Siquan Hu
email: husiquan@ustb.edu.cn

Haiou Wang

Chundong She

Junfeng Wang

AgOnt: Ontology for Agriculture Internet of Things

Keywords: Ontology for Agriculture Internet of Things Agriculture Internet of Things, Ontology, Semantics

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

Introduction

Recent advances in networking, sensor and RFID technologies allow connecting various physical world objects to the IT infrastructure, which could, ultimately, enable realization of the Internet of Things (IOT) and the Ubiquitous Computing visions [START_REF] Katasonov | Smart Semantic Middleware for The Internet Of Things[END_REF] [2] [START_REF] Brock | On the Semantic Web of Things[END_REF]. IOT has great potential in Agriculture. Consider the future vision of the food lifecycles are well recorded from seeds, cultivation, products, transportation, food processing, sales in supermarket, it is exciting to have public confidence on food security and tremendous additional value to the agriculture and food suppliers. However, in the agriculture internet of things, the data are sourced from different organizations and information technology facilities; it is an enormous challenge to integrate them into a workable system so that the midstream firms and the end consumers can query the history of the agricultural products unhindered by the bounds of the previous venders. One of the most important requirements of such integration is that the data semantics are consistence among the different phase of the products. To achieve such target, a unified ontology of agriculture products should be utilized by all the information systems of the different phases. Ontology is a new concept that is emerging from the various Semantic Web initiatives, which roughly speaking can be defined as a semantic system that contains terms, the definitions of those terms, and the specification of relationships among those terms.

In this paper, we proposed AgOnt -agriculture ontology for the purpose of agriculture internet of things. To keep the ontology light-weighted, we ignore the complexity of the specific agriculture activities or food processing; only the environments of the agriculture products are paid close attention to so that all the history can be queried by the follow-ups users. The remainder of the paper is organized as follows: section 2 presents the design of proposed AgOnt ontology. In section 3 the query of AgOnt based knowledge base is discussed. Section 4 highlights existing related work briefly. Section 5 concludes the current work and discusses possible future avenues for this research.

AgOnt Ontology

Ontology gives formal description on the hierarchical categories for real world knowledge [START_REF] Henson | An Ontological Representation of Time Series Observations on the Semantic Sensor Web[END_REF], [START_REF] Kuhn | A Functional Ontology of Observation and Measurement[END_REF], [START_REF] Fensel | Enabling Semantic Web Services[END_REF]. Our approach uses ontology to capture the semantics of agriculture grains and their cultivation, logistics, storage history in the application of agriculture IOT. The purpose of building this ontology is to provide a mechanism for semantic interoperation between different systems (clouds) in the global agriculture clouds computing system. The semantic integration based on the ontology provides a solid basis for the integration of heterogeneous agriculture information system to form a huge information platform to record the grain lifecycles from the seeds, plant cultivation to food consumption. AgOnt is based on the IEEE Suggested Upper Merged Ontology (SUMO) [START_REF] Niles | Towards a Standard Upper Ontology[END_REF], which is the largest formal public ontology in existence today and used widely for research and applications in search, linguistics and reasoning. To capture the semantics of the grain or food lifecycle terminologies, we not only are able to define the environment of a product, but also describe the recall relationship between the succeeding forms such as the seeds and the plant, the wheat and the ponder, etc.

2.1.

Structure of the Ontology

We create AgOnt ontology on the basis of two kinds of relationships in the lifecycle of agriculture products. One is the relationship of a product and its properties such as location, timestamp, environments parameters, processing status, etc. The other is the relationship between a product and its source products such as a plant and its seedlings where the plant grows from. Currently, we have defined 3 relationships between entities in our ontology generation: Is-a, Has-property, and Source-from:  Is-a: entity A is an instance of entity B.  Has-property: entity A has a property B.  Source-from: entity A sources from entity B.

To describe and maintain the knowledge cleanly, we identify 5 main primitive domain classes as the top level ontology of the AgOnt as in Fig. 1.

