Zhiming Guo

Liping Chen
email: chenlp@nercita.org.cn

Chunjiang Zhao

Wenqian Huang

Quansheng Chen

Nondestructive estimation of total free amino acid in green tea by near infrared spectroscopy and artificial neural networks

Keywords: Near infrared spectroscopy, back propagation neural network, nondestructive estimation, free amino acid, green tea

The total free amino acid of green tea was nondestructive estimated by near-infrared (NIR) spectroscopy combined with multivariate calibration, compared the performance of back propagation neural networks (BP-NN) and partial least squares (PLS) regression analysis. The original spectra of tea samples in wavelength range of 10000-4000cm -1 were acquired. Spectral pretreatment methods were applied to reduce the systematic noise, and enhance the contribution of the chemical composition. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that the performance of BP-NN model was superior to the performances of PLS model, from the point of view of the predictive ability. The optimal results of the BP-NN model with multiplicative scatter correction spectral pretreatment were achieved as follow: RMSEP=0.246 and R p =0.958 in the prediction set, respectively. It can be concluded that NIR spectroscopy combined with BP-NN has significant potential in quantitative analysis and monitor of free amino acid content in green tea.

Introduction

Tea is considered one of the most popular beverages consumed worldwide. It is made from the leaves of the plant Camellia sinensis (L.). There are different tea varieties with different external qualities and inner qualities due to morphologic and chemical  Corresponding author. Tel: +86 10 51503696; fax: +86 10 51503886. E-mail address: chenlp@nercita.org.cn (L.P. Chen). Financial supported by National Natural Science Foundation (No.31071324) and National High Technology Research and Development Program of China (No. 2011AA100703) diversities [START_REF] Moreda-Pineiro | The classification of tea according to region of origin using pattern recognition techniques and trace metal data[END_REF][START_REF] Chen | Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition[END_REF] . Tea has important physiological properties and potential health benefits due to the presence of compounds such as polyphenols, amino acids, vitamins, carbohydrates, caffeine, and purine alkaloids, and those chemical constituents influence the taste and flavor of tea infusions [START_REF] Naithani | Decline in antioxidant capacity of Indian herbal teas during storage and its relation to phenolic content[END_REF][START_REF] Hung | Sequential determination of tannin and total amino acid contents in tea for taste assessment by a fluorescent flow-injection analytical system[END_REF] . Tea flavor is enhanced by the umami taste of amino acids. The predominant amino acid in tea infusion is a unique amino acid, theanine, which exists only in the free form and accounts for about 50% of the total free amino acids (TFAA). It has been reported that theanine can decrease the level of norepinephrine and serotonin in brain [START_REF] Lekh | L-theanine: a unique amino acid of green tea and its relaxation effect in humans[END_REF] , and intake of theanine by hypertensive rats results in decreased blood pressure [START_REF] Kuroda | Antimutagenic and anticarcinogenic activity of tea polyphenols[END_REF] . The total content of amino acids in green tea is the greatest in all kinds of tea. It has been demonstrated that there is a relationship between the quality of green tea and the amino acid contents [START_REF] Sakanaka | Antimicrobial effects of green tea polyphenols on thermophilic spore-forming bacteria[END_REF][START_REF] Ding | Direct determination of free amino acids and sugars in green tea by anion-exchange chromatography with integrated pulsed amperometric detection[END_REF][START_REF] Alcazar | Differentiation of green, white, black, oolong, and Pu-erh teas according to their free amino acids content[END_REF] . However, amino acid levels in tea vary during each manufacturing stage. Young two-leaf flushes are typically chosen for manufacturing the finest tea because theanine accumulates in growing shoots [START_REF] Kim | L-theanine, an amino acid in green tea, attenuates β-amyloid-induced cognitive dysfunction and neurotoxicity: Reduction in oxidative damage and inactivation of ERK/p38 kinase and NF-κB pathways[END_REF] . Therefore, a rapid assay for TFAA in tea is essential for quality control, evaluation and classification in tea processing.

There many descriptions of procedures for analysis of amino acids in green tea based on high-performance liquid chromatography (HPLC) [START_REF] Reich | HPLC methods for identification of green tea and green tea extract[END_REF][START_REF] Wang | Analysis of free amino acids in Chinese teas and flower of tea plant by high performance liquid chromatography combined with solid-phase extraction[END_REF] , capillary electrophoresis (CE) [START_REF] Aucamp | Simultaneous analysis of tea catechins, caffeine, gallic acid, theanine and ascorbic acid by micellar electrokinetic capillary chromatography[END_REF] , fluorometric flow-injection analytical [START_REF] Kato | Rapid analysis of amino acids in Japanese green tea by microchip electrophoresis using plastic microchip and fluorescence detection[END_REF] and ninhydrin colorimetric assay. Although these assays mentioned above are accuarate to quantify individual compounds, they are labor intensive, time-consuming and requires large quantities of regents for quality control purposes.

Near infrared reflectance (NIR) spectroscopy has been proved to be a powerful analytical tool that can be acted as a replacement of time-consuming chemical assay and there is no need for sample preparation or trained staff to perform the analysis routinely. NIR spectroscopy can be used online as well as in the field using the appropriate instrument. It has been introduced as a rapid and non-destructive method for evaluating tea quality parameters (such as polyphenols, catechins, gallic acid, caffeine and theobromine) [START_REF] Luypaert | Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.)[END_REF][START_REF] Chen | Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration[END_REF][START_REF] Sinija | FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules[END_REF][START_REF] Chen | Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy[END_REF] . The few published studies on green tea focused mainly on the prediction of bioactive components. In the literature no studies were found testing NIR spectroscopy for the prediction of free amino acid composition of green tea. Although the linear partial least squares (PLS) analyses is the most common techniques for spectral calibration and prediction [START_REF] Kasemsumran | Improvement of partial least squares models for in vitro and in vivo glucose quantifications by using nearinfrared spectroscopy and searching combination moving window partial least squares[END_REF] , with PLSR being the most accurate, other nonlinear techniques e.g. artificial neural network (ANN) have got much less attention and were rarely explored for the spectral analysis in tea sciences. There has been no report on using artificial neural networks for studying the TFAA dependence on the main components of the tea samples

The objective of this work was to evaluate the ability of NIR spectra to predict the development of TFAA in whole green tea leaves. In addition, the prediction capabilities of different calibration models, including PLS and ANN were investigated and compared for their use in modeling the TFAA. The results of this study may open a wide range of applications in process control or in laboratory routine for the quantification of TFAA in tea.

Materials and methods

Preparation of samples

A total of 90 green tea samples were collected from different geographical regions of PR China (i.e. Jiangsu, Henan, Zhejiang, Hunan, Fujian, Anhui, Jiangxi, Sichuan, Guangdong and Yunnan) for experiments. Based on the results of the pre-test, the moisture content of each commercial green tea was 4.0 ± 0.5%. The chemical compositions of these tea samples varied with their species, cultivation conditions such as weather, temperature, moisture, latitude, and season, and process of manufacturing [START_REF] Ngetich | Responses of tea to environment in kenya. 1. Genotype environment interactions for total dry matter production and yield[END_REF] . Samples were stored under controlled circumstance (20°C and 45% relative humidity) before measurements.

Reference measurement

The content of TFAA in tealeaves was measured by a ninhydrin colorimetric assay, which can refer to GB/T 8314-2002, a national standard method used to determine free amino acids content of tea infusions in China. The calibration standard is Ltheanine, which was obtained from Sigma (St. Louis, MO, USA). In a 25-ml volumetric flask, 1.0 ml of each tea infusions was added. This was followed by addition of 0.5 ml of 1/15 mol/L phosphate buffer solution (pH 8.0) and 0.5 ml of 2% ninhydrin solution containing 0.8 mg/ml of SnCl 2 • 2H 2 O. The mixtures in the volumetric flasks were then placed on a boiling water bath for 15 min. The probes were quickly cooled with cold water, and adjusted to 25 ml with water. After they were left to stand still for 10 min, a UV-VIS spectrophotometer (UV-1601, Beijing Rayleigh Analytical Instrument Co., Ltd, China) used to detect the absorbance of the reaction solution in a 10mm light-path cell at 570 nm.

NIR spectra acquisition

An AntarisTM II Method Development Sampling (MDS) system (Thermo Fisher Scientific Inc., Madison, WI, USA) spectrometer was used to measure diffuse reflectance spectra from 10,000 to 4000 cm -1 at 4 cm -1 resolution by co-adding 32 scans. Spectra were using an integrating sphere and an empty cell as a reference. FT-NIR measurements were performed with a high sensitivity InGaAs detector with a tungsten lamp as the NIR source. The reflectance values (R v ) were converted into absorbance (A) values using the formula A=log (1/R v ) for data analysis.

The standard sample accessory holder was used for performing the tea spectra collection. The sample accessory holder is sample cup specifically designed by Thermo Fisher Scientific. For each tea sample, 10±0.1g of dry tealeaves was filled into the sample cup in the standard procedure depending upon the bulk density of materials. When spectra collecting, tea sample was collected one times every rotating the cup 120 degree angle, thus, each sample was collected three times. The average of these three spectra was used in the next analysis.

Spectral data processing

NIR spectra could be affected by the physical properties of the samples and other environmental noises. Thus, it is necessary to perform mathematical processing to reduce the systematic noise, and enhance the contribution of the chemical composition [START_REF] Hao Lin | Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations[END_REF] . Pretreatment is usually performed in order to furnish more parsimonious PLS models, by avoiding the presence of certain spectral trends before regression. In the present work, it was only applied to the experimental NIR data set, where scattering corrections are frequently applied. Spectral derivatives may also be employed to improve resolution and to highlight the selectivity towards a particular analyte when strong multicollinearity is present. Fig. 1a presents the raw spectra profile of green tea sample and the raw spectra require to be preprocessed.

In this study, several spectral preprocessing methods were applied comparatively. These methods are standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative and second derivative. SNV is a mathematical transformation method used to remove slope variation and correct scatter effects in spectra [START_REF] Fearn | On the geometry of SNV and MSC[END_REF] . Each spectrum is corrected individually by first centering the spectral values, and then the centered spectrum is scaled by the standard deviation calculated from the individual spectral values. MSC is another important procedure for the correction of scatter light [START_REF] Ely | Analysis of the effects of particle size and densification on NIR spectra[END_REF] . It is used to modify the additive and multiplicative effects in the spectra on the basis of different particle sizes. First and second derivatives focus on eliminating baseline drifts and enhancing small spectral differences. Comparing with these preprocessing methods, MSC method was as good as SNV, better than first and second derivatives. The reason was that dry green tea particle solid was easy to bring in scatter light. MSC and SNV spectral preprocessing methods have better ability in correcting light scatter, and can also remove slope variation. MSC pretreated spectra was shown in Fig. 1b.

