 5 Fig. 5. The spatial distribution of soil nutrients.

Table 1 .

 1 Standard scoring functions and parameters for quantitative tobacco planting suitability evaluation indicators.

		FT	L	L1	U1	U	MF
	Temperature(℃)	S-type	17	--	--	19

Table 2 .

 2 The weight of evaluation indicator for tobacco planting suitability

	C	B1 0.49	B2 0.20	B3 0.31	Weights
	Temperature C1	0.54	0	0	0.26
	TVDI C2	0.16	0	0	0.08
	Solar radiation C3	0.30	0	0	0.14
	Elevation C4	0	0.75	0	0.15
	Slope C5	0	0.25	0	0.05
	pH C6	0	0	0.44	0.14
	Organic matter C7	0	0	0.29	0.09
	Available N C8	0	0	0.13	0.04
	Available P C9	0	0	0.05	0.02
	Available K C10	0	0	0.09	0.03

Table 3 .

 3 Descriptive statistics of soil nutrients in the study area.

		Min	Max	Mean	Std	CV	Skewness
	pH	4.9	8.1	6.3	0.72	0.11	0.19
	OM(g/kg)	6.0	62.2	24.7	10.5	0.43	0.84
	AN(mg/kg)	11.4	212.2	81.4	39.1	0.48	0.64
	AP(mg/kg)	2.3	51.8	23.3	11.7	0.5	0.45
	AK(mg/kg)	58.4	490.9	198.6	102.9	0.52	1.05

Table 4 .

 4 Semivariogram models and their parameters for soil nutrients.

		Model	C 0	C	C 0 /(C+C 0 )	Range (m)
	pH	Exponential	0.18	0.23	0.44	1820
	OM	Gaussian	29.1	29.57	0.5	1720
	AN	Exponential	721	624	0.54	2850
	AP	Exponential	55	86	0.39	2474
	log(AK)	Exponential	0.12	0.11	0.52	2970

Table 5 .

 5 Summary of cross validation of ordinary kriging interpolation for soil nutrients

		pH	OM	AN	AP	AK
	ME	0.0	-0.03	0.01	-0.02	0.05
	MAE	0.13	3.61	3.63	3.13	5.3
	RMSE	0.45	4.77	4.79	4.23	7.5
	r	0.77	0.68	0.72	0.64	0.55

Acknowledgments: This research was financially supported by Natural Science Foundation of Hunan Province (No. 11JJ6029) and the Fundamental Research Funds for the Central Universities (No.2011QNZT006).


Yujian Yang

Guangming Liu

Xueqin Tong

Zhicheng Wang

Study on Spatial Model and Service Radius of Rural Areas and Agriculture Information Level in Yellow-river Delta

Keywords: Rural areas and agriculture information level, Service Radius, Measurement indices, Radial basis function, Yellow-river Delta

Based on the evaluation methods and systems of information measurement level, and according to the principles of agriculture information subject, the study constructed 13 indices system for the measurement of the rural areas and agriculture information level in Yellow-river Delta in 2007. Spatial autocorrelation model of rural areas and agriculture information of 19 country units showed that the comprehensive information level of Hanting, Shouguang, Guangrao, Bincheng, Huimin, Wudi and Yangxin country unit is very significant, has the obvious spatial agglomeration and homogeneity characteristics, but information level agglomeration of Kenli country and Zouping city has the significant heterogeneity, and information level agglomeration characteristics of other 10 country units is not significant. The radius surface of the complicated information level from radial basis function model indicated that rural areas and agriculture information service has a certain service radius, the distance of service radius in theory is 30Km, the gradient and hierarchy is obvious. According to it, the comprehensive service node should be established in Bincheng district and the secondary service node should be set up in Wudi country for improving the service efficiency. Combining with GIS grid technology, the profile line results of rural areas and agriculture information service from centroid coordinates based on 19 country units illustrated the fluctuation characteristics of the comprehensive information level. For yellow-river Delta, the high-efficiency ecological zone construction should consider the gradients of information service, continuity of information service and spatial agglomeration characteristics of 19 country units. Correspondingly, the regional policy which reflected the regional difference and association characteristics was carried out to achieve the leap development.

Introduction

Rural areas and agriculture Information is not only the inevitable trend of the modern agricultural development, but also an important component of the national economy information (Zang Chunrong et al., 2004). Rural areas and agriculture information assessment is to evaluate the level and effect of rural information construction and development level by adopting mathematical statistics(such as AHP method, fuzzy theory study), operation research principle and specific indicator system (Liu Shihong et al., 2007; Du Jing et al., 2010). Evaluating rural information level comprehensively, measuring and grasping the information development level accurately will help to find problems and shortcomings in the process of rural information. Rural information assessment is a huge system engineering that synthesizes technological and institutional innovations. Its implementation involves all aspects of rural business and information technology (Qin Xiangyang et al., 2008) [START_REF] Chunrong | Present situation analysis of China rural informatization development[END_REF][START_REF] Shihong | Study on the indicator system for measuring the rural area information level in China[END_REF][START_REF] Fangquan | Choice of development model of low-cost rural informationization in China[END_REF][START_REF] Jing | Research on assessment method for rural informatization level based on AHP[END_REF][START_REF] Lina | Construction of index system for IT application in agriculture[END_REF][START_REF] Xiangyang | Study on assessment index system for rural informatization in Beijing[END_REF][START_REF] Guoliang | A study on contents, obstacles and countermeasures of rural informatization in China[END_REF] . Rural areas and agriculture information constructions reflected the science and technology progress and the main promotion power of agriculture economy(Chen Xiwen, 2010), the present study showed that the correlation domains involved in the evaluation indices system of rural areas and agriculture information, but the measurement evaluation of it should be strengthened on the regional scale to intensify the agriculture resources and high-efficiency utilization of rural areas and agriculture information. The regional measurement of rural areas and agriculture information expanded the open degree and propelled the regional economy development, the service scope and service radius study of information level is rarely on the regional scale from the correlation documents [8] .

There is crucial to select the service node of rural areas and agriculture information level, whether in theory, or in practice for the comprehensive service system of information level. The study selected the regional economy body-Yellow river delta, as demonstration site, and explored the regional effects of rural areas and agriculture information level. Furthermore, the gradient and hierarchy of rural areas and agriculture information service was developed in the study, with the support of spatial analysis technology, the important service node of rural area and agriculture information level where the suitable location is selected was analyzed to improve the service efficiency in Yellow river delta. And other emphasis study is the fluctuation characteristics of the comprehensive information level of 19 country units in the paper.