 Product class is used to describe the instance of agriculture products such as seeds, wheat, ponder, etc. It is the core of the whole ontology, which is a reflection of the view of thing-focused in the internet of things.  Phase is a simple class to capture the conception of position in the lifecycle of the agriculture product. Phase is a property of any product.  Time class is a supplement for phase to describe the concrete timestamp of the product activity. It is a property of any product.  Location class is to capture where the product is and what organization is responsible for the maintenance of the agriculture phase  Condition class captures the environment parameters of the product. "Thing" is a dummy root for all classes in the ontology hierarchy. To illustrate the relationship "source-from", Fig. 1 gives an example between "Seed" and "Seedling" to show the grow-up or evolution of agriculture product. It is obvious that "Seed" and "Seedling" are the second level ontologies. The Product class hierarchy is showed in Fig. 2. All agriculture products are dived into 5 subclasses, Seed, Seedling, Plant, Crop and Processed food, with each sources from previous. Crop capture the concept of the product cropped directly from the field. Different from it, Processed food describe the product produced by food processing factories.

Fig. 2. The extended Product ontology

The Phase class describe the abstract activity of the product ignore the specific characteristics. Currently we have 6 kinds of phases showed in Fig. 3.

Fig. 3. The extended Phase ontology

The Condition class captures all the sensor output to log the environment data of the product. Depending on the phase, the conditions of a product may have different properties. These property data are captured by smart sensors and can be propagated into the IOT. Fig. 4 shows a snippet of the conditions.

Querying on Agriculture Knowledge Base based on AgOnt Ontology

The AgOnt ontology provides a logical base where the semantics of the history of an agriculture product. Different agriculture information systems can integrate into a self-consistent knowledge base via a semantic middleware showed in Fig. 5.

AgOnt Ontology Models

Reasoning Server Middleware Query Interface

Fig. 5. Reasoning on the knowledge base

A user can query the abnormal condition history of a product after the base is built based on the ontology. The knowledge base has two kinds of descriptions. One is the classification of the agriculture terminologies and their relationship, the users and machines can reason and analyze the structure association of agriculture concepts. The other is the concrete instances of the products, their conditions and relationship, where the users and machines can judge what the history condition caused an unsatisfied product at the end. For example, assume at some phase a decayed product ABP is found, the user wish to identify where the problem comes from. Depending on the setting of abnormal condition, the user can query the history to find which phase may cause it. Assume the abnormal condition is "the temperature > 4 degC and the humidity > 30%", following query procedure is built to identify the problem.

Set a search depth upper bound H; Searchdepth=1; CurProduct=ABP; do { Select x from products where CurProduct source-from x and x has-Property temperature > 4 degC or x has-Property humidity > 30%; If x is not empty, return x; else { CurProduct =x; searchdepth ++;} } while (searchdepth < H)

Related work.

In the agricultural sector there exist already many well-established controlled vocabularies, such as FAO's AGROVOC Thesaurus [START_REF]AGROVOC Thesaurus[END_REF]. However, to build a semantic tool entirely effective on the Internet, there is a need to re-assess the traditional "thesaurus" approach and move towards to the development of "ontologies". Taking FAO's multilingual thesaurus AGROVOC as a starting point, the AOS Concept Server [START_REF]AGROVOC Concept Server Workbench[END_REF] is a project for such purpose with helping structuring and standardizing agricultural terminology to be used in a wide range of systems in the agricultural domain. The Concept Server will provide a core ontology in the domain of agriculture that people can take as a starting point for building more detailed domain specific ontologies. Although it is a really huge data store in agriculture, the server is more informatics-oriented than food lifecycles tracking.

Xie et al. [START_REF] Xie | Ontology-based Agricultural Knowledge Acquisition and Application[END_REF] presented an agriculture-specific ontology to meet the requirement of agricultural knowledge processing and discussed the method for agricultural knowledge acquisition and representation.