ANN description

ANN is multivariate calibration tools, which are especially suited for nonlinear systems. ANN is a powerful analytical tool because of the flexible learning algorithm, diverse network topology, fast learning algorithm and high error tolerance [START_REF] Huang | Applications of artificial neural networks (ANNs) in food science[END_REF] . These advantages will lead to more sophisticated applications of ANN in food science, particularly in sensory analysis and analytical chemistry. Back propagation (BP) is the most commonly used algorithm to train feedforward neural networks for spectral data interpretation [START_REF] Liu | Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN[END_REF] . In this study, a multi-layer perception (MLP) based feedforward ANN which uses back propagation learning algorithm, was applied for modeling TFAA concentration.

The network consists of an input layer, hidden layers and an output layer. Fig. 2 illustrates the architecture of the network that is most commonly used with the back propagation algorithm-the multilayer feed-forward network. The determination of number of neurons in hidden layers is very important as it affects the training time and generalization property of neural networks. A higher value of neurons in hidden layer may force the network to memorize the patterns which it has seen during training whereas a lower value of neurons in hidden layer would waste a great deal of training time in finding its optimal representation [START_REF] Benardos | Optimizing feedforward artificial neural network architecture[END_REF] . There is no general rule for selecting the number of neurons in a hidden layer. The most popular approach to finding the optimal number of neurons in hidden layer is by trial and error [START_REF] Abutaleb | A neural network for the estimation of forces acting on radar targets[END_REF] . In this study, trial and error approach was used to determine the optimum neurons in hidden layer of the network.

Inputs

Input layer Hidden layer Output layer

Outputs

h n (x) h 1 (x) f(x)

Software

All computations were performed using a Matlab (version 7.9) Toolbox (Mathworks Inc., USA) under Windows XP. For the spectral acquisition Result Integration 3.0 (Antaris II System, Thermo Electron Co., USA) was used.

Results and discussion

Reference data description

Green tea samples were divided randomly into calibration sets of 60 samples and prediction sets of 30 samples. Table 1 shows the descriptive statistics (mean, standard deviation and range) for calibration and prediction sets. As seen from Table 1, the range of reference measurements results of TFAA in the calibration set almost covers the range in the prediction set, and their standards deviations between the calibration and prediction sets are no significant differences. The reference values of these four properties had a broad range of variation, which was helpful for the calibrations. Therefore, the distribution of the samples is appropriate both in the calibration and the prediction sets.

Models evaluation

The performance of the calibration model was evaluated in terms of the root mean square error of cross-validation (RMSECV). For RMSECV, a leave-one-sample-out cross-validation was performed: the spectrum of one sample of the calibration set was deleted from this set, and a model was built with the remaining spectra of the calibration set. The left-out sample was predicted with this model, and the procedure was repeated with leaving out each sample of the calibration set. Root mean square error of prediction (RMSEP) was used to evaluate the performance of the prediction set in the prediction process. Correlation coefficient (R) between the predicted and the measured value were calculated for both the calibration and the prediction sets.

Generally, a good model should have lower RMSECV, RMSEP and higher R but small differences between RMSEP and RMSECV.

PLS models

A PLS model performance evaluation was performed using leave-one-out cross validation for the prediction of TFAA in tea samples. It is generally known that the number of PLS factors and the spectra preprocessing are two critical factors in calibrating model for the application of PLS algorithm [START_REF] Chen | Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy[END_REF] . In the experiment, the number of PLS factors and the spectral preprocessing were optimized by cross validation, and, they were determined by the lowest value of RMSECV. MSC and SNV spectral pretreatments were used to produce reasonable results for the spectra, respectively. Different PLS factors were applied to build the calibration models, and no outliers were detected in the calibration set during the development of PLS models.

The results of calibration and prediction sets for each model through different preprocessing methods are presented in Table 2. With a comparison of results for calibration and prediction by the aforementioned evaluation standards, the PLS models based on MSC spectral pretreatment turned out to be the optimal results which were achieved as follows: R c = 0.927 and RMSECV = 0.337 in the calibration set; R p = 0.925 and RMSEP = 0.323 in the prediction set. Fig. 3 is the scatter plot of reference measurements and NIR predicted results by the optimal PLS model.

BP-NN models

In order to improve the training speed and reduce the training error, after spectra pretreatment, principal components (PCs) issued from principal component analysis (PCA) for dimensionality reduction were applied as input of BP-NN models because the training time increased with the square of the number of training samples and linearly with the number of variables (dimension of spectra). The PCs from the NIR spectra was used as new eigenvectors to TFAA. Four and three PCs were obtained by the criterion for an increment of explained variance lower than 0.25% [START_REF] Urbano-Cuadrado | Near infrared reflectance spectroscopy and multivariate analysis in enology: Determination or screening of fifteen parameters in different types of wines[END_REF] , and the cumulative reliability was 99.39% and 99.28% for MSC and SNV spectral data, respectively.

In the model development by using BP-NN method, three layers of BP-NN were arranged. The number of PCs was also optimized according to cross validation. The output of BP-NN was TFAA content in tea. The hidden nodes were optimized by 'trial and error', and optimal of hidden nodes is evaluated by the minimal mean square error (MSE) value [START_REF] Svozil | Introduction to multi-layer feed-forward neural networks[END_REF] . Several network architectures were tested by varying the number of neurons in the hidden layer with different initial weights. Adjusting by many times, learning rate factor and momentum factor were all set 0.1; all initial weights were set 0.3; scale function was set the 'tanh' function. The permitted regression error was set 0.01, and the maximal iterative epochs were set 1000 times. The optimal performance could be achieved, when 4 PCs and 8 nodes in the hidden layers were included for MSC pretreatment, and when 3 PCs and 8 nodes in the hidden layers were included for MSC pretreatment. With these optimal parameters, the BP-NN model, based on MSC spectral pretreatment, could obtain better prediction results with R c = 0.979 and RMSECV = 0.182 in the calibration set, and R p = 0.958 and RMSEP = 0.246 in the prediction set. Fig. 4 is the scatter plot of reference measurements and NIR predicted results by the optimal BP-NN model with MSC spectral pretreatment.

Discussion of the results

The scope of the paper is to compare the performances of linear PLS and nonlinear BP-NN analyses for the prediction accuracy of TFAA in green tea. The PLS models achieved an acceptable results for the prediction of TFAA content of green tea, and these results indicated that it was feasible to utilize spectroscopic technique for the detection and monitoring of free amino acid composition in tea leaves. The BP-NN models performed a better performance than that of PLS models, which indicated that the latent nonlinear information was helpful to improve the prediction performance. The performance of BP-NN models was a little better than that of PLS models. ANNs are suitable for solving nonlinear problems. This suggests that there are nonlinear relationships between the TFAA content and absorption of NIR spectra, more work would be done to discover the useful information or effective wavelength or wavelength bands for the nondestructive determination of TFAA content in green tea leaves.

Conclusion

Near Infrared spectroscopy combined with a suitable preprocessing method and with BP-NN regression is shown to be a promising method for predicting free amino acid composition in whole tea samples. The BP-NN models were developed for the prediction of properties of interest, and BP-NN models outperformed PLS models.

The correlation coefficient R p and RMSEP in prediction set by BP-NN models were 0.958 and 0.246 for TFAA, respectively. The NIR technique can estimate free amino acid compositions in whole green tea, nondestructively with good accuracy and has enormous potential in improving the efficiency for quality control, evaluation and classification in tea processing.

Based on results achieved in this study, it is recommended to adopt BP-NN analysis as the proper calibration method to predict TFAA in green tea. However, further studies are needed to provide in depth interpretation of the successful measurement of tea properties that do not possess direct spectral response in NIR region.

Fig. 1 .

 1 Fig. 1. Spectra of green tea obtained from (a) raw data and (b) MSC preprocessing data.

Fig. 2 .

 2 Fig. 2. Illustration of a feed-forward back propagation neural network containing input, hidden and output layers.

Fig. 3 .

 3 Fig. 3. PLS results: predicted vs. measured TFAA content for calibration (o) and prediction () set

Fig. 4 .

 4 Fig. 4. Predicted values of calibration (o) and prediction () set against reference values using BP-NN method.

Table 1 .

 1 Reference measurement of free amino acid content (g/g, %) in calibration and prediction sets.

	Subset	N	Range	Mean	SD b
	o. a
	Calibration set	60	0.976~5.127	2.986	0.905
	Prediction set	30	1.236~4.898	2.939	0.883

a No: Number of samples; b SD: Standard deviation.

Table 2 .

 2 Results of calibration and prediction for PLS and BP-NN models with MSC and SNV spectral pretreatments

	Method	Pretreat ment	P Cs a	R c	Calibration RMSECV(% )	R p	Prediction RMSEP( %)
	PLS	Raw	8	0.8 98	0.424	0.8 90	0.401
	PLS	SNV	7	0.9 25	0.344	0.9 10	0.356
	PLS	MSC	7	0.9 27	0.337	0.9 25	0.323
	BP-NN	SNV	3	0.9 72	0.207	0.9 50	0.265
	BP-NN	MSC	4	0.9 79	0.182	0.9 58	0.246
	a principal component factors

Acknowledgements

This research was financially supported by National Natural Science Foundation (No.31071324) and National High Technology Research and Development Program (No. 2011AA100703).


Feng Tan

Xiaodan Ma

Chun Wang

Tingyi Shang

Data Analysis of Cold Rice Blast Based on Near Infrared Spectroscopy

Keywords: Cold Rice, Blast, Near Infrared Spectroscopy, Disease Detection

In order to overcome the low diagnostic accuracy of traditional blast visual defects, set cold rice blast for example, four data groups has been acquainted through near infrared spectroscopy, the first group is healthy and diseased planting stock; the second group is three diseased levels of leaf blast; the third group is five diseased levels of grain blast; the forth group is four diseased levels of panicle blast, according to the analysis , we can know that different circumstances of plants had their own near-infrared spectral bands which made the preliminary basis for real-time detection of cold Rice blast.