2

Material and Methodology

Study Area and Indices System Establishment

Yellow river delta was formed on the basis of the yellow river alluvial plain and the adjacent marine regions in the north parts of Shandong province, extension the round regions, including Dongying city, Binzhou city, Hanting, Shouguang city, Changyi city in Weifang city, Leling and Qingyun in Dezhou city, Gaoqing in Zibo city and Laizhou in Yantai city. The total area is 26500Km 2 , the 1/6 of the total province. Yellow river Delta is the high-efficiency ecological zone, which has the important strategic significance, it located in the north emphasis of the whole strategy in Shandong province and is the important part of -blue economic zone of peninsula‖, the comprehensive development of rural areas and agriculture information level in Shandong province is crucial part of the national demonstration province of rural areas and agriculture information on 11 th and 12 th five year development planning of China.

Indices system establishment of rural areas and agriculture information referred to the new countryside planning of socialism construction from development and reform commission of Shandong province "developed production, affluent life, civilized rural atmosphere, clean and tidy village and democratic administration." Combing with well-off indices from Wu Dianting (2006), and considering the measurement system rule, study data were effectively collected and the selected 13 indices was applied in the study [START_REF] Dianting | Regional Analysis and Planning Turtorials[END_REF] .The 6 indices reflected material life (Enger coefficient, per house area of household, tap water countryside, household car, hospital numbers, urbanization). The 3 indices reflected spiritual life (telephone communication countryside, cable television countryside, patents per year). The income and distribution situation, included 2 indices(per capita net income and per capita GDP. Population quality considered 2 indices (students in middle school and per capita expenditure of education, science-technology and culture). In circumstances, Data acquisition, elimination dimension, the determination of indices weight, model establishment, the comprehensive indices computerization and comparison analysis, a series of steps were carried out to deal with the correlation data. As mentioned, measurement indices system was used to explore the autocorrelation characteristics and service radius of rural areas and agriculture information level [START_REF]Statistical Bureau of Shandong Province, China county (city) Socio-economic Statistical Yearbook[END_REF]11] .

Moran's I Coefficient and Radial Basis Function

Spatial autocorrelation can be defined as the coincidence of value similarity with site similarity. As the name suggests, autocorrelation is the correlation of a variable with itself, the autocorrelation coefficient (Moran's I) was used as description about the spatial dependency and was selected to describe the correlation degree of the location attribution value. The Moran's I coefficient is given by:

     2 ) ( ) ( x x W x x n I i i (1)
Where i

x is the data value at location i , h is the distance between locations i and j , ij w takes 1 if the pair ( , ) ij pertains to distance class h (the one for which the coefficient is computed), otherwise 0, W is the sum of ij w .

Where:

] 1 , 1 [  I , 0
I  stands for the independent variables, 0 I  stands for the positive correlation, 0 I  stands for the negative correlation [START_REF] Yingxia | Analyzing regional economic disparities based on ESDA[END_REF][START_REF] Yuming | A spatial econometric analysis of industrial total factor productivity in China's provincial regions[J][END_REF][START_REF] Anselin | Advances in spatial econometrics : methodology , tools and appl ications[END_REF] . Moran's I coefficient reflecting spatial dependency embodied the similarity of nearby observations. If high values of an attribute tend to cluster together in some parts of country unit and low values tend to cluster together in other parts, the attribute is said to exhibit positive spatial autocorrelation. Conversely, if high values tend to be found in close proximity to low values and vice versa, the attribute is said to exhibit negative spatial autocorrelation [START_REF] Fotheringham | Geographically weighted regression the analysis of spatially varying relationships[END_REF] .

Radial basis function (RBF) enables you to create a surface that captures global trends and picks up the local variation, which helps in cases where fitting a plane to the sample values will not accurately represent the surface. To create the surface, suppose you have the ability to bend and stretch the predicted surface so that it passes through all of the measured values. The study uses a set of n basis functions, one for each data location. The predictor is a linear combination of the basis functions.

1 0 1 0 ) ( ) ( ˆ      n i n i i s s s Z    (2) Where ) (r  is a radial basis function, 0 s s r i   is Euclidean distance
between the prediction location 0 s and each data location i s and

  1 , 3 , 2 , 1 :   n i i   are weights to be estimated, 1  n
 is a bias parameter [START_REF] Haining | Spatial data analysis: theory and practice[END_REF] .

RBF methods are a series of exact interpolation techniques, that is, the surface must go through each measured sample value. RBF are conceptually similar to fitting a rubber membrane through the measured sample values while minimizing the total curvature of the surface. The selected basis function determines how the rubber membrane will fit between the values. RBF can predict values above the maximum and below the minimum measured values as in the cross section below. The optimal parameter is determined using cross-validation in a similar manner. The RBF are also used for calculating smooth surfaces from data points. The functions produce good results for gently varying surfaces [START_REF]Using ArcGIS Geostatistical Analyst[END_REF] .

3

Results and Analysis

Moran's I Coefficient Analysis

The heterogeneity and dependence of rural areas and agriculture information level was analyzed by Moran`s I computerization, Moran`s I value is 0.2424, the positive spatial autocorrelation, Monte-Carlo tests showed the significant correlation of information level in confidence interval of 99%, the rural areas and agriculture information measurement on basis of 19 country units has the strong agglomeration characteristics in Yellow river delta in 2007. As illustrated in Fig. 1. Hanting, Shouguang and Guangrao(High-High), which displays the country units with a high information level (above the average) surrounded by country units with high information level (above the average) Bincheng, Huimin, Wudi and Yangxin( Low-Low), which presented the country units with low information level surrounded by country units with low information level. In the study, information level of the 7 country units presented the spatial clustering and homogeneity, information level of Kenli (Low-High) country showed the country units with low value surrounded by country units with high values, the evident difference existed in them. Zouping (High-Low) shows the country units with high value of information indices surrounded by country units with low value of information indices, the two country units presented the heterogeneity. The other information indices of 10 country units has not obvious significance.

The regional spatial pattern of rural areas and agricultural information undertook the historical consistence, the better economic foundation, relative location advantage and the regional development policy, these factors have a large effect on the spatial pattern of rural areas and agricultural information level. Combining with Moran`s I computerization results, showed that Hanting, Shouguang and Guangrao has the high level of information level, promoting the information coordination development of the surrounding country units, such as Zouping city, Kenli country. For the low information level of Bincheng district, Huimin country, Wudi country and Yangxin country, should mainly depend on the sparking sites of rural areas and agricultural information to promote service efficiency. Especially, the policy power has a large effect on shortening the difference.