Gangemi et al. [START_REF] Gangemi | A Formal Ontological Framework for Semantic Interoperability in the Fishery Domain[END_REF] aimed at building an ontology in the fishery domain through the conceptual integration and merging of existing fishery terminologies, thesauri, reference tables, and topic trees. The ontology will support semantic interoperability among existing fishery information systems and will enhance information extraction and text marking, envisaging a fishery semantic web.

In summary, above ontologies are not helpful from the view of agriculture IOT application, where more semantic interoperability between grain or food lifecycles is focused. It is necessary to create a specific ontology suitable for the integration of multiple data sources of multiple phases so that all history record can be recalled.

Conclusion and future work

The AgOnt ontology gives a description of the concepts of agriculture terms and lifecycle between seeds, grains, transportations, storage and consumption. According to the classified domain terms and definition based on attribute descriptions in the ontology, a semantic middleware can reason on the food healthy knowledge. By using this proposed approach, the distributed agriculture products' information system can be accessed seamlessly. Thus follow-up users can know the history of the product before making a further processing. And the mechanism can also be used to recall when and where may take responsibility when a problem is found.

The future work is to find mechanisms of managing the agriculture ontology at runtime, to build a user query model to map various user requirements to reasoning procedures, and to sum up the experience of migration from the legacy systems to an ontology-based system.

Fig. 1 .

 1 Fig. 1. The Top Level ontology of AgOnt.

Fig. 4 .

 4 Fig. 4. The extended Condition ontology

Acknowledgement

Project supported by the National High Technology Research and Development Program of China (2008AA01Z208 and 2009AA01Z405), the National Natural Science Foundation of China (60772150), and the Youth Foundation of Sichuan Province (2009-28-419) and the Applied Basic Research Program of Sichuan Province (2010JY0013).


Anxiang Lu

Xiangyang Qin

Jihua Wang
email: wangjh@nercita.org.cn

Jiang Sun

Dazhou Zhu

Ligang Pan

{anxiang Lu

Determination of Cr, Zn, As and Pb in Soil by X-Ray Fluorescence Spectrometry Based on a Partial Least Square Regression Model

Keywords: Heavy metal, Soil, Partial least square regression, X-ray fluorescence spectrometry