Introduction

The rice planting area in China accounts for 1/4 of the food crops area, While the production close to 1/2 of the national's total grain output, and over 1/2 of the commodity, So rice plays a very important role in China's grain production. Rice blast 1 is one of the most important diseases in rice production ,Widely distributed in the world rice area in the whole growth period of rice causing widespread damage. Blast is a major disease in the production of rice production in cold land, Year of the disease cuts 23% of the rice production in cold, severe up to 45%, or even never 1 Corresponding Author:Wang Chun(1963-), PhD Supervisor Professor produced. Among them, the rice leaf blast and the rice ear and neck blast caused the largest loss. The annual incidence rate of leaf blast is 40-50%,Up to 80% when it is In recent years, for the advantages of non-destructive, fast and accurate , multi-spectral,The technology [START_REF] Zhang | Study of Rice Blast Retrospect and Prospect[END_REF][START_REF] Wenbo | Potential for rice production in Northeast China and its development strategy[END_REF][START_REF] Gold | Occurrence of Rice Blast Causes and countermeasures[END_REF][START_REF] Yuan | off into the macro. 2005 blast in Heilongjiang Province of the outbreak investigation and control[END_REF][START_REF] Xueyi | Blast in the north and integrated control[END_REF] is more and more used in the field of plant diseases detection. Adams et al [START_REF] Adams | Yellowness index: an application of spectral second derivatives to estimate choruses of leaves in stressed vegetation[END_REF] used 550nm, 650 and 750nm spectral image to detect trace elements in vegetables; Bravo [START_REF] Bravo | Early disease detection in wheat fields using spectral reflectance[END_REF] detected the wheat yellow rust in the visible and near infrared bands ;Yang Z et al [START_REF] Yang | Using ground-based multispectral radiometry to detect stress in wheat caused by geenbug (Homoptera: Aphididae) infestation [J][END_REF] ,with the help of the 16 band spectral analyzer studied the wheat canopy invasion which was Infringed by Schizaphis germanium .In 1994,Qi-Ying Lin [START_REF] Lin Qi-Ying | Dwarf Disease of Rice cluster spectral properties of the virus VII[END_REF] and other Scientists imposed a laser Raman spectrometer to take a Raman scattering experiment towards the clusters of rice dwarf virus in three samples which were purified ,The results showed that the intensity of different viruses is different. mechanism of action which the Small molecule cysteine stay in the silver surface, Hao Yaqiong 2002 [START_REF] Hao Yaqiong | Cysteine substrate in the adsorption mechanism of silver Raman spectra[END_REF] using surface-enhanced Raman scattering discussed and studied the adsorption method, the use of molecular spectroscopy lays the foundation that explores the mechanism and further improve the enzyme activity. (In)2002, Cheng Hongyan [START_REF] Cheng Hongyan | C60 derivative molecules Raman spectroscopy[END_REF] found that C60 derivatives which its Raman spectra is significantly different from the original in Raman spectroscopy study ,Analysis of the emergence of organic functional groups makes the C60 changes the molecular structure.

In 2003 Yu duowei [START_REF] Yu | DNA molecules China and apyrimidinic fiber Raman spectroscopy[END_REF] utilized Raman spectra analysis whether the acid induce the part of the DNA purine, pyramiding fall off,And whether the reasons for the protonated pyramiding purine could be stronger than the relevant. In 2003, Exotic [START_REF] Feng Min | Raman spectroscopy of crystalline SiC crystals[END_REF] and others used the Raman scattering method to study the quality of the SiC single crystal ,which can improve Lely.they found that the structure of the sample found is 6H-SiC,also pointed out that more defects exist in the sample, gave the Raman spectra of SiC within 100-4000cm-1 for the first time, and detected the crystal structure successfully with the Raman spectroscopy. In 2010, Kang Yi Pu [START_REF] Kang Pu | Xanthomonas oryzae races in the nano-silver membrane of surface-enhanced Raman spectroscopy[END_REF] and other people used the portable Raman spectrometer to measure the silver membrane Xoo seven races of the surface enhanced Raman spectroscopy (SERS),The results showed that the seven races of the SERS spectra in the peak position and relative intensity of the peak which has many differences, They Proved that this method can quickly and easily test the different Xoo races.Wu Di et al [START_REF] Di | Based on visible / near-infrared spectroscopy of the early detection of Botrytis cinerea eggplant leaves [J][END_REF]

II Materials and methods

Sample selection

Experimental samples were taken from experimental plots of rice in cold region in Heilongjiang Reclamation, during the experiment, four groups of experimental samples were selected, healthy and infected plants in different parts of plants; infected leaves of the three disease rating; infected grain level of the five diseases; infected stems of four disease rating.

Experimental apparatus

This experiment use WQF-600N FT-NIR spectrometer, the instrument is a measurement of material absorption of infrared radiation (or transmittance) of the analytical instruments. Since each substance has a characteristic absorption spectrumthere are only certain wavelengths to absob while other wavelengths is not absorbed, so the characteristics of the absorption spectra can be used to carry out qualitative analysis of substances. In addition, the total amount of material is proportional with the absorption, the absorption spectra can also make use of quantitative analysis of substances. Wave number range: 3300cm-1 ~ 10000cm-1; resolution: better than 4cm-1; wave number accuracy: better than the resolution set by the 1 / 2; transmission repeatability: 0.5% T; 100% T line of the letter noise ratio: 4300cm-1 ~ 4400cm-1, 32 scans (equivalent to 1 minute measurements), S / N better than 10000:1 (RMS value); figure 1 shows what we select is rice plant stems, leaves and grains were compared with the spectrum, from which we can see that the transmittance of the spectrum of three parts are not in the same position; Figure 2 shows the spectrum of plant stems, leaves and seeds with a serious blast of damage. Figure 1 and Figure 2 shows the comparison which can be seen that stems transmission are relatively low, while the leaves and transmittance spectra of grain almost the same intensity, Wave number from the variation of transmittance on the stem and the transmittance spectrum of grain is in good agreement.

blast leaves of three levels of data collection Disease

According to different diseases of rice plant of infrared spectrum acquisition, the collected results are shown in Figure 3

Five diseased levels of infected seed of data collection

Figure 4 shows the near infrared transmittance spectroscopy of the five grains ,Contrast can be seen in Figure 3, The absorption peak in Grains is very high consist with rice leaves. The reason is that leaves organic components and grain shell organic components is very similar so the absorption peak location will be the same.

Although part of the near-infrared light into the leaves or seeds inside, the grain ,is also the seed inside part of the internal components and the internal components inside leaves are not still change, so the absorption spectrum of the intensity at each wavelength is different. It can be seen from Figure 4, five different grains in case of blast injury of the transmission spectrum do not show very good consistency in parallel. according to analysis, it is because in the acquisition of spectrum, There are too many grains entering into the loading room, and with random arrangement grains, also the near infrared spectrometer is smaller than the grain size of the spot, so every time in the collection process, light pass through the outer surface of grain and incident angle of the interface is different, They lead to the light penetration depth in the grain interior change, so the curve characteristics in the Characterization of grain display inconsistency. From the analysis view, although the five kinds grains of spectral curves are irregular, due to different levels of disease, in some bands they have relatively strong discrimination, and also five curves do not cross too many points. If the differential sum rule act on the entire point ,it can eliminate part of the interference-free spectrum.

3..3 Four diseased levels of Infected stems of data collection

It can be seen from figure 5,like the leaves and seeds, the stem has a feature that the transmission of serious rice blast is higher than that of normal plants in the range of 4000WN ~ 10000WN.Besides,the regularity of the stem and seed is better than that of leaf, differences of stems in 40000WN ~ 70000WN are more obvious while differences in grains are more obvious in the 7000WN ~ 10000WN.In race's growing process, the changes of stem and seed are slight, only change with some reasons such as illness or under the influence of external transformation.Therefore,it is more reliable and more regularity among detection of the disease. The leaves and the light, moisture and other factors related larger, such as leaves in absence of sufficient water performed significantly, so the regularity is relatively poor. Although the parallel spectrum and regularity are not strong, the same conclusion still can be drawn from the analysis. Integrated to analyze ,It will be more effective to diagnose rice blast, and more easier to analyze quantitatively of the rice blast, if it is possible to analyze the rice blast combine with the strongest differences of stem, leaves and seeds. The transmission of spectra can be collected to the appropriate map data. Table 1 shows the spectral data comparison of leaves between serious leaf disease and disease-free. It can be seen that larger wave number, the two large difference appears transmission intensity, and where is the small wave numbers becomes smaller. The difference reaches the maximum value in the 7899WN from the difference of transmission intensity view. When we subtract the two spectra ,we find that it has the largest difference of 6.106 in the 7565WN. It also indicates that the largestly different wavelength must concern the wave number 7565.

Ⅳ Error sources

The spectroscopy which is collected by near infrared spectrometer will have a slight error compared with the actual spectroscopy, mainly from the following:

1) The requirement which the moving mirror shifts from negative infinity to positive infinity leads to the ends of the interferogram data suddenly cutting off and then gets the incomplete data when the interference spectrum is acquired,due to the the actual moving distance of the moving mirror spectrometer.

2) when the interferogram is changed to the Spectrogram,the compensation of incomplete data (apodization) also make the peak distortion, resulting in error;

3) The transform used for a fast algorithm of discrete generalized fourier transform, will lead to the error between the data and continuous fourier transform.