Service Radius Surface

Clustering characteristics represents the spatial effect of adjacent regions, clustering characteristics of rural areas and agriculture information level provide the foundation of information service radius, that is to say, high information level region tend to be clustered close to the high information level region, the low information level region tend to be clustered close to the low information level region. The gradients and continuity of rural areas and agriculture information level need explore the sound analysis by spatial model. In the study, the centroid coordinates extracted from 19 country units on basis of spatial analysis were regarded as the data points of rural areas and agriculture information level. Owing to the gently varying of information level, the precision of interpolation surface was improved with the support of RBF, as showed in Fig. 2.

On the whole, rural areas and agriculture information level in Yellow-river Delta has spatial variability, there is obvious gradients of information level from the weight value(-7.597) to the weight value(13.657) depending on RBF analysis of the comprehensive level in 19 country units. As shown in Figure 2, Bincheng district, the -sink‖ area of information level, the -peak‖ area around Dongying, the service radius of rural areas and agriculture information level by GIS calculations, about 30Km. In order to upgrade the whole pattern of information service nodes and improve the service efficiency of information level, combining with the service radius, The comprehensive service node should be established in Bincheng district and the secondary service node should be set up in Wudi country, the kind of the spatial pattern had not only important decision significance, but also provided the foundation for constructing the better hierarchy service system of rural areas and agriculture information level in Yellow-river delta.

Fig. 2. The Service radius surface of rural areas and agriculture information level

Based on GIS grid technology [START_REF] Yang | The spatial continuity study of NDVI based on Kriging and BPNN algorithm[END_REF] , as the foundation of centroid coordinates of 19 country units, according to the following sequence, Laizhou, Changyi, Hanting, Shouguang, Guangrao, Dongying, Kenli, Hekou, Lijin, Zhanhua, Wudi, Qingyun, Leling, Yangxin, Bincheng, Gaoqing, Boxing, Zouping and Hui min, the profile line of rural areas and agriculture information level was drawn, the results referred to Fig. 3. Results showed that there is the fluctuation characteristics of rural areas and agriculture information level in Yellow river delta. The "peak" area is in Dongying and Shouguang, etc., the "sink" area is Bincheng district, the quantity description and sound fluctuation characteristics of information level is as the following contents, the weight value of the comprehensive information level referred to the brackets values, and represented the service level in different country units. Laizhou(0.707), Changyi (1.354)

Conclusions and Discussion

The paper constructed 13 indices measured the rural areas and agriculture information level in Yellow-river Delta in 2007. Moran's I Coefficient analysis of rural areas and agriculture information level of 19 country units showed that the comprehensive information of 7 country units is significant, has the obvious spatial agglomeration and homogeneity characteristics, information level of Kenli and Zouping has the significant heterogeneity. The continued spatial surface from radial basis function method indicated that rural areas and agriculture information service in theory is 30Km, the gradient and hierarchy is obvious. The comprehensive service node should be established in Bincheng district and promote the development of information level, the secondary service node should be set up in Wudi country for improving the service efficiency according to the study. Combining with GIS grid technology, the profile line results of rural areas and agriculture information service of centroid coordinates of 19 country units illustrated the fluctuation characteristics of the comprehensive information level.

The service of rural and agricultural information is the inevitable trend of the world modern agricultural development, we should emphasize the comprehensive platform construction for rural areas and agriculture information services, support platform making full use of existing low-cost, mature means of communication and platform, multiple platform integrated organic body. In essence, the service construction of information platform of rural areas and agriculture information in Yellow-river delta is the branch platform and sub-nodes of the rural areas and agriculture information platform in Shandong province, so we should perfect the sound measurement system and the evaluation system of rural areas and agriculture information level, and especially measurement system on the regional scale, correspondingly, improve and strengthen the quality of rural areas and agriculture information service in demonstration site, promote the whole upgrading of the information level in Shandong province.

Fig. 1 .

 1 Fig. 1. Scatterplot map of measurement indices of rural areas and agriculture information level.

  The profile line of rural areas and agriculture information level in 19 country units

		Dongying(13.642),Kenli(-0.576),Hekou(1.678),Lijin(-2.249),Zhanhua(-2.879),Wudi(-
		2.358),Qingyun(-3.174),Leling(0.553),Yangxin(-2.806),Bincheng(-7.593),Gaoqing(-
		3.981),Boxing(-1.840),Zouping(1.791)and Hui min(-1.263).The profile line also
		indicated the information level of other regions excluded by 19 country units and
		there is the continuous distributions of information level, which is consistent with the
		service surface results of rural areas and agriculture information level from RBF
		surface results.
			Profile line of informatation index based on 19 country units
		14
		12
		10
	value	8 6 4
	Index	2 0 -2
		-4
		-6
		0.708 1.254 1.416 2.06 2.75 4.24 3.28 4.453 9.66 0.321 -1.96 1.387 -1.251 -2.871 -2.36 -2.915 -1.1 -1.28 -2.79 -4.7 -5.397 -1.49 -0.526 -1.37 -3.035 -8
				Index value
		Iinformation level
		Fig.3.
		,	Hanting(2.456),	Shouguang(4.592),	Guangrao(1.947)


Hanming He

Lei Yang

Liming Fan

Lihua Zhao

Han Wu

Jing Yang

Chengyun Li
email: licheng_yun@163.com

The Effect of Intercropping of Maize and Soybean on Microclimate

Keywords: :Intercropping, Temperature, Relative humidity, Light intensity, Biological characters

Intercropping induces the diseases decreasing, and yield increasing, may partly due to the improvement of microclimate in fields. In order to understand the mechanism and efficiency of resource utilization in intercropping of maize (Zea mays) and soybean (Glycine max), a field experiment was conducted as factorial on the bases of randomized complete block design of three patterns with three replications. Three cropping patterns were maize monocropping (A), 2 rows maize and 2 rows soybean intercropping (C) and 2 rows maize and 4 rows soybean intercropping (D). Our studies showed that compared with monocropping, the temperature in intercropping was a little higher in the daytime, but in the nighttime, the contrary results were observed; the relative humidity in intercropping was lower in the daytime, but in the nighttime, the contrary results were observed; the light intensity in intercropping was markedly higher than that in monocropping. The yield components of maize in intercropping, including thousand kernel weight, yield per plant and leaf area were increased than that in monocropping. These results imply that microclimate variation of intercropping probably play important role to maize yield increasing.

Introduction

Compared with the monoculture, there may be more effectively utilize the temporal-spatial remainder of intercropping crops growth and development to bring into play the production potential of limited agricultural resources including radiation, fertilizer, water, gas and heat [START_REF] Youyong | Genetic diversity and disease control in rice [J][END_REF][START_REF] Chengyun | Crop Diversity for Yield Increase[END_REF][START_REF] Youyong | Genetic Diversity for Crops Diseases Sustainable Management[END_REF] . Therefore intercropping plays an important role in agricultural productions.