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

Introduction 1

Contamination by metals in the soil has become widespread in a global context. Wastewater irrigation, solid waste disposal, sludge applications, vehicular exhaust and industrial activities are the major sources of soil contamination with heavy metals. Increasing metal pollution has severely disturbed the natural ecosystem and harmed human health through food chain. [START_REF] Cai | Food Chain transfer of cadmium an lead to cattle in a lead-zinc smelter in Guizhou, China[END_REF][START_REF] Liu | Metal toxicology[END_REF] Numerous programs have been conducted to monitor heavy metal in soils by governments or institutions all over the world. [START_REF] Tan | Spatial prediction of heavy metal pollution for soils in periurban Beijing, China based on fuzzy set theory[END_REF][START_REF] Saby | Multivariate analysis of the spatial patterns of 8 trace elements using the French soil monitoring network data[END_REF] Heavy metals in soil can be measured by several conventional analytical techniques including electro-chemical methods, chromatographic separation and spectroscopic techniques etc. Especially, atomic absorption spectroscopy (AAS) and inductively coupled plasma-atomic emission spectroscopy (ICP-AES) have been the official reference methods, preceded by time-consuming acid digestions which are also subject to possible contamination to sample and additional pollution to laboratory environment. Compared with traditional methods, X-ray fluorescence spectroscopy (XRF) has some potential advantages for soil heavy metal analysis, it is nondestructive with rapid throughout and simple sample preparation avoiding acid digestion and the sensitivity of ~ 10 mg kg -1 is appropriate for field screening for most metals. [START_REF] Radu | Comparison of soil pollution concentration determined using AAS and portable XRF techniques[END_REF] For example, field XRF can easily provide detection limits for lead-in-soil of less than 100 mg kg -1 , well below typical regulatory levels of 300 to 1500 mg kg -1 . Actually, XRF has been widely applied in metal determination in variety of environmental samples, such as soil, sediment, dust, rocks. [START_REF] Chou | Rapid detection of toxic metals in non-crushed oyster shells by portable X-ray fluorescence spectrometry[END_REF][START_REF] Block | Use of handheld X-ray fluorescence spectrometry units for identification of arsenic in treated wood[END_REF][START_REF] Carr | Identification and mapping of heavy metal pollution in soils of a sports ground in Galway City, Ireland, Using a portable XRF analyzer and GIS[END_REF] However, the accuracy of the measurement by XRF can be affected by the sample characteristics, e.g. moisture content, density, flatness of the surface, particle size, soil type. [START_REF] Goldstein | Energy-dispersive X-ray fluorescence methods for environmental characterization of soils[END_REF] For soil and other complex matrices, empirical methods for calibration can be difficult or cumbersome, and theoretical calibration methods such as fundamental parameters models are not always viewed as reliable. [START_REF] Vanhoof | Energy -dispersive X-ray fluorescence systems as analytical tool for assessment of contaminated soil[END_REF] Partial least squares regressions (PLSR) are multivariate statistical techniques that have been applied to different sciences to obtain calibration models as an alternative to linear regressions. This statistical method has provided good predictive models for the simultaneous analysis in complex matrices. [START_REF] Alvarez-Guerra | Development of models for predicting toxicity from sediment chemistry by partial least squares-discriminant analysis and counter propagation artificial neural networks[END_REF] In the paper, by combining XRF analysis with the PLSR model, we have developed a relatively uncomplicated technique to determine the Cr, Zn, Pb and As in a collection of soil samples. This approach will almost certainly prove to be applicable to other metals of environmental samples as well. This technique could be useful for the semi-quantitative or quantitative determination of metals in variety of environmental solid samples.

MATERIALS AND METHODS

Sample Preparation

Topsoil (0~20 cm) samples were collected from five provinces in China, including Beijing, Xinjiang, Heilongjiang, Yunnan, and Jiangsu. Soil samples were air-dried and passed through a 2.0 mm sieve, homogenized and stored at 4℃ until use. An incubation experiment was conducted with 500 g of each soil in plastic pot to simulate metal pollution. Heavy metals Cr, Zn, Pb and As were added as nitrate salts (Cr (NO 3 ) 3 , Zn (NO 3 ) 2 , Pb (NO3)2 and NaAsO 2 ) in aqueous solution and then mixed with soils thoroughly. The amounts of metals added to soils were 100, 200, 400, 600, 800, 1000 and 1500 mg kg-1 of Cr, Zn, Pb, and As (metal/soil), respectively. These soils were incubated for 2 months, and air-dried for analysis. And extra pots without addition of heavy metals were simultaneously prepared as blank sample.

XRF Spectra Collection

Cr, Zn, Pb and As in soil samples were simultaneously analyzed by a portable XRF (XRF7), obtained from Beijing Purkinje General Instrument. The instrument parameter and operating condition was listed in table 1. Prior to sample analysis, an internal instrument calibration was performed. All samples were analyzed using the bulk mod for soils. Each sample was analyzed for 300 s through a small plastic cup covered with SDI mylar film.

Measurement of Heavy Metals by Standard Methods

Soil samples were digested using the standard method. 1.0 g soil was placed in a 50 ml round bottom flask with 10 ml aqua regia (HCl :HNO 3 = 1:3). The solution was kept at room temperature overnight before a water condenser was attached and the solution heated to boiling for 2 h. 10 ml of water was added down the condenser before filtration of the mixture through using a Whatman No. 42 filter. The filtered residue was rinsed twice with 5mL of water and the solution was made up to 50mL. All solutions were prepared with 18.3 MΩ deionised water. The above procedure was also used to obtain a blank and control samples and all samples were blank-corrected. Concentration of Cr, Zn, Pb and As in digested sample solution were analyzed using AAS and AFS (Atomic Fluorescence Spectrometry). Reference soil sample ESS-1 was also analyzed as quality control sample. 12,13

Data Analysis

XRF spectra was exported from XRF 7 (version 1.0) software in CSV format to MS-Excel (version 2003) for spectral analysis.