Ⅴ Summary

this study conducted qualitative analysis of rice in cold land. there are four groups of experimental samples, namely,different parts in healthy plants and infected plants;three disease levels of infected leaves;five disease levels of infected grains;four disease levels of infected stems;The results showed that the plants have their own near-infrared spectral bands under different circumstances,the conclusion lays an early foundation for real-time detection of rice blast cold disease using the near infrared spectroscopy technology.

  very serious; Up to 50% of neck blast. Traditional method of classification detection of rice disease resistance is based on visual concept of the disease by plant protection experts, it has drawbacks of subjective factors, and time-consuming. Therefore, there is an urgent need for a new technology to solve the technical problems in cold blast resistance classification, In modern agricultural production, there's an increasingly high demand for the frequency, speediness and accuracy of work in the forecast on cold blast, classification and the evaluation of prevention work. This claims a new research of cold rice disease diagnosis technology.

  technology achieve the early detection of Botrytis cindered eggplant leaves with the near infrared spectroscopy. Infected with the blast of cold rice, The changes of their Agencies within the physiological and external shape of each band is bound to cause the differences in spectral reflectance characteristics of change. Based on a combination of these changes,we can create a model for disease diagnosis. In this paper, We combine the advantages of the spectral analysis and image processing technology, adopt multi-spectral vision technology to explore identified classification test on disease resistance of rice varieties, The work provide technical support for automated identification .

  splitter: CaF2; detector: DTGS (Pubs, InGaAs can beelection); source: air-cooled tungsten light source; Data Systems: General computer, connect inkjet printer or laser printer can output high-quality spectra; Software: Chinese Windows operating system under the common operating software systems, including spectrum acquisition offer, spectral transform, spectral processing, spectral analysis, construction the conventional mode of operation, and all other functions, which can provide both chemo metric software. Working with a lot of third party software in WINDOWS run together under the same time, users can also develop new operational procedures of spectral data in their needs .Data Acquisition and AnalysisⅢ Healthy plants and infected plants in different parts of data collectionIn order to study cold Blast spectral characteristics, In the Experiment, We first study the different parts between healthy plants and infected plants, stems, leaves and grains. The spectral characteristics is Obtained by near infrared spectroscopy instruments projection . The result is shown in Figure1and Figure2below.

Figure

  Figure 1spectrum contrast between healthy stem, leaf and grain

  figure correspond to the three diseases of rice plants in order to reduce the transmittance spectrum. It can be seen from the figure, leaves transmittance in the race's serious incidence is the strongest, Bottom curve is the transmittance spectra(of) healthy plants, and its transmission strength is the weakest. And from the general trend, as the wave number decreases, the difference become smaller between transmission intensity, and local transmission in a large wave number is relatively large different In other words, in the near infrared band, as the wavelength increases gradually disease transmission in different degrees of intensity become not obvious.

Figure 2 Figure 3

 23 Figure 2 spectrum contrast between diseased stem, leaf and grain

Figure 4

 4 Figure 4 Blast Grain contrast between five infected level

Figure 5

 5 Figure 5 Panicle blast contrast between three Panicle level

Table 1the

 1the

			spectrum constract between
		healthy and diseased lamina
		Wave	Transmittance Wave	Transmittance
		number		number
		9898	108.255	9403	111.691
	Diseased	8898	114.439	8403	116.055
	leaves
		7899	118.233	7404	118.661
		9898	103.375	9403	106.256
	Heathy	8898	108.708	8403	110.387
	leaves	7899	112.245	7404	113.045
		6900	110.271	6404	112.369


Zhanyu Liu
email: liuzhanyu@zju.edu.cn

Cunjun Li

Yitao Wang

Wenjiang Huang

Xiaodong Ding

Bin Zhou

Hongfeng Wu

Dacheng Wang

Jingjing Shi

Comparison of Spectral Indices and Principal Component Analysis for Differentiating Lodged Rice Crop from Normal Ones

Keywords: Hyperspectral remote sensing, Lodged rice, Principal component analysis (PCA), Vegetation indices (VIs), Artificial neural networks (ANN)

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

Introduction

Detecting plant health condition plays an important role in controlling disease and insect stress in precise pest management (PPM), which always results in yield loss and poor quality [START_REF] Everitt | Using airborne video, global positioning system, and geographical information system technologies for detecting and mapping citrus blackfly infestations[END_REF] . Although plant pest stress is predominantly concentrated in patches around stress centers, it is still widespread to spray agrochemicals indiscriminately over the entire field in practice [START_REF] Mccartney | Dispersal of foliar fungal plant pathogens: mechanisms, gradients and spatial patterns[END_REF] . It is necessary to accurately assess the pest stress distribution and damage caused by disease and insect pests [START_REF] Pedigo | Closing the gap between IPM theory and practice[END_REF] so that agrochemicals can be applied to reduce environmental pollution due to agrochemicals in the stressed patches.

Lodging at the late growth stage is caused by insect and fungal disease in paddy rice field, which often leads to yield loss and quality decrease [START_REF] Sōgawa | The rice brown planthopper: feeding physiology and host plant interactions[END_REF] . Accurate distribution information of lodging rice is helpful to spray agrochemicals and harvest in agricultural field management. Remote sensing has been proved to be more effective than ground surveys for detecting plant health condition in numerous studies [START_REF] West | The potential of optical canopy measurement for targeted control of field crop diseases[END_REF] . Perhaps vegetation indices (VIs) is the most popular technique of remote sensing to predict agricultural crop biophysical variables such as leaf area index, chlorophyll content/concentration, above-ground biomass, and percent vegetation cover and so forth [START_REF] Elvidge | Comparison of broad-band and narrow-band red and nearinfrared vegetation indices[END_REF] . In addition to VIs, principal component analysis (PCA) has been extensively studied as a data compression technique [START_REF] Holden | Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy[END_REF] .

Table 1 Broadband and narrowband spectral indices used in this study

Abbreviatio n

Formula Reference DVI NIR-R Richardson and Everitt [START_REF] Richardson | Using spectral vegetation indices to estimate rangeland productivity[END_REF] RVI NIR/R Jordan [START_REF] Jordan | Derivation of leaf area index from quality of light on the forest floor[END_REF] NDVI (NIR-R)/(NIR+R) Rouse et al. [START_REF] Rouse | Monitoring vegetation systems in the great plains with ERTS[END_REF] SAVI L=0. 5 1.5(NIR-R)/(NIR+R+0.5) Huete [START_REF] Huete | A soil-adjusted vegetation index (SAVI)[END_REF] OSAVI 1.16(NIR-R)/(NIR+R+0.16) Rondeaux et al. [START_REF] Rondeaux | Optimization of soil-adjusted vegetation indices[END_REF] MSAVI 2

2 2 1 ( 2 1) 8( ) 2
NIR NIR NIR R     
Qi et al. [START_REF] Qi | A modified soil-adjusted vegetation index (MSAVI)[END_REF] TVI 60(NIR-G)-100(R-G) Broge et al. [START_REF] Broge | Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[END_REF] IPVI NIR(NIR+R) Crippen [START_REF] Crippen | Calculating the Vegetation Index Faster[END_REF] TDVI 1.5( )

0.5 NIR R NIR R  
Bannari et al. [START_REF] Bannari | Transformed difference vegetation index (TDVI) for vegetation cover mapping[END_REF] RDVI NIR R NIR R   Roujean and Breon [START_REF] Roujean | Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[END_REF] EVI 2.5 6.5 7.5 1

NIR R NIR R B    
Liu and Huete [START_REF] Liu | A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[END_REF] GEMI

2 2 (1 0.25 ) ( 0.125) /(1 ) 2( ) 1.5 0.5 0.5 R R NIR R NIR R NIR R            
Pinty and Verstraete [START_REF] Pinty | GEMI: A non-linear index to monitor global vegetation from satellites[END_REF] BI 0.2909B+0.2493G+0.4806R+0.5568NIR +0.4438SWIR 1 +0.1706SWIR 2 Crist et al. [START_REF] Crist | Vegetation and soils information contained in transformed thematic mapper data[END_REF] GVI -0.2728B-0.2174G-0.5508R+0.7721NIR+0.0733SWIR 1 -0.1648 SWIR 2

Crist et al. [START_REF] Crist | Vegetation and soils information contained in transformed thematic mapper data[END_REF] WI 0.1446B+0.1761G+0.3322R+0.3396NIR-0.621SWIR 1 -0.4186 SWIR 2 Crist et al. [START_REF] Crist | Vegetation and soils information contained in transformed thematic mapper data[END_REF] NDWI (λ 860nm -λ 1240nm )/( λ 860nm +λ 1240nm ) Gao [START_REF] Gao | NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space[END_REF] LSWI (λ 860nm -λ 1640nm )/( λ 860nm +λ 1640nm ) Xiao et al. [START_REF] Xiao | Observation of fl ooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data[END_REF] NMDI

860
         
Wang et al. [START_REF] Wang | NMDI: A Normalized Multi-band Drought Index for Monitoring Soiland Vegetation Moisture with Satellite Remote Sensing[END_REF] *B,G,R,NIR,SWIR 1 and SWIR 2 represent surface spectral reflectance averaged over ranges of wavelengths in Landsat ETM+, viz.TM1 (450-515nm), TM2 (525-605nm), TM3 (630-690nm), TM4 (775-900nm), TM5 (1.55-1.75μm ) and TM7 (2.09-2.38μm ), respectively. λ i nm denotes surface spectral reflectance at i nanometer.

Furthermore, the application of artificial intelligent methods including artificial neural networks (ANN) and support vector machines (SVM) in plant stress detection has also been reported recently [START_REF] Karimi | Application of support vector machine technology for weed and nitrogen stress detection in corn[END_REF][START_REF] Shi | Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine[END_REF] . For example, SVM technology was used to detect weed and nitrogen stress in corn by Karimi et al [START_REF] Karimi | Application of support vector machine technology for weed and nitrogen stress detection in corn[END_REF] . Shi et al. [START_REF] Shi | Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine[END_REF] applied SVM model to discriminate the health status in rice and great classification was obtained. However, few studies have been conducted to resolve classification problems by VIs and PCA [10] .

The main goal of this research is to evaluate and compare the performances of broad-and narrow-band VIs (Table 1) and PCA in differentiating the normal rice crop from the lodged ones stressed by rice brown planthopper or rice panicle blast.

Methodology

Study Sites and Materials

There were two sampling sites in this research. One site was located in Friendship Farm (120°43′E, 46°39′N), Heilongjiang Province. The rice crop cultivar was Suijing 4, which was naturally infected with rice panicle blast. The other site was in Jiuxian Village (119°37′E, 29°48′N), Tonglu County, Zhejiang Province. And the rice crop cultivar was Hybrid rice 718, which was stressed by rice brown planthopper.

Hyperspectral Measurement

Canopy hyperspectral reflectance were measured using an ASD FieldSpec Pro FRTM Spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO, USA) throughout the whole spectral range from 350 nm to 2 500 nm. The spectral resolution of the instrument varied from 3 nm (<1 000 nm) to 10 nm (>1 000 nm), while the spectra were interpolated by the spectrometer software in 1nm interval. Therefore, each measurement generated a spectrum ranging between 350 nm and 2500 nm at 1nm increment.