The microclimate including temperature, relative humidity (RH) and light intensity in farmland is an important factor in the growth and production of crops. Previous studies showed that the relative humidity, which played vital roles in disease injuries of crops, was found to present downtrend and reducing the number of hours per day with relative humidity ≥92% in intercropping [START_REF] Youyong | Genetic Diversity for Crops Diseases Sustainable Management[END_REF][START_REF] Gómez-Rodrí Gueza | Allelopathy and microclimatic modification of intercropping with marigold on tomato early blight disease development[END_REF] . And the intercropping can increase the amount of light intercepted of crops in unit planting area so that improve the crop dry matter production, yield and radiation use efficiency [START_REF] Monteith | Climate and the efficiency of crop production in Britain[END_REF][START_REF] Sivakumar | Crop productivity in relation to interception of photosynthetically active radiation[END_REF][START_REF] Jahansooz | Radiation-and water-use associated with growth and yields of wheat and chickpea in sole and mixed crops [J][END_REF][START_REF] Tsubo | Comparisons of radiation use efficiency of mono-/intercropping with different row orientations[END_REF][START_REF] Zhang | Light interception and utilization in relay intercrops of wheat and cotton[END_REF][START_REF] Tsubo | A model of radiation interception and use by a maize-bean intercrop canopy[END_REF] . So we explore the microclimate change regulation in multi-culture patterns of maize and soybean and compared with monocropping so to discover the mechanism of intercropping.

Materials and methods

Field experiments were conducted at the farm of Yunnan Agricultural University, Yunnan province, China, 25°01′N,103°E during 2009/05/06 to 2009/10/22. the crops used in the experiment were maize (Yunrui 88) and soybean (Nandou 12), and were sown by north-south rows at the same time. The experiments was made up of three cropping modes, one of which was maize monocropping (A, row distance 40cm), two of which were maize intercropping, viz., 2 maize rows (row distance 50cm) and 2 soybean rows (row distance 50cm) (2:2)-(C) which distance of maize to nearest soybean row is 50cm, and 2 maize rows (row distance 35cm) and 4 soybean rows (row distance 30cm) (2:4)-(D) which distance of maize to nearest soybean row is 40cm. Maize plant distance is 20cm and soybean plant distance is 30cm in all cropping patterns. Experiments repeated 3 times which using two-factor completely block design. Every unit area was 4.0cm ×5.0cm. Full irrigation and fertilizer were applied for every cropping system. HOBO U12-012 data loggers were put beside maize plants and soybean plants to measure temperature ranged from -20℃ to 70℃, RH ranged from 5% to 95%, light intensity ranged from 1 to 3000 footcandles (lumens/ft 2 ). The light intensity beyond 3000 footcandles was recorded as the max value. But the light distributing difference between mono-cropping and intercropping focus mostly in the upper, middle and botten part, and the light intensity is usually in the measurement range. The Li-6400 portable photosynthesis was used to measure the photosynthetic rate of maize leaves.

The temperature, RH and light intensity on the position between maize and soybean, above ground 30cm (below the head of soybean canopy) and 70cm (above the head of soybean canopy) of field were measured at maize heading stage in our experiment. The HOBO loggers recorded the data one time every 30 minutes and the average of that during a period was obtained as the daily changes of temperature, RH and light intensity.

Results and discussion

The difference of temperature and relative humidity beside maize plant at heading stage in the monocropping and intercropping

Fig. 1-(a) and (b) indicated that the similar trend of the temperature beside maize plants above ground 30cm and 70cm between monocropping and intercropping was observed, viz., temperature in the daytime was higher than that in the nighttime.

Wherever above ground 30cm or 70cm, in the daytime the temperature in intercropping was a little higher than that in monocropping, and the temperature in intercropping D was the most highest, followed by that in intercropping C and that in monocropping A. But in the night time, the contrary results were observed. The temperature in the nighttime in intercropping was a little lower than that in monocropping, and the temperature in intercropping D was the most lowest, followed by that in intercropping C and that in monocropping A. Fig. 1-(c) and (d) also illustrated that similar daily change regulation of RH beside maize plants above ground 30cm and 70cm between monocropping and intercropping was also observed as the temperature, viz., the RH in daytime was lower than that in nighttime. Both above ground 30cm and 70cm, the RH in the daytime in monocropping A was the most highest, followed by that in intercropping D and that in intercropping C. But in the nighttime, the contrary results were observed. The RH in the nighttime above ground 30cm in intercropping D was the highest, followed by that in intercropping C and that in monocropping A. Above ground 70cm, the highest RH was found in intercropping D, and the lower one was found in monocropping A and in intercropping C. The results indicated that the microclimate in field such as temperature and RH in the intercropping were improved compared with that in monocropping. In other words, the maximum was found in the noon, while the minim was observed in the morning and evening in whatever cropping systems, which were consistent to the rules in meteorology. Moreover, light intensity in intercropping was significant higher than that in monocropping, especially with the increase of radiation angle. that in monocropping. Further more, the light intensity was over 400 footcandles in intercropping from 10:00 am to 4:00 pm. Therefore, no matter in the time length and the intensity, effective radiation among the maize canopy above the ground 30cm in intercropping was markedly higher than that in monocropping during July 4 th to July 19 th . We conducted a Pearson correlation between daily average light intensity and the photosynthetic rate of 7 th leaves (nearly on the position above ground 30cm) during July 20 th to July 28 th . The results showed that strong correlation (R 2 =0.92, P<0.001) can be observed in Fig. 2-(c). Table 1 indicated that maize's yield characters in intercropping, including thousand kernel weight, yield per plant and area of ear leaves are all greater than that in monocropping, in which the thousand kernel weight showed D>C>A, yield per plant showed C>D>A, the area of ear leaves showed C>D>A. Accordingly, from the trial results it was seem that the biological characters of maize in intercropping were better than that in monocropping.

Conclusions

Intercopping leads to variation of microclimate, especially for light intensity, RH and temperature. Maize and soybean are staple crops in the world, intercropping of the crops have long history in Asian countries, mainly for increasing the yield and nourishing the soil. However, mechanisms and efficiency of intercropping is unclear. It will be the limitation for improving intercropping system. In this study, microclimate of maize field including temperature, RH and light intensity in monocropping and intercropping were continously investigated by using Multi-channel Data Logger (Hobo H8) analyzed and compared. The results show that the microclimate was improved siginificantly.