The main idea of PLSR is to get as much concentration information as possible into the first few loading vectors. One of the main advantages of PLSR is that the resulting spectral vectors are directly related to the constituents of interest. In this study, PLSR and leave-one-out cross-validation were used for establishing calibration models for Cr, Zn, Pb and As respectively. Leave-one-out cross-validation estimated the prediction error by splitting all samples into two groups. One was reserved for validation, and the other was used for calibration. The process was repeated until all the samples had been used once in the validation set. The optimum number of factors used in PLSR was determined by the lowest value of predicted residual error sum of squares (PRESS). In this study, PLSR were performed using the Matlab (version 7.0) from Math-Works Inc.

The statistics used for estimating the performance of the calibration models developed by PLSR included correlation coefficients for validation (R)and root mean square error of validation (SEP).

)

n i i i n i m i y y R y y          (1) 1 1 (
)

n i i i SEP y y n      (2)
where y i is the reference value of the i-th sample, y ^i is the predicted value of the ith sample, y m is the average of the referenced value of the validation set, and n is the number of samples in the validation set. By means of full cross-validation with in PLSR method, the selection of optimal PLS factor (number of latent variables) was important. The number of latent variables for heavy metals is obtained according to the smallest PRESS. The number of latent variables for all the metals was 6 the same. Using the optimum parameters for PLSR, the calibration models for Cr, Zn, As and Pb were established respectively. Table 2 showed the results of calibration models for metals. It can be seen that metal model had high calibration accuracy, the correlation coefficient (R) was 0.984, 0.929, 0.979, and 0.958 for Cr, Zn, As and Pb. Its prediction ability was also satisfied, the relative square error of validation (RSEP) were 14.5 %, 15.6 %, 14.9 % and 21.0 %, The XRF predicted metal concentration and its reference value were closely arranged with the 45°line (Figure 2), indicating the prediction error was low. The above result suggested that concentration of heavy metal Cr, Zn, As and Pb in soil could be measured by XRF combined with PLSR model easily.

RESULTS AND DISCUSSION

Conclusion

Quantitative or semi-quantitative analyses for Cr, Zn, As and Pb in soil can be performed using XFR with a calibration model established by the method of PLSR. Creating PLSR calibration models for XRF especially useful if suitable well characterized reference standards are not available or the fundamental parameter method is inaccessible or unsatisfactory. As demonstrated, this model is suitable for soil samples of different types without a set of standards for each site. Certainly, XRF with PLSR model approach can be considered as a useful tool for fast screenings, field testing and rapid identification of heavy metals in soil.

Fig. 1 .

 1 Fig. 1. X-ray fluorescence spectra of the soil samples

Figure 1

 1 Figure 1 shows the XRF spectra of soil samples. The mail features of the soil samples are energy bands between 3.0 keV to 18.0 keV. From the characteristic energy of metals listed in table 1, the relevant energy of electron volt at 5.414 keV, 8.638 keV, 11.725 keV and 12.611 keV and close range represents the concentration of Cr, Zn, As and Pb respectively. Therefore spectra in these energy ranges: 5.399 ~ 5.429 keV, 8.623 ~8.653 keV, 11.710 ~11.740 and 12.595 ~12.625 keV were used for developing PLSR models for Cr, Zn, As and Pb separately.By means of full cross-validation with in PLSR method, the selection of optimal PLS factor (number of latent variables) was important. The number of latent variables for heavy metals is obtained according to the smallest PRESS. The number of latent variables for all the metals was 6 the same.

Fig. 2 .