The fiber optic sensor with a 25° instantaneous field of view (IFOV) was pointed to the paddy rice canopy about 1 m height at the nadir position. Reference panel measurements were made at the beginning of each set of canopy measurements. The reference panel painted by BaSO4 is a Lambert surface with a reflectance of no less than 99%. These radiance units were in turn converted to reflectance using scans of the BaSO4 white reference panel.

The hyperspectral measurement date was taken on 24th August and 28th September of 2007 in Heilongjiang and Zhejiang Province, respectively. The number of reflectance spectra of normal paddy rice were 12 and 35 in the two sites, respectively, and the number of reflectance spectra of lodged paddy rice were 10 and 35 in the two sites, respectively.

Hyperspectral Data Preprocessing

Hyperspectral reflectance was smoothed with a five-step moving average to suppress instrumental and environmental noise [START_REF] Kobayashi | Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[END_REF] . The hyperspectral reflectance represented the domains of 400 ~ 2 350 nm, and the missing segments corresponding to strong instrument and environment noise were not considered for further analysis [START_REF] Liu | Discrimination of lodged rice based on visible/near-infrared spectroscopy[END_REF] .

The total datasets collected from the two paddy rice fields were divided into three different subsets, namely, one training dataset and two testing subsets. The training dataset (n=52) was used to calibrate the artificial neural network (ANN) classification models with three quarters of the canopy hyperspectral reflectance data collecting on 28 th September of 2007, and one quarter was applied to validate the ANN classification models as the first testing dataset (n=18). While the hyperspectral reflectance data collecting on 24 th August of 2007 was applied as the second testing dataset (n=22).

Analytical Techniques

Principal component analysis (PCA) has been used as a data compression technique for preserving total variance in transformation and minimizing mean square approximate errors [START_REF] Holden | Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy[END_REF] . This technique is very suitable for hyperspectra data for high dependence and autocorrelation in adjacent wavebands [START_REF] Liu | Discrimination of lodged rice based on visible/near-infrared spectroscopy[END_REF] . In this study, the front two principal component spectra (PCs) were derived from the canopy hyperspectral reflectance, which could explain over 98% of information variation.

Fig1. Neural architecture for probabilistic neural networks (PNN)

Probabilistic neural networks (PNN) are a novel and composite neural network with radial basis function and competitive neural network. The neural architecture of PNN (Fig. 1) consists of a radial basis layer and competitive layer with the exception of input layer [START_REF] Sivandm | Introduction to neural networks using Matlab 6.0[END_REF] . In this study, PNN has been used to identify the lodged rice crop from normal ones at the canopy level. The neural nodes in the input layer were the broad-and narrow-band VIs and PCs. The competitive layer is also called as the output layer in practice. The neural nodes in the output layer were the classification of

Q ×1

Competitive layers Radial basis layers Input layers a 2 =y

Q Q ×1 P R ×1 1 n 2 K ×1 K× Q LW 1,1 ‖dist ‖ b 1 . * Q ×R Ω L W 2, 1 C R a 1 Q n 1 Q ×1 Q ×1 n 1
rice crops, viz. lodged and normal. The objective of the training process is examined to find the probabilistic mode between neural node of input layer and different classification of output layer. Additional detailed information on PNN can be found in the literature by Sivandm et al. [START_REF] Sivandm | Introduction to neural networks using Matlab 6.0[END_REF] .

The performance of VIs and PCA for classifying the lodged and normal paddy rice was evaluated with two statistical parameters, namely overall accuracy and Kappa coefficient [START_REF] Congalton | A review of assessing the accuracy of classification of remotely sensed data[END_REF] .

PNN and PCA were conducted with Matlab functions in Matlab 7.0 (R14). Hyperspectral reflectance data preprocessing was made in ViewSpec Pro (V5.6.10) and VIs were derived from canopy hyperspectral reflectance in Microsoft Office Excel 2003.

Results and Discussions

Canopy Hyperspectral Reflectance

The spectral response properties of rice crop to disease and insect stresses were very significant for identifying lodged rice from normal ones in PPM using hyperspectral remote sensing technique. Fig. 2 showed the canopy hyperspectral reflectance curves of normal rice and lodged rice caused by rice brown planthopper. Six wavelength intervals indicated the spectral differences (Table 2) because the photon transferring process was different between normal and lodged rice crop. Maybe it's because after the rice plant was damaged and lodged due to disease and insect stresses, which resulted in evident changes in the arrangement structure of rice panicles, leaves and stems.

Fig.2 Canopy hyperspectral reflectance of normal rice crop and lodged rice crop stressed by rice brown planthopper

As shown in Fig. 2, the spectral curve shape of lodged rice crop was similar to that of the normal ones through the entire range (400~2350 nm), but the spectra amplitude was different. The hyperspectral reflectance of lodged rice crops increased about 75.7%, 70.9%, 79.2%, 62.3%, 64.7% and 76% in the blue-green (450~515 nm), green (525~605 nm), red (630~690 nm), near-infrared (775~900 nm), shortwave infrared (1550~1750 nm and 2090~2350 nm), respectively (Table 2).

Results of probabilistic neural network (PNN) classifier

The overall accuracy and Kappa coefficient of broad-band and narrow-band spectral indices and PCA for discriminating lodged rice from normal ones based on PNN were shown in Table 3. PCA had the highest average overall accuracy (97.8%) and Kappa coefficient (95.5%) than the broad-band and narrow-band spectral indices for the two testing datasets, DVI and BI followed. Among the spectral indices, DVI had relative higher average overall accuracy (90.9%) and Kappa coefficient (0.796). Maybe it was because that DVI was more sensitive to changes of photosynthesis pigment (i.e. chlorophyll and carotenoid) of rice organs due to lodging caused by rice brown planthopper or rice panicle blast at the late growth stage.

Conclusions

The results of our study showed that the spectral indices based on distance from isoline to soil line such as DVI, BI, GVI and WI had higher overall accuracy and Kappa coefficient than those spectral indices based on ratios of spectral bands such as RVI, NDVI, SAVI series, and so on. Ratio-based spectral indices suppressed the spectral difference between different spectral bands, which made it more difficult to discriminate lodged rice from normal ones than distance-based spectral indices that amplified the spectral difference between different spectral bands [START_REF] Asrar | Theory and applications of optical remote sensing[END_REF] . The front several principal components (PCs) always accounted for most proportion of the variance of the original hyperspectral reflectance dataset [START_REF] Liu | Discrimination of lodged rice based on visible/near-infrared spectroscopy[END_REF] , and retained more information than the broad-band and narrow-band spectral indices, and then PCs had the higher overall accuracy and Kappa coefficient than spectral indices.

The results indicated that hyperspectral remote sensing with PCA and ANNs has considerable potential in discriminating the lodging rice from normal ones as a supplementary and even alternative technique against spectral indices at the regional and even large spatial scales. However, there was only two kinds of stress status (i.e., healthy and lodging) in our research. Different lodging angles bring about different damage severity in practice. Future study is needed to include more stress status and extrapolate from ground to airborne and spaceborne platforms.s.

Table 2 .

 2 Average canopy hyperspectral reflectance of rice crops at six wavelength intervals representing the Enhanced Thematic Mapper Plus (ETM+) of Landsat-7 (Unit: %) Reflectance values within a row followed by the same letter are not significantly different from each other by Duncan test at α = 0.05.

	Spectrum range	Site 1 (rice brown planthopper)	Site 2 (rice panicle blast)
	(nm)	Healthy	Lodged	Healthy(n=1	Lodged
		(n=35)	(n=35)	2)	(n=35)
	Blue-Green
	(450~515)	5.9c	10.4a	3.3d	7.6b
	Green (525~605)	9.6d	16.4b	10.9c	17.2a
	Red (630~690)	11.4b	20.5a	8.2c	18.1a
	NIR (775~900)	23.3c	37.7b	45.7a	46.7a
	SWIR 1 (1550~175
	0)	22.0b	36.2a	20.5c	41.6a
	SWIR 2
	(2090~2350)	14.2c	25.0b	13.4c	35.3a
	*:

Table 3

 3 Comparison of the two accuracy measures for VIs and PCA based on PNN (Unit: %)

	Dataset types	Training dataset	Testing dataset 1	Testing dataset 2
	Input vectors	OA	KC	OA	KC	OA	KC
	DVI	96.2%	0.923	88.9%	0.778	90.9%	0.814
	RVI	100%	1	61.1%	0.222	63.6%	0.214
	NDVI	88.5%	0.769	72.2%	0.444	63.6%	0.214
	SAVI L=0.5	94.2%	0.885	55.6%	0.111	68.2%	0.319
	OSAVI	94.2%	0.885	66.7%	0.333	72.7%	0.421
	MSAVI 2	96.2%	0.923	61.1%	0.222	63.6%	0.214
	TVI	90.4%	0.808	66.7%	0.333	63.6%	0.214
	IPVI	94.2%	0.885	66.7%	0.333	63.6%	0.214
	TDVI	100%	1	77.8%	0.556	63.6%	0.214
	RDVI	94.2%	0.885	66.7%	0.333	59.1%	0.108
	EVI	94.2%	0.885	66.7%	0.333	72.7%	0.421
	GEMI	94.2%	0.885	44.4%	-0.111	72.7%	0.421
	BI	100%	1	100%	1	77.3%	0.56
	GVI	86.5%	0.731	88.9%	0.778	81.8%	0.621
	WI	94.2%	0.885	88.9%	0.778	81.8%	0.621
	NDWI	92.3%	0.846	44.4%	-0.111	63.6%	0.214
	LSWI	94.2%	0.885	33.3%	-0.333	68.2%	0.319
	NMDI	84.6%	0.692	72.2%	0.444	27.3%	-0.467
	2 PCs	100%	1	100%	1	95.5%	0.909
	* OA and KC denoted the overall accuracy and Kappa coefficient, respectively.

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).


Xuechun Wang

Shishun Tao

Mingde Hao

Wei Li

Sustainable fertilizer level for winter wheat in different rainfall regions on the Loess Plateau of China

Keywords: Words:The loess plateau, Winter wheat, Fertilizer, EPIC model

Higher fertilization on winter wheat increased the fluctuation of winter wheat yield in different rainfall years and impacted the sustainable development of winter wheat production on the Loess Plateau.