Firstly, wherever above the ground 30cm or 70cm, in the daytime the temperature in intercropping was a little higher than that in monocropping, but in the night time, the contrary results were observed. Both above the ground 30cm and 70cm, the RH in the daytime in monocropping was higher then that in intercropping, but in the nighttime, the contrary results were observed.

Secondly, from the daily change, no matter in the time length and the intensity, effective radiation among maize plants above the ground 30cm and 70cm in intercropping was markedly higher than that in monocropping. Moreover, a remarkably linear correlation between daily average light intensity and the photosynthetic rate of leaves was observed.

Finally, the yield components of maize in intercropping, including thousand kernel weight, yield per plant and leaf area were increased than that in monocropping.

In summary, our results showed microclimate variation, including increasing the radiation duration and light intensity at different height position in intercropping field significantly, and remarkable correlation between light intensity and photosynthetic rate implied the variation of light intensity play important role for improving the yield component of maize. However, intercropping is a very complex system, biological variation of maize plants play roles for increasing the yield as well. To determine the better combination and more efficiency of resource utilization in intercropping, it is need to understand the biological, physical, chemical and microclimate effectors and their interaction.

Fig. 1 .Fig. 2 -

 12 Fig.1. Daily change of temperature and RH beside maize plants above ground 30cm and 70cm in monocropping and intercropping at heading stage

Fig. 2 -

 2 Fig. 2-(a) indicated that above ground 30cm in monocropping only from 11:30 am to 13:30 pm, the light intensity was above the compensation points. But in intercropping (C and D), the light intensity from 8:30 am to 15:30 pm was always above the light compensation points. It was implied that the time length of light intensity above the compensation points was significantly longer (nearly 5 hours) than

Fig. 2 -Fig. 2 .

 22 Fig.2-(b) indicated that above ground 70cm (beyond the head of soybean canopy)in monocropping, the light intensity was significant lower than the light compensation points, viz., the light intensity was below 100 footcandles. However, in intercropping C and D, the light intensity from 9 am to 5 pm (nearly 8 hours) was above the light compensation points, and from 9 am to 4 pm (7 hours) the light intensity was more than 200 footcandles. Maybe it becaused the canopy above the ground 70 cm in intercropping was not shadowed and can fully accept radiation. In Fig.2-(d) the linear correlation (R 2 =0.95, P<0.001) between the light intensity and the photosynthetic rate in ear leaves (nearly on the position above ground 70cm) was also observed in monocropping and intercropping.

Table 1 .

 1 Biological characters of maize in monocropping and intercropping

	characters Pattern	Biological	Thousand kernel weight (g)	Yield per plant (g)	Leaf area(cm 2 )
	Monocropping	323.8	120.8	626.0
	Intercropping 2:2	339.2	167.2	877.3
	Intercropping 2:4	344.9	160.7	666.6

Ackonwledgemants

The authors would like to thank Project supported by the National Basic Research Program (2011CB100400).


Zhanyu Liu
email: liuzhanyu@zju.edu.cn

Wenjiang Huang

Guofu Mao

Cunjun Li

Xingang Xu

Xiaodong Ding

Jingjing Shi

Bin Zhou

Estimating Foliar Pigment Concentration of Rice Crop Using Integrated Hyperspectral Index

Keywords: Hyperspectral reflectance, Nitrogen fertilization rate, Pigment concentration, Rice

Photosynthetic pigment concentration has strong relationship with nitrogen (N) concentration which is an essential element of plant growth and plays an important role in estimating the net primary productivity (NPP) in terrestrial ecosystem research and precision agriculture (PA). In this study, hyperspectral reflectance and pigment concentration of the upper three leaves of rice crop (Oryza sativa L.) with five N fertilization rates were measured in the laboratory. The results showed that there was no significant difference between the leaf hyperspectral reflectance and pigment concentrations in the visible and near-infrared (NIR) spectral regions at each leaf position. But in the shortwave infrared (SWIR) region, the difference of each two of the three leaf positions was obviously significant at level 0.05. The integrated hyperspectral index MCARI/OSAVI [670,800] had been proved to be better linear related with leaf pigment concentrations at different leave position. The result demonstrated that MCARI/OSAVI [670,800] was a reliable and stable hyperspectral index for estimating pigment concentration at leaf level..

Introduction

Net primary productivity (NPP) is a function of an efficiency coefficient defining the carbon dioxide (CO 2 ) fixed in short-lived (foliage and fine roots) and long-lived (wood) tissue per unit area and time [START_REF] Ahl | Heterogeneity of light use efficiency in a northern Wisconsin forest: implications for modeling net primary production with remote sensing[END_REF][START_REF] Zeng | Modeling the interannual variation and response to climate change scenarios in gross and net primary productivity of Pinus elliottii forest in subtropical China[END_REF] , which plays a vital role in essential materials and suitable environments for human society [START_REF] Zhao | Improvements of the MODIS terrestrial gross and net primary production global data set[END_REF][START_REF] Jia | Estimating total net primary productivity of managed grasslands by a state-space modeling approach in a small catchment on the Loess Plateau, China[END_REF] . The precise estimation of NPP over a continental and global scale would facilitate an improved interpretation of dynamic physiological processes within terrestrial ecosystems research and precise agriculture (PA) management, and is also crucial for the research on the relationship between global climate change and carbon cycling processes [START_REF] Blackburn | Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves[END_REF][START_REF] Steele | A regression strategy for analyzing environmental data generated by spatio-temporal processes[END_REF] .

NPP has been shown to be related to the fraction of absorbed photosynthetically active radiation (f apar ) which is a function of the sum of the concentrations, and more precisely the molar extinctions, of chlorophyll a (Chl a hereafter), chlorophyll b (Chl b), and the carotenoids (Cars). In addition, leaf characteristics, especially nitrogen (N) content, and specific leaf area affect NPP directly by constraining photosynthesis and transpiration rates [START_REF] Fownes | Forest canopy chemistry from Blackhawk Island, Wisconsin[END_REF][START_REF] Gholz | Environmental limits on aboveground net primary production, leaf area, and biomass in vegetation zones of the Pacific Northwest[END_REF][START_REF] Ingestad | Nitrogen stress in birch seedlings. 2. N, K, P, Ca and Mg nutrition[END_REF] . Moreover, N is an essential element for plant growth and is frequently the major limit nutrient in most agricultural soils. Excessive N fertilizer may move into surface water and groundwater and accelerate eutrophication of lakes and streams [START_REF] Zhou | Detection of nitrogen-overfertilized rice plants with leaf positional difference in hyperspectral vegetation index[END_REF] . Farmers must balance the competing goals of supplying enough N to their crops in PA management while minimizing the loss of N to the environment, which represents both a threat to water quality and an economic loss. Since N is the component element of chlorophyll molecules, there is a close link between leaf chlorophyll content and leaf N content [START_REF] Yoder | Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales[END_REF] . Thus remote sensing techniques have the potential to evaluate the chlorophyll variability over large fields quickly [START_REF] Daughtry | Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[END_REF] .