 2 Fig. 2. Scatter plots between measured and predicted value by PLSR model

Predicted value (mg kg - 1 ) 1 ) 1 )

 111 Measured value (mg kg -1 )Predicted value (mg kg -Measured value (mg kg -1 )Measured value (mg kg -1 ) Predicted value (mg kg -Measured value (mg kg -1 ) Predicted value (mg kg -1 )

Table 1 .

 1 The instrument parameter and operating condition.

		Instrument parameter and operating condition
	X-ray tube	Ag
	Filter	Al + Mo
	Detector	Si-PIN
	V	30 kV
	I	40 A μ
	Cr-Kα	5.414 eV
	Zn-Kα	8.638 keV
	As-KΒ	11.725 keV
	Pb-LΒ	12.611 keV

Table 2 .

 2 The results of calibration models for metals.

		R	SEP (mg kg -1 )	RSEP (%)
	Cr	0.984	109	14.5
	Zn	0.929	117	15.6
	As	0.979	116	14.9
	Pb	0.958	168	21.0

Acknowledgements: This work was supported by the Beijing municipal sciences and technology program (Z09090501040901) and Nation hi-tech research and development program of China ( 2010AA10Z403).


Zhengguang An

Huishan Lu

Huanyu Jiang
email: hyjiang@zju.edu.cn

Yibin Ying
email: ybying@zju.edu.cn

Prediction of Marked Age of Mature Vinegar Based on Fourier Transform Near Infrared Spectroscopy

To evaluate the applicability of near infrared (NIR) spectroscopy for discrimination between mature vinegar with different marked age (1 year, 3 years, and 5 years), transmission spectra were collected in the spectral range from 800 nm to 2500 nm in 1, 2, and 5 mm optical path-length rectangular quartz cuvettes with air as reference at room temperature. Discriminant models were developed based on discriminant analysis (DA) together with raw, first and second derivative spectra. The calibration result for raw spectra was better than that for first and second derivative spectra. The percentage of samples correctly classified for raw were 100% for 1 year old, 100% for 3 years old and 97.5% for 5 years old, respectively. In validation analysis, for 1 year, 3 years, and 5 years old sample groups, the percentage of samples correctly classified was 100%, 100%, and 100%, respectively. The results demonstrated that NIR spectroscopy could be used as a rapid and reliable method for classification of mature vinegar with different marked age.

Introduction

Fermented mature vinegar, which has a long history in China, is treated as the favorite condiments, health-care products and even medicines by Chinese people. The commercial mature vinegar consists of water, acetic acid, sugars, and other secondary constituents that contribute to the smell, taste and preserving qualities. The composition of mature vinegar is 80% (v/v) of water and a great variety of other compounds like organic acids, alcohols, minerals, polyphenols, amino acids, etc. accounting for the other 20%.

In recent years, near-infrared (NIR) spectroscopy has gained wide acceptance in different fields by virtue of its advantages over other analytical techniques, the most salient of which is its ability to record spectra for solid and liquid samples without any pre-treatment. This characteristic makes it especially attractive for straightforward, speedy characterization of natural and synthetic products. NIR spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical and biomedical sectors. One of the most common applications of near-infrared spectroscopy combined with pattern recognition methods has been to discriminate between samples belonging to one of several distinct groups based on spectral properties. Some researches had reported the application of NIR spectroscopy for the aging of wine vinegar or Chinese rice wines [START_REF] Cozzolino | Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins[END_REF] , for discriminate between samples from different origins [START_REF] Liu | Preliminary study on the application of visible-near infrared spectroscopy and chemometrics to classify Riesling wines from different countries[END_REF] , for the prediction of the quality parameters of wine vinegar or wines [START_REF] Pontes | Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry[END_REF][START_REF] Reid | Differentiation of apple juice samples on the basis of heat-treatment and variety using chemometric analysis of MIR and NIR data[END_REF][START_REF] Saiz-Abajo | Classification of wine and alcohol vinegar samples based on near-infrared spectroscopy: Feasibility study on the detection of adulterated vinegar samples[END_REF][START_REF] Yu | Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS[END_REF] . However, although it has been proven that original NIR spectra of vinegar samples might be used to develop classification models with good abilities to discriminate between different origins, NIR data, especially from liquid samples, are affected by different types of perturbations such as optical interferences (light scatter), temperature differences and turbidity, making the use of pre-processing methods and inverse calibration essential. As a result of all the foregoing, NIR spectra contain not only chemical but also physical information which may be irrelevant and can mask the chemical information in the spectra (including information closely related to sample origin), and might deteriorate classification models developed from raw NIR spectra. Therefore, the application of suitable chemometrical methods to NIR spectra in order to minimize the contribution of physical effects and thus enhance the chemical information contained therein, could be seen as an important stage in model development and improvement. The main aim of this paper is to study the aging of mature vinegar during storage period.