Based on the long term field experimental data at Chagnwu Agricultural Station, this paper evaluated the EPIC model. And this paper also suggested a sustainable fertilizer level for winter wheat, based on the analysis of simulation results in different rainfall regions. Results of this study indicated that: 1) The EPIC model simulated both winter wheat yields and soil water among different fertilizer levels well, with the mean R value of 0.91 and 0.89 respectively. 2) With the increasing of fertilizer, the value of IRFG (Increase Rate of Grain yield by Fertilizer) and WUEG (Water Using Rfficiency for Grain yield) became higher, when soil water in deep soil was not be used excessively; however, the value of IRFG became lower, when soil water in deep soil was used excessively. 3) In the semi-humid region, fertilizer for winter wheat should be from N 4 to N 5 ; in the semi-humid and drought-prone region and in the semi-arid region, it should be from N 3 to N 4 ; in the semi-arid and drought-prone region, it should be lower than N 3 .

Introduction

Winter wheat is a major food and feed grain crop in the world. It occupies a large area (82%) of the Loess Plateau rain-fed region of china [START_REF] Zhu | Soil and Agriculture in the Loess Plateau[END_REF]. Wheat is mostly grown under dry land conditions, therefore, its growth, development and yield depended mainly on available water and fertilizer. An increase in fertilization can stimulate deeper rooting of winter wheat, making a greater quantity of stored soil-water available to the plant, thereby reducing potential water stress and harvesting more yields [START_REF] Brown | Water use and soil-water depletion by dryland winter wheat as affected by nitrogen fertilization[END_REF][START_REF] Read | Factors affecting fertilizer nitrogen response of wheat insouthwestern Saskatchewan[END_REF]. With the spreading and applying of fertilizer, winter wheat yield on the Loess Plateau had increased from an average of 1696 kg/hm 2 for the period of 1980-1985 to 3438 kg/hm 2 for 1986-2010. However, larger above ground biomass and transpirational leaf area, stimulated by increased fertilizer, results in greater transpiration demands and amount of water loss from the crop canopy [START_REF] Ritchie | Soil and plant factors affecting evaporation[END_REF]. Therefore, this increased productivity had increase soil water depletion and reduced available soil water in deep soil [START_REF] Huang | Effect of increased fertilizer applications to wheat crop on soil-water depletion in the Loess Plateau, China[END_REF]. Excessive consumption of soil water has become the key reason for soil desiccation and yield fluctuation in high-yield land farm [START_REF] Li | Fluctuation of yield on high-yield field and desiccation of the soil on dryland[END_REF]. It is urgently need to determine a sustainable fertilizer level for this region as well as the similar region in the world.

To carry out an experiment in different regions and in a long period may found out an answer for the sustainable fertilizer level in different rainfall regions. However, it is a long-term endeavor that is both expensive and time-consuming. An alternative approach is to use computer model to simulate soil water content and crop yield under different fertilizer levels in different rainfall regions based on local situations (soil, crop and climate etcetera). Several models have been developed to simulate soil water and crop yield [START_REF] Zhang | WAVES-An Integrated Energy and Water Balance Model[END_REF][START_REF] Van Genuchten | A Numerical Model for Water and Solute Movement in and Below the Root Zone[END_REF][START_REF] Jones | The DSSAT cropping system model[END_REF]. One such model is the EPIC model that simulates the soil water and crop yield simultaneously with the help of its two sub-models (growth model and hydrology model) [START_REF] Williams | A modeling approach to determining the relationship between erosion and soil productivity[END_REF]. Wang and Li [START_REF] Wang | Evaluation of crop yield and soil water estimates using the EPIC model for the Loess Plateau of China[END_REF] evaluated EPIC model for crop yield and soil water content among different cropping systems (spring maize, winter wheat and alfalfa) on the Loess Plateau. They found that EPIC model estimate soil water and crop yield well with the new database built up for the Loess Plateau.

The primary objective of this study was to determine a sustainable fertilizer level for winter wheat in different rainfall regions on the Loess Plateau. The secondary objective was to evaluate EPIC model for crop yield and soil water among different fertilizer levels, using a long-term experimental data at Changwu Agricultural Station.

Materials and methods

Field experiment

The field experiment was carried out at Changwu Agricultural Station from 1985 to 2000. It consisted of three fertilization treatments (table 1) with three replications in 9 plots of 10.26×6.5 m (with a buffer zone of 1m between plots). Plots were arranged as a randomized complete block design.

All fertilizers were mixed and applied at sowing, and winter wheat was sown at the rate of 19.5 kg/hm 2 , using a no-till disk drill with the row space of 0.25 m. For grain yield determination, the plots were harvested manually. Soil samples were taken by core break method [START_REF] Bennie | An assessment of the core-break method for estimating root density of different crops in the field[END_REF] in 0.1 m layers to the depth of 3 m soil. Soil water content was measured (gravimetrically) for each soil sample by the oven-drying method [START_REF] Blake | Bulk density[END_REF].

EPIC model

EPIC is a widely tested and adopted process-based agro-ecological model originally built to quantify the effects of soil erosion on productivity [START_REF] Williams | A modeling approach to determining the relationship between erosion and soil productivity[END_REF][START_REF] Williams | The EPIC crop growth model[END_REF]. Currently the model has evolved into a comprehensive model capable of simulating photosynthesis, evapo-transpiration and other major plant and soil processes [START_REF] Williams | The EPIC model[END_REF]. The model runs on a daily time step and needs daily weather data as well as information on soil properties, specific crop growth parameters and farm management practices [START_REF] Robert | Sensitivity of crop yield and water use to change in a range of climatic factors and CO, concentrations: a simulation study applying EPIC to the central USA[END_REF].

Based on crop parameters and other related parameters, the EPIC model can calculate the uptakes of soil water and nutrients by crop, estimate the impacts of temperature, water, nutrients (N, P and k), air and salt stresses on crop biomass accumulation and crop yield by daily step [START_REF] Niu | Reliability and input-data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the[END_REF]. For soil water, the EPIC model contains algorithms that allow for a description of the hydrological balance at the small watershed [START_REF] Izaurralde | Simulating soil C dynamics with EPIC: Model description and testing against long-term data[END_REF]. Calculated hydrological processes include snowmelt, surface runoff, infiltration, soil water content, percolation, lateral flow, water table change, and evapo-transpiration at a daily time step, details was given out by Sharply and Williams [START_REF] Williams | The EPIC Model[END_REF] and Williams et al. [START_REF] Williams | The EPIC model[END_REF].

Methods

Evaluation of the EPIC model

In this study, six statistical values were used to evaluate the model performance as followings.

Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Relative Error (RE), Model Efficiency (ME), Correlation Coefficient (R), Determination Coefficient (R 2 ), detailed information for these equations can found in Wang and Li [START_REF] Wang | Evaluation of crop yield and soil water estimates using the EPIC model for the Loess Plateau of China[END_REF] and Ko et al. [START_REF] Ko | Using EPIC model to manage irrigated cotton and maize[END_REF].

Calculation for WUEG and FUEG

Water-use efficiency was calculated as equation 1 [START_REF] Hussain | Effect of irrigation and nitrogen on water use efficiency of wheat in Saudi Arabia[END_REF]:

ET GY WUEG  (1)
Where WUEG was water-use efficiency for the grain yield, GY was the grain yield and ET was the cumulative evapo-transpiration over the growing season which was calculated using the water balance equation (equation 2):

S D R I P D ) ( ) C ( ET       (2)
Where P was precipitation, I was irrigation, C was upward flow into the root zone, R was surface runoff, and D was downward drainage out of the root zone. DS was the change of available soil water in winter wheat field, which was calculated by equation 3.

ASH ASP DS   (3)

Where ASP was available soil water in 0-7 m soil when winter wheat was sown, ASH was available soil water in 0-7 m soil when winter wheat was harvested.

Since the experimental field was terraced, and located in the Loess Plateau, surface runoff was ignored. The groundwater table was very low, so the upward flow into the root zone and the downward drainage out of the root zone were negligible. Consequently, the soil water balance equation was reduced to equation 4.

) ( ASH ASP P ET    (4)
Increase rate of grain yield for different fertilizer level was calculated by equation 5, 6 and 7.

DN DG IRFG 

(5)

1 - m m GN GN DG   , 6≥m≥1 (6)
1 - m m N N DN   , 6≥m≥1 (7)
Where DG was increased yield from fertilizer level N m-1 to fertilizer N m , DN was increased fertilizer of N from fertilizer level N m-1 to fertilizer N m ; GN m was grain yield of winter wheat under fertilizer level of N m , GN m-1 was grain yield of winter wheat under fertilizer level of N m-1 ; N m was amount of N in fertilizer level of N m , N m-1 was amount of N in fertilizer level of N m-1 .

Description of the development of dry soil layer

In order to compare the development of dry soil layer among different fertilizer levels and different regions, parameters as following were considered in this study. 1) SMDDT Period, a Period in which soil water decreased and dry soil layers thickened continuously; 2) SSMD Year, a year in which steady dry soil layer built; 3) Max DSL, Maximum depth of dry soil layer occurred; 4) SSL Range, a depth range in which soil water content was stable after steady dry soil layer built; 5) SMC Range, a depth range in which soil water content was unstable after steady dry soil layer built; 6) SWU Depth, Maximum depth in which soil water was used; 7) DSLD Range, a depth range in which dry Soil Layers occurred; 8) SD Speed, dry soil layer building speed.

Parameters of SMDDT Period and SSMD Year described the time needed to building dry soil layer; Max DSL and DSLD Range presented the distribution depth of dry soil layer; SSL Range and SMC Range told us the stability of dry soil layer; SD speed indicated the building speed of dry soil layer.

Statistical method

Analysis of variance (ANOVA) was used to test the difference in grain yield, water-use efficiency and fertilizer-use efficiency among different treatments and different regions. Mean comparisons were made by the LSD (the least significant difference) method with P < 0.05 and P<0.01 respectively. The analyses were conducted using the SPSS program [22].

Design for the simulation

Mean annual rainfall at Luochuan and Changwu were 622 mm and 584 mm respectively; at Yan'an and Shouyang were 535 mm and 455 mm respectively. In this study, Luochuan and Yan'an were selected as the representation of semi-humid region and semi-arid region respectively; Changwu as the representation of semi-humid and drought-prone region; Shouyang as the semi-arid and drought-prone region on the Loess Plateau. Fertilizer treatments for each regions consisted of 7 fertilizer treatments as table 2. 3). Comparing with that for NP, winter wheat yield was simulated better with a higher value of R 2 for CK and N (figure 1).