Developments in hyperspectral remote sensing made it possible to estimate N and NPP variability through quantifying individual photosynthetic pigments within vegetation. Spectral reflectance measurements of corn (Zea mays L.) and wheat (Triticum aestivum L.) canopies have been used to detect characteristic of different N status of crop and provide reliable information for developing variable-rate fertilizer N application technique [START_REF] Blackmer | Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies[END_REF][START_REF] Walburg | Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies[END_REF][START_REF] Hinzman | Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat[END_REF] . The variety of N treatments resulted in the differences in leaf chlorophyll content, leaf area index (LAI), biomass, and foliage cover which contributed to the differences in spectral reflectance at the leaf and canopy scales. The relatively subtle differences in leaf and canopy reflectance associated with changes in leaf chlorophyll are often confounded with major changes in plant growth and development due to N treatments [START_REF] Daughtry | Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[END_REF] .

Vegetation indices (VIs) have been developed as an attempt to reduce spectral effects caused by external factors such as the atmosphere and the soil background [START_REF] Demarez | A modeling approach for studying forest chlorophyll content[END_REF] . Peñuelas et al. [START_REF] Peñuelas | Reflectance indices associated with physiological changes in nitrogen-and water-limited sun flower leaves[END_REF] have advocated a shift towards narrow-band VIs for estimating the absolute and relative concentrations of Chl a, Chl b, and Cars in plant leaves. Zarco-Tejada et al. [START_REF] Zarco-Tejada | Optical indices as bioindicators of forest condition from hyperspectral CASI data[END_REF] performed a study of VIs for chlorophyll estimation in open-canopy forest from leaf level to the canopy through SAIL and Kuusk canopy reflectance model and demonstrated that nominal canopy reflectance model parameters appear to be sufficient to allow accurate application of the optical index/bioindicator algorithm to airborne data. Research by Wu et al. [START_REF] Wu | Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation[END_REF] suggested that the integrated VIs, namely, TCARI/OSAVI [705,750] and MCARI/OSAVI [705,750] , were most appropriate for chlorophyll estimation with high correlation coefficients R 2 of 0.881 and 0.941, respectively, because more disturbances such as shadow, soil reflectance and nonphotosynthetic materials were taken into account.

Therefore, this study aims at examining the performance of integrated hyperspectra indices MCARI/OSAVI [670,800] developed by Rondeaux et al. [START_REF] Rondeaux | Optimization of soil-adjusted vegetation indices[END_REF] and Daughtry et al. [START_REF] Daughtry | Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[END_REF] in photosynthetic pigment concentration estimation within the rice leaf. Meanwhile, the feedback response of leaf spectral characteristics to leaf pigment concentration due to N treatments will be investigated.

Materials and Methods

Field Experiment

The field experiment was conducted on sandy loam soil from early June to late October in 2008 at the research farm of Xiaoshan Agricultural Science Research Institute (30°20′N latitude, 120°31′E longitude and altitude approximately 6m), Hangzhou, Zhejiang Province, China. The original soil had 13.1g/kg organic C, 5.6mg/kg bicarbonate extractable P, 35.2mg/kg exchangeable K, 1.26g/kg total N and Ph 7.5 (soil: water = 1:1 (w/v)). Three rice (Oryza sativa L.) cultivars, namely, Yongyou 8, Zhongzheyou 1, and Zhejiang 22, were employed in this study.

A random block design was employed with the three rice cultivars and five N fertilization rates (0, 75, 180, 285 and 390 kg/ha). Forty percent of the N fertilizer (i.e. urea) was fertilized during the pre-transplanting period, 30% at the tillering stage, and 30% at the initial heading stage. Each treatment was replicated three times. The rice plants were transplanted on July 5 th , 2008 and harvested on October 31 st , 2008.

Hyperspectral Reflectance Data Acquisition

One whole rice plant from each plot was collected, placed in a plastic bucket with water, and transported to the laboratory for spectral measurement on August 21 st , September 27 th , and October 10 th , 2008. The first, second and third uppermost leaves were cut from the main stem of rice plant and referred to as L1, L2 and L3, respectively. The hyperspectral reflectance of L1, L2 and L3 was measured with a portable spectrophotometer (Analytical Spectral Devices, Inc., Colorado, CO, USA) in the wavelength range of 350-2500 nm. The spectral resolution of the instrument is 3 nm for the region of 350-1000 nm and 10 nm for the region of 1000-2500 nm.

Rice leaves were positioned on a dark background so that the fiber optic sensor with a 25° instantaneous field of view (IFOV) vertically pointed to centre of the leaf surface with about 3.5 cm height, equivalent to 1.9 cm 2 observed area. An incidence angle of 45°was maintained at a standard distance of 50 cm throughout the study in a closed chamber with a halogen lamp (50 Watt). A sampling spectrum was consisted of ten readings for each leaf. The average spectra derived from the sampling spectra represented each leaf using ViewSpec Pro (version 5.6.10).

Foliar Pigment Concentration Acquisition

After the hyperspectral measurements, a leaf disk with a weight of 0.1 g was cut from each leaf for pigment analysis. Each leaf disk was crushed, and dipped in 20 ml solution (acetone: ethanol: distilled water = 4.5:4.5:1) for 24 hours in the dark environment to extract pigment. The optical density (OD) of the extraction solution was measured at 440 nm, 645 nm, and 663 nm by spectrophotometer (Shimadzu UV 2550, Tokyo, Japan). Pigment consists of Chl a, Chl b, and Car in the study. The formula of pigment concentration (mg/g) was referenced the literature of Tang et al. [START_REF] Tang | Relations between red edge characteristics and agronomic parameters of crops[END_REF] .

Data Analysis

Data Preprocessing

The hyperspectral reflectance was smoothed with a five step moving average to suppress instrumental and environmental noise before these data were further analyzed [START_REF] Kobayashi | Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[END_REF] . Then, the raw hyperspectral reflectance less than 400 nm and more than 2400 nm were ineffective due to severe instrument and system noise [START_REF] Liu | Application of neural networks to discriminate fungal infection levels of rice panicle using reflectance measurements and principal components analysis[END_REF] .

Statistical Analysis

Analysis of variance (ANOVA) is useful for assessing what proportions of the variations in a dependent variable can be accounted for by one or more independent variables [START_REF] Rondeaux | Optimization of soil-adjusted vegetation indices[END_REF][START_REF] Daughtry | Effects of management practices on reflectance of spring wheat canopies[END_REF] . The hyperspectral reflectance (Table 1) and pigment concentration (Table 2) were expressed as mean and standard deviation (SD) for the remote sensing and biophysical data and were analyzed by ANOVA, followed by Duncan's multiplerange test when appropriate. Differences between groups were considered significant when p<0.05. Pearson's linear regression coupled to ANOVA was used to verify the effects of pigment concentration on hyperspectral reflectance and integrated hyperspectral VIs. Values of the determination coefficients were obtained by the linear regression analysis (Fig. 3). ANOVA was then performed with SPSS software (Statistical Package for the Social Science, version 16.0.0).

Integrated Hyperspectral index

The chlorophyll absorption ration index (CARI) developed by Kim [START_REF] Kim | The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar)[END_REF] could minimize the effects of nonphotosynthetically materials on spectral estimates of absorbed photosynthetically active radiation (PAR). Daughtry et al. [START_REF] Daughtry | Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[END_REF] simplified the calculation equation of CARI to obtain the modified CARI (MCARI). MCARI is the depth of chlorophyll absorption at 670 nm relative to the reflectance at 550 nm and 700 nm and is defined as the following equation:

700 [670,700] 700 550 700 670 670 R MCARI =[(R -R )-0.2(R -R )] R (1)
Rondeaux et al. [START_REF] Rondeaux | Optimization of soil-adjusted vegetation indices[END_REF] concluded that the optimized soil-adjusted vegetation index (OSAVI) could reduce the sensitivity of object reflectance to the underlying soil. OSAVI is defined as follows:

800 670 [670,800] 800 670 (1 0.16)(R -R ) OSAVI (R +R 0.16)    (2)
Therefore, the integrated form of MCAI and OSAVI could be defined as

[(R -R )-0.2(R -R )](R /R ) MCARI [670,800] OSAVI (1 0.16)(R -R ) /(R +R 0.16)    (3)
Wu et al. [START_REF] Wu | Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation[END_REF] proved that MCARI/OSAVI is one of the most appropriated integrated hyperspectral indices for chlorophyll estimation because more disturbances such as shadow, soil reflectance and nonphotosynthetic materials are taken into account.

Results

Leaf Hyperspectral Reflectance

The mean (n = 45) hyperspectra reflectance of L1, L2, and L3 of rice crops collected on three sampling dates for three cultivators at five N fertilization rates were shown in Fig. 1. They showed very little detail information in the full wavelength range (400-2400 nm), especially in the visible region.

To get more specific information on the difference between different leaf positions in leaf hyperspectral reflectance due to changes of leaf pigment concentration, the hyperspectral reflectance as waveband means was calculated to simulate the Enhanced Thematic Mapper Plus (ETM+) of Landsat-7 (Table 1). ANOVA followed by Duncan's test with 0.05 of alpha was used to verify the effects of pigment concentration at different leaf positions on hyperspectral reflectance (Table 1 and2). Photosynthetic pigments control the hyperspectral reflectance and transmittance in the visible region. Because there was almost no significant difference among L1, L2 and L3 in concentration of Chl a, Chl b and Cars (Table 2), the hyperspectral reflectance had no corresponding significant difference in the visible blue-green (450-515nm), green (525-605nm), red (630-690nm), near-infrared (NIR, 775-900nm) spectral regions except the samples collected on September 27 th , 2008 as shown in Table 1.

In the shortwave infrared spectral region (SWIR 1 :1550-1750nm; SWIR 2 :2090-2350nm), the differences between L1 and L2, and between L2 and L3 were significant only for the latter two sampling dates. Comparing with L1 collected on September 27 th , 2008, the hyperspectral reflectance of L2 decreased 5.1% and 5.2% in SWIR 1 and SWIR 2 regions, respectively; and comparing with L2, the hyperspectral reflectance of L3 decreased 4.1% and 5.3% in SWIR 1 and SWIR 2 regions, respectively(Fig. 1 b and Table 1). Same phenomena occurred for the third sampling date (Fig. 1 c and Table 1). But the very reverse changes in hyperspectral reflectance appeared for the first sampling date (Fig. 1 a and Table 1).

Relationship Between Hyperspectral Reflectance and Leaf Pigment Concentration

The correlation between the hyperspectral reflectance and the leaf pigment concentrations was investigated to interpret the changes of spectral characteristics through the full wavelength range (400-2400 nm) (Fig. 2).

As shown in Fig. 2(a), the hyperspectral reflectance in the green light region had greatest negative correlation with leaf pigment concentrations. The correlation coefficients were 0.735, 0.660 and 0.708 for Chl a, Chl b and Cars, respectively; and the corresponding wavebands of the former pigment located at 560nm, the latter two pigments at 545nm. In the red light region, the highest and lowest coefficients of hyperspectral reflectance with leaf pigment concentrations appeared near 667nm and 694nm wavebands, respectively. The inflexion points of correlation coefficient in the NIR region came forth near 760nm. The highest and lowest coefficients in SWIR region rose near 1458nm and 1650nm, but the former waveband would in the vapor absorption region if the hyperspectral reflectance was measured at the canopy level from the airborne or spaceborne platforms. Although the growth stages were different (Fig. 2 a-d), the sensitive wavebands of hyperspectral reflectance to leaf pigment concentrations always occurred near 550nm, 670nm, 700nm and 1450-1460nm in the green, red, NIR and SWIR regions, respectively.

Regression Between Pigment Concentration and Integrated Hyperspectral Index

As analyzed above, the sensitive wavebands were consistent with the applied wavebands of integrated hyperspectral index (MCARI/OSAVI [670,800] ). MCARI/OSAVI [670,800] had better linear relationship with leaf pigment concentrations (Fig. 3). As shown in Fig. 3 (a) to (l), the correlation between MCARI/OSAVI [670,800] and leaf pigment concentrations were obviously significant (p<0.05) in any leaf positions and total samples. Strong correlation existed for all the data collected in the three sampling dates (Fig. 3 m-p). MCARI/OSAVI [670,800] were most appropriate for Chl a estimation with high determination coefficient (R 2 ) of 0.749 for L1 collected on August 21 st , 2008 (Fig. 3 a). It was worst appropriate for all the data collected in the three sampling dates without regard to leaf positions (Fig. 3 p). The determination coefficients of total samples for any sampling dates (Fig. 3 d,h, l and p) were always lower than those of each leaf position (Fig. 3 a-c, e-g, i-k andm-o).