Soil water

Measured mean available soil water in 0-3 m soil were 120, 96 and 85mm for CK, N and NP respectively; simulated mean value were 113, 97 and 92 mm respectively. The value of mean available soil water estimated by EPIC model was slightly higher than that of simulated for N and NP, and slightly lower for CK (table 4). Paired-t test indicated the difference between simulated and measured available soil water in 0-2 m soil for each treatment was not significant with the P value of 0.05. EPIC model estimated mean annual available soil water well, with the R vale of 0.90, 0.96 and 0.81 for CK, N and NP respectively.

Tab. [START_REF] Ritchie | Soil and plant factors affecting evaporation[END_REF] 4). Comparing with that for NP, available soil water in 0-3 m soil was simulated better with a higher value of R 2 for CK and N (figure 2).

Simulation results of winter wheat yield and soil water

Winter wheat yield

The difference of mean annual winter wheat yield was highly significant (P=0.01) in different rainfall regions (table 5). Mean value of annual winter wheat yield were 2.77, 2.70, 1.97 and 1.00 t/hm 2 , with standard deviation of 1.52, 1.59, 1.40 and 0.84 t/hm 2 . With the increase amount of precipitation, more winter wheat yield was founded at Luochuan and Changwu.

The difference of winter wheat yield among N 0 , N 1 , N 2 , and N 3 fertilizer level was significant (table 5). The difference of winter wheat yield between N 5 and N 6 fertilizer level was not significant. Before N3 fertilizer level, winter wheat yield increased with the increase amount of fertilizer application; after N4 fertilizer level, the impact of fertilizer on winter wheat was different in different rainfall regions (table 5). The correlation between winter wheat yield and precipitation over growing season was highly significant (P=0.01); and it was found significant (P=0.05) between winter wheat yield and precipitation over growing year (table 6). Table 6 showed that winter wheat yield was highly significant correlated with the available soil water in 0-3 m soil before sowing. This indicated that available soil water in 0-3 m soil before sowing and precipitation in growth period was the key impact factors to influence winter wheat yield on the Loess Plateau. Since the correlation index between winter wheat yield and available soil water in 0-7 m soil before sowing was significant (P=0.05), soil water in 0-7 m soil should be considered when to determine a sustainable fertilizer level for the winter wheat on the Loess Plateau. The value of DS in different rainfall regions was different significantly. Table 7 showed more soil water decreased at Changwu and Yan'an, with the mean DS value of 113 and 114mm respectively.

Comparing with that in Shouyang, more precipitation and less DS value was founded at Changwu and Yan'an. Analysis of the winter wheat growth indicated the leaf area index (LAI) of winter wheat at Shouyang was significantly less than that at Changwu and Yan'an. It means that less trans-evaporation at Shouyang, comparing with that at Changwu and Yan'an. A highest value of LAI and precipitation was found at Luochuan. Though higher trans-evaporation (indicated by the high value of LAI) took place and may be decreased soil water, more precipitation added more water to the soil and decreased the DS value at Luochuan.

Comparing with N 1 , N 2 and N 3 fertilizer level, a higher value of DS was founded in N 4 , N 5 and N 6 fertilizer level. This indicated that more soil water decreased under the higher fertilizer level in winter wheat field on the Loess Plateau. 9).

From N 1 to N 4 fertilizer level, the value of IRFG increased with the increase of fertilizer applied to the winter wheat field (table 9). From N 4 to N 5 fertilizer level, a contrary changing trend was founded for the value of IRFG. The value of IRFG became lower, when the fertilizer was more than 180 kg/hm 2 . Among different rainfall regions, the value of IRFG was the highest at Luochuan, it was the second at Changwu, and the lowest value was at Shouyang (table 9). Annual rainfall was one of the key factors to influence fertilizer using efficiency on the Loess Plateau of China.

Discussions

Fertilizer, dry soil layer and rainfall

Nitrogen fertilizer stimulated the winter wheat root to develop into the deeper soil, and using more deep soil water [START_REF] Nielsen | Nitrogen fertility influence on water stress and yield of winter wheat[END_REF]. However, once dry soil layer built in deep soil, it was difficult to recover on the Loess Plateau [START_REF] Wang | Water restoration of dry soil layers in the Loess Plateau and crop yield response[END_REF]. Therefore, the use of high fertilization rates has been found to increase the thickness of dry soil layer [START_REF] Wang | Simulation of yield and soil desiccation effects of continuous spring maize in different precipitation areas of the Loess Plateau[END_REF] and resulted the fluctuation of winter wheat yield in different rainfall years on the Loess Plateau [START_REF] Li | Fluctuation of yield on high-yield field and desiccation of the soil on dryland[END_REF]. This paper showed that more soil water in deep soil depleted and the SD speed (soil desiccation speed) increased, with the increasing application of fertilizer for winter wheat.

The amount of annual rainfall impacted the develop speed of dry soil layers in different regions.

Comparing with Luochuan and Changwu, higher value of SD speed was founded at Yan'an and Shouyang.

Fertilizer, WUEG and IRFG

The increasing use of deep soil water during critical crop development stages increased the value of WUEG (water use efficiency for grain yield) for high fertilizer winter wheat [START_REF] Huang | Effect of increased fertilizer applications to wheat crop on soil-water depletion in the Loess Plateau, China[END_REF]. Various study found that water and fertilizer related and impacted each other [START_REF] Mitchell | Overview of long-term agronomic research[END_REF][START_REF] Sandor | Significance of ancient agriculture soils for long-term agronomic studies and sustainable agriculture research[END_REF]. This study indicated higher value of IRFG and WUEG in higher fertilizer level field, when no dry soil layer build in deep soil; while lower IRFG value in higher fertilizer level field, when dry soil layers had been built in deep soil.

Sustainable fertilizer level for winter wheat field

At Luochuan, the difference of winter wheat yield for N 5 and N 6 fertilizer level was not significant;

there was not significant difference between winter wheat yield for N 5 and N 6 at Changwu and Yan'an, and also for N 4 and N 5 fertilizer level was not significant; At Shouyang, there was no significant difference between winter wheat yield for N 5 and N 6 ,N 4 and N 5 , N 3 and N 4 . These statistical results indicated that fertilizer level should not be higher than N 5 , N 4 , N 4 and N 3 , at Luochuan, Changwu, Yan'an and Shouyang respectively.

When dry soil layers develop to more than 5 m depth, it will become more difficult to recover [START_REF] Wang | Sustainable recovery of soil desiccation in semi-humid region on the Loess Plateau[END_REF] on the Loess Plateau of China. Table 8 showed that the depth of dry soil layer was deeper than 5m for N 4 , N 5 and N 6 at Luochuan and Changwu; for N 3 , N 4 , N 5 and N 6 at Yan'an and Shouyang. Therefore, considering on the recovery of dry soil layers, fertilizer level for winter wheat should not be higher than N 4 at Luochuan and Shouyan; and it should not be higher than N 3 at Yan'an and Shouyang. Table 9 indicated the value of IRFG became lower when the fertilizer level was higher than N 4 for winter wheat field, either at Luochuan and Changwu or at Yan'an and Shouyang. This result indicated the fertilizer using efficiency decreased when fertilizer level was higher than N 4 for winter wheat field.

Considering on the sustainable production of winter wheat and the sustainable using of soil water, the fertilizer level for the different rainfall regions on the Loess Plateau should as followed. 1) In the semi-humid region (Luochuan), fertilizer level should be from N 4 to N 5 ; 2) in the semi-humid and drought-prone region (Changwu) and in the semi-arid region (Yan'an), it should be from N 3 to N 4 ; 3) in the semi-arid and drought-prone region (Shouyang), it should be lower than N 3 .

Conclusions

The EPIC model simulated the variance of winter wheat yield among different fertilizer levels well, with the mean value of 0.91; and it estimated mean annual available soil water well, with the mean R vale of 0.89, on the Loess Plateau of China.

Soil desiccation speed increased with the increasing of fertilizer applied to winter wheat field; and it decreased with the increasing of annual rainfall in different rainfall regions on the Loess Plateau of China.

With the increase application of fertilizer, the value of IRFG and WUEG became higher, when soil water in deep soil was not be used excessively; the value of IRFG became lower, when soil water in deep soil was used excessively.

Sustainable fertilizer levels for winter wheat on the Loess Plateau were different for different rainfall regions. 1) In the semi-humid region (Luochuan), it should be from N 4 to N 5 ; 2) in the semi-humid and drought-prone region (Changwu) and in the semi-arid region (Yan'an), it should be from N 3 to N 4 ; 3) in the semi-arid and drought-prone region (Shouyang), it should be lower than N 3 .

Fig. 1

 1 Fig.1 Comparison of simulated and measured winter wheat yield for different fertilizer levels at Changwu Agricultural Station

Fig. 2

 2 Fig. 2 Comparison of simulated and measured annual mean available soil water in 0-3m soil for different fertilizer levels at Changwu Agricultural Station

Table 1

 1 Fertilizer treatments for the winter wheat at Changwu Agricultural Station from 1985 to 2000

	Treatments	N (kg/hm 2 )	P 2 O 5 (kg/hm 2 )
	CK	0	0
	N	120
	NP	120	60

Table 2

 2 Fertilizer treatments for winter wheat in different rainfall regions on the Loess Plateau of China First, we input soil data, crop data, meteorological data and management data into the EPIC model for each selected region. Second we evaluate the EPIC model using the long-term experimental data at Changwu Agricultural Station. Third we run the EPIC model from 1961 to 2000, and out put the simulated winter wheat yield year by year and soil water day by day. At last we analyzed the fluctuation of crop yield and soil water during a long period, based on the simulation results.Considering the sustainable using of soil water and production of winter wheat, we pointed out a sustainable fertilizer level for different rainfall regions on the Loess Plateau.

	Fertilizer	Luochuan (kg/hm 2 ) Changwu (kg/hm 2 ) Yan'an (kg/hm 2 ) Shouyang (kg/hm 2 )
	level	N	P	N	P	N	P	N	P
	N 0	0	0	0	0	0	0	0	0
	N 1	90	45	90	45	90	45	90	45
	N 2	120	60	120	60	120	60	120	60
	N 3	150	75	150	75	150	75	150	75
	N 4	180	90	180	90	180	90	180	90
	N 5	210	105	210	105	210	105	210	105
	N 6	240	120	240	120	240	120	240	120

results of the EPIC model 2.1.1 Winter wheat yield

	Agricultural Station
		Annual mean yield(t/hm 2 )	RE	R	RMSE	RRMSE
		Measured	Simulated	(%)		(t/hm 2 )	(%)
	CK	1.40	1.46	4.4	0.91**	0.41	29
	N	2.28	2.30	1.2	0.93**	0.53	23
	NP	2.98	2.77	-7.1	0.89**	0.86	28
		Little difference between ME and R

Measured mean annual winter wheat yield were 1.40, 2.28 and 2.98 t/hm 2 ; Simulated mean annual winter yield were 1.46, 2.30 and 2.77 t/hm 2 for CK, N and NP respectively. Table

3

showed that simulated winter wheat yield were slightly higher for CK and N treatment and were slightly lower for NP treatment, comparing with measured yield. Paired-t test demonstrated the difference between simulated and measured winter wheat yield for each treatment was not significant with P=0.05. EPIC model simulated mean annual winter wheat yield well, with the R vale of 0.91, 0.93 and 0.89 for CK, N and NP respectively.

Tab. 3 Comparison of simulated and measured winter wheat yield for different fertilizer levels at Changwu 2 value (figure

1

) presented the variance of winter wheat yield in different years was simulated well by EPIC model with the RRMSE value of 29 %, 23 % and 28 % respectively (table

  Comparison of simulated and measured available soil water in 0-3m soil for different fertilizer levels at Little difference between ME and R 2 value (figure 2) showed the variance of available soil water in different years for each fertilizer level was simulated well by EPIC model with the RRMSE value of 19%, 17% and 23% respectively (table

	Changwu Agricultural Station
		Annual available soil water (mm)	RE	R	RMSE	RRMSE
		Measured	Simulated	(%)		(mm)	(%)
	CK	120	113	-5.8	0.90	22.38	19
	N	96	97	1.0	0.96	16.44	17
	NP	85	92	8.2	0.81	18.37	23

Table 5

 5 Difference of winter wheat yield among different fertilizer levels in different rainfall regions on the Loess

	Plateau of China
	Fertilizer level	Luochuan	Changwu	Yan'an	Shouyang
	N 0	1.20 a	1.44 a	0.96 a	0.53 a
	N 1	1.83 b	1.94 b	1.36 b	0.69 b
	N 2	2.53 c	2.45 c	1.76 c	0.90 c
	N 3	3.00 d	2.87 d	2.08 d	1.07 d
	N 4	3.44 e	3.24 e	2.43 e	1.22 de
	N 5	3.59 f	3.38 ef	2.54 ef	1.25 ef
	N 6	3.78 f	3.56 f	2.67 f	1.33 f

Table 6

 6 Winter wheat yield, precipitation and available soil water in different rainfall regions on the Loess Plateau With the increase amount of precipitation, more available soil water was founded in winter wheat field at luochuan and Changwu. Mean value of monthly available soil water in 0-7 m soil were 740, 558, 512 and 412 mm, with standard deviation of 102, 140, 137 and 99 mm, at luochuan, Changwu, Yan'an and Shouyang respectively. Considering among different rainfall regions, precipitation was one of the key factors to impact the available soil water in winter wheat field.

	of China
	Fainfall	Mean annual		Correlation index between yield and
	region	yield (t/hm 2 )	Precipitation over (mm)	Available soil water before sowing in (mm)
			Growing season	Growing year	0-3 m soil	0-7 m soil
	Luochuan	2.77 a	0.66**	0.42*	0.81**	0.57*
	Changwu	2.70 b	0.63**	0.38*	0.78**	0.60*
	YanAn	1.97 c	0.80**	0.43*	0.80**	0.70*
	Shouyang	1.00 d	0.51**	0.34*	0.79**	0.68*
	Growing season of winter wheat was from September to June; growing year was from September to August.
	2.2.2 Soil water
	2.2.2.1 Available soil water in 0-7 m soil

Table 7

 7 Mean value of decreased available soil water from winter wheat was sowed to winter wheat was harvested Fertilizer and rainfall were two key factors to effect the development of dry soil layer in winter wheat field on the Loess Plateau of China. Table5indicated that higher fertilizer level increased the winter wheat yield; at the same time increase the SD speed and the thickness of dry soil layer (table 8).Highest value of SD speed and highest SSL range were founded in N6 fertilizer level at four selected regions. With the increase amount of fertilizer applied to winter wheat, the value of SD and Max DSL increased (table8). With the increasing amount of fertilizer level, more soil water was depleted and soil desiccation degree increased. More rainfall resulted in lower SD speed and SSL range in Luochuan and Changwu, comparing with that in Shouyang and Yan'an.

		N 0	N 1	N 2	N 3	N 4	N 5	N 6
	Luochuan (mm)	61	71	78	82	91	93	94
	Changwu (mm)	85	99	109	116	125	126	130
	Yan'an (mm)	86	100	111	111	127	128	131
	Shouyang (mm)	76	86	93	99	104	106	111
	2.2.2.2 Development of dry soil layer in 0-7m soil

Table . 8

 . Statistical value of soil water distribution in 0-7 m soil in winter wheat field under different fertilizer levels in different rainfall regions on the Loess Plateau

		N6	1960~1965	1968	5	1~5	2~5	0~2	0.63	>5
	Shouyang	N0	1960~1966	1966	3	1~3	2~3	0~1	0.5	2~3
		N1	1960~1965	1966	3	1~3	2~3	0~2	0.5	2~3
		N2	1960~1965	1968	4	1~4	2~4	0~2	0.5	>4
		N3	1960~1965	1967	4	1~4	2~4	0~2	0.57	>5
		N4	1960~1965	1968	5	1~5	2~5	0~2	0.63	>5
		N5	1960~1965	1967	5	1~5	2~5	0~2	0.71	>5
		N6	1960~1965	1967	5	1~5	2~5	0~1	0.71	>5
	2.2.3 WUEG and IRFG
	Rainfall region	treat	SMDDT	SSMD	Max	DSLD	SSL	SMC	SD	SWU
		ment	Period	Year	DSL	Range	Range	Range	Speed	Depth
					(m)	(m)	(m)	(m)	(m/a)	(m)
	Luochuan	N0	1960~1968	1968	3	1~3	2~3	0~1	0.38	2~3
		N1	1960~1968	1969	3	1~3	2~3	0~2	0.33	2~3
		N2	1960~1967	1971	4	1~4	2~4	0~2	0.36	>4
		N3	1960~1967	1970	4	1~4	2~4	0~2	0.4	>4
		N4	1960~1969	1971	5	1~5	2~5	0~2	0.42	>5
		N5	1960~1968	1971	5	1~5	2~5	0~2	0.45	>5
		N6	1960~1968	1970	5	1~5	2~5	0~1	0.5	>5
	Changwu	N0	1960~1968	1969	3	1~3	2~3	0~2	0.33	2~3
		N1	1960~1967	1968	3	1~3	2~3	0~2	0.38	2~3
		N2	1960~1967	1971	4	1~4	2~4	0~2	0.36	>4
		N3	1960~1968	1970	4	1~4	2~4	0~2	0.4	>4
		N4	1960~1966	1972	5	1~5	2~5	0~2	0.45	>5
		N5	1960~1966	1971	5	1~5	2~5	0~1	0.45	>5
		N6	1960~1966	1970	5	1~5	2~5	0~1	0.5	>5
	Yan'an	N0	1960~1966	1967	3	1~3	2~3	0~2	0.43	2~3
		N1	1960~1966	1967	3	1~3	2~3	0~2	0.43	2~3
		N2	1960~1966	1969	4	1~4	2~4	0~2	0.44	>4
		N3	1960~1965	1968	4	1~4	2~4	0~2	0.5	>5
		N4	1960~1965	1969	5	1~5	2~5	0~2	0.56	>5
		N5	1960~1965	1968	5	1~5	2~5	0~1	0.63	>5

Table 9

 9 showed the value of WUEG increased, with increase application of fertilizer to winter wheat field. Mean value of WUEG among different regions for N 0 , N 1 , N 3 , N 2 , N 4 , N 5 and N 6 fertilizer level were 2.76, 3.80, 4.83, 5.57, 6.32, 6.59 and 6.92 t/(hm 2 • mm) respectively. Among different rainfall regions, WUEG increased with the increasing of annual rainfall at Luochuan, Changwu, Yan'an and Shouyang (table

Table 9

 9 Comparison of WUEG among different fertilizer levels and different rainfall regions on the Loess Plateau of China

	Rainfall regions	items	N 0	N 1	N 2	N 3	N 4	N 5	N 6
	Luochuan	WUEG (t/(hm 2 • mm)) IRFG (t/( hm 2 • kg))	3.14 --	4.73 0.70	6.37 1.11	7.22 1.20	8.38 1.24	8.76 1.14	9.19 1.08
	Changwu	WUEG (t/( hm 2 • mm)) IRFG (t/( hm 2 • kg))	3.53 --	4.61 0.56	5.63 0.84	6.50 0.95	7.24 1.00	7.55 0.92	7.93 0.88
	Yan'an	WUEG (t/( hm 2 • mm)) IRFG (t/( hm 2 • kg))	2.67 --	3.66 0.43	4.58 0.67	5.33 0.75	6.07 0.82	6.35 0.75	6.66 0.71
	Shouyang	WUEG(t/( hm 2 • mm)) IRFG (t/( hm 2 • kg))	1.71 --	2.20 0.18	2.73 0.30	3.23 0.36	3.58 0.38	3.70 0.34	3.91 0.33

Acknowledgment

This study was sponsored by the Knowledge Innovation Program of Chinese Academy of Sciences (KZCX2-YW-JC408), sub research item (2009CB118604) of the National Basic Research Program of China (973 Program) and the Key Projects in the National Science &

Technology Pillar Program (2011BAD31B01). We are grateful to Williams J.R. with whom we had many discussions about the EPIC model. We want to give our thanks to the students who had done a great work for the field experiment at Chanwu Agricultural Station. We also want to give our thanks to two anonymous reviewers for their helpful comments on the earlier version of this paper.