Discussion and Conclusion

In the present study, the hyperspectral reflectance were found that there was no significant difference (n=45 in each group) in the visible blue (450-515nm), green (525-605nm), red (630-690nm), near-infrared (NIR, 775-900nm) spectral regions for difference leaf positions (Fig. 1, Talbe1) because the difference in concentrations of photosynthetic pigments Chl a, Chl b and Cars, which absorb the visible spectrum by electronic transitions among L1, L2 and L3 was almost not significant (Table 2). In SWIR regions, however, the difference was obviously significant (Duncan test at alpha = 0.05) by twos of all the three leaf positions (Table 1). The leaf hyperspectral reflectance in SWIR regions decresed gradationally from the top to below in the two out of three sampling dates (Fig. 1, Table 1). Such results were similar to others reports by Zhao et al. [START_REF] Zhao | Improvements of the MODIS terrestrial gross and net primary production global data set[END_REF] who concluded that the hyperspectral reflectance of maize would change in different spectral regions due to N treatments. The correlation analysis of hyperspectral reflectance indicated that the most sensitive wavebands mainly located at near 550nm, 670nm, 700nm, 1460nm, and 1650nm. These wavebands always presented the greatest correlation coefficient with significant difference (p<0.05) even very obviously significant difference (p<0.001). The result in this study was incompletely with the current research which suggested that the most sensitive wavebands distributed in the green (525-605nm) and yellow (605-650nm) regions, and the worst in SWIR region [START_REF] Zhao | Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply[END_REF] . The waveband at 1460nm was efficient to estimate the absolute and relative concentration of photosynthetic pigments and other biophysical or biochemical parameters at leaf level in the laboratory condition, but it's inefficient at the canopy level from the near ground, airborne and spaceborne platforms because of the presence of water vapor [START_REF] Daughtry | Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[END_REF][START_REF] Blackburn | Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches[END_REF] .

The integrated hyperspectral index MCARI/OSAVI [670,800] had the better linearity with leaf pigment concentrations(Fig. 3). Perhaps because the component wavebands were derived from the aforementioned sensitive wavebands (Equ. ( 3)). As shown in Fig. 3 (a) to (l), The regression analysis denoted that MCARI/OSAVI [670,800] had a great relationship with leaf pigment concentrations in any leaf positions and total samples. To take into account the leaf positions, MCARI/OSAVI [670,800] was a reliable hyperspectral index for estimating the pigment concentration. However, there are more external factors including canopy structure, LAI, plant cover density, soil types, soil moisture, sunlight illumination, cloudy and plant shadow, and so forth, which coexist in the field [START_REF] Liu | Application of neural networks to discriminate fungal infection levels of rice panicle using reflectance measurements and principal components analysis[END_REF] , and bring lots of noise in remote sensing application. Therefore, it will be necessary to make multiangular measurement to acquire more detailed information on plant structure than vertical measurement, and then the hyperspectral VIs will obtain high accuracy for estimating the concentration of photosynthetic pigments and other agricultural parameters.

Fig. 1

 1 Fig.1 Mean hyperspectral reflectance of rice crop at the first, second and third uppermost leaf (L1, L2 and L3) collected on August 21 st (a),September 27 th (b) and October 10 th (c), 2008.

Fig. 2

 2 Fig.2 Correlograms of hyperspectral reflectance to leaf pigment concentration collected on August 21 st (a),September 27 th (b) and October 10 th (c), 2008, and all the three sampling dates (d). The dashed line denoted obviously significant level at p<0.001.

Fig. 3

 3 Fig.3 Linear relationship between foliar chlorophyll a concentration of rice crop and the integrated index (MCARI/OSAVI [670,800 ] )

Table 1

 1 Average hyperspectral reflectance of rice crop at the first, second and third uppermost leaves (L1, L2 and L3) at six wavelength intervals denoting the Enhanced Thematic Mapper Plus (ETM+) of Landsat-7 (Unit :%)*

	Specta range	August 21 st , 2008	September 27 th , 2008	October 10 th , 2008
	(nm)	L1	L2	L3	L1	L2	L3	L1	L2	L3
	Blue(450-515)	6.0a	5.8a	6.0a	7.5a	7.5a	7.1a	8.0a	8.2a	7.7a
	Green(525-605)	11.8a	11.2a	11.5a	16.0a	15.8a	14.9a	18.7a	19.3a	19.3a
	Red(630-690)	6.4a	6.1a	6.5a	8.1a	8.1a	7.7a	10.2a	10.6a	11.1a
	NIR(775-900)	72.7a	73.1a	72.4a	57.4a	55.1b	52.2c	71.2a	71.8a	70.0a
	SWIR1(1550-1750)	43.6b	44.7a	45.0a	41.4a	39.3a,b	37.7b	48.6a	47.7a,b	45.9b
	SWIR2(2090-2350)	19.9b	20.5a	21.4a	26.9a	25.5a,b	24.4b	28.4a	27.1a	24.8b
	* Means followed by the same letter in each row are not significantly different from each other by Duncan test at
	alpha = 0.05.

Table 2

 2 Statistical description of pigment concentration for the first, second and third uppermost leaves (L1, L2 and L3) Means followed by the same letter in each row are not significantly different from each other by Duncan test at alpha = 0.05.

	Date	Pigment Position	L1	Chlorophyll a L2 L3	L1	Chlorophyll b L2	L3	L1	Carotenoids L2	L3
	08/21	Mean SD	1.97a 0.32	2.12a 0.33	2.07a 0.45	0.66b 0.12	0.71a,b 0.75a 0.14 0.16	0.74b 0.12	0.81a 0.12	0.80a 0.13
	09/27	Mean SD	2.40a 0.82	2.19a 0.89	2.11a 0.80	0.82a 0.31	0.80a 0.36	0.81a 0.35	0.91a 0.27	0.81a 0.26	0.81a 0.26
	10/10	Mean SD	1.63a 0.76	1.39a 0.74	1.02b 0.55	0.53a 0.28	0.48a 0.27	0.37b 0.22	0.75a 0.27	0.65a 0.28	0.50b 0.21
	*

Acknowledgements

This research was supported by the China Postdoctoral Science Special Foundation Project (201003712), the Start Research and Fund Profect of Hangzhou Normal University (2011QDL23), the National Basic Research Program (973) of China (2010CB126200), the Agro-Industry R&D Special Fund of China (200903051), and the National Hi-Tech Research and Development Program (863) of China (2007AA10Z205).