Materials and method

Samples

In this work, a total of two brands of mature vinegars were obtained in local market named Ninghuafu and Donghu, with different marked age (1 year, 3 years, and 5 years) of each variety. All of these mature vinegars were commonly used in Chinese people's daily life. Before the experiment, the mature vinegar samples were stored in the laboratory at a constant temperature of 25±1 0 C for more than 48h to have an equalization room temperature. The samples were all original vinegar liquid without dilution. A total of 150 samples (25 samples for each age of each variety) were prepared for further treatments. Twenty samples were selected randomly from each age of each variety and a total of 120 mature vinegar samples were used in the calibration set, whereas, 30 samples (5 for each age of each variety) were selected randomly as the validation set from the remaining samples. And the details were listed in Table 1.

Tab. 1. Distribution of mature vinegar samples in the calibration and validation set

Spectral Measurements

Samples taken from freshly opened bottles of mature vinegar were scanned in transmission mode (800-2500 nm) using a scanning spectrometer, Nexus FT-NIR (Thermo Nicolet Corp., Madison, WI), with an interferometer, an InGaAs detector, and a broad-band light source (quartz tungsten halogen, 50 W). NIR spectral data were collected using OMNIC software (Thermo Nicolet Corp.) and stored in absorbance format. Samples were scanned in demountable liquid cells of different optical path lengths (1, 2, and 5 mm; Pike Technologies, Madison, WI) with air as the reference at room temperature. All samples were shaken before scanning. The mirror velocity was 0.9494 cm s-1, and the resolution was 2 cm-1. The spectrum of each sample was the average of 32 successive scans. The spectral regions with an absorbance value equal to or higher than 1.5 were not used in spectral analysis due to the zero transmissivity and the fact that they are considered saturated.

Chemometrics and Data Analysis

Chemometrics analysis was performed using the commercial software package, TQ Analyst v6.2.1 (Thermo Nicolet Corporation, Madison, WI, USA). Discriminant analysis (DA) was used to classify mature vinegar samples with different vinegar age. It was used to determine the classes of known samples which were most similar to an unknown sample by computing the unknown's distance from each class center in Mahalanobis distance.

Principal component analysis (PCA) was performed before DA models were developed. PCA was performed in order to reduce the number of variables showing co-linearity. Thus, the samples were in a new reduction k-dimensional space (k<n). From the k factor score, Mahalanobis distance which indicated how different a sample spectrum was from the average spectrum of the sample set was calculated. It was defined by the following equation:

( ) ( ) T t t S t t MD i k i i - - - = 1
Where MD i is the Mahalanobis distance, t i is the score vector of ith sample, t is the mean score vector of the sample set, S k is the scores covariance matrix. The DA models were then validated by using it to predict the age of samples in the validation set.

Results and discussion

Spectral Analysis

Figure 1 shows the average spectra of 1, 3 and 5 years old sample groups in the whole sample set without any preprocessing. No obvious spectral differences could be observed between the three spectra, and the spectra are highly overlapped except in the regions 950-980 nm, 1140-1160 nm, 1650-1700 nm, 1780-1800 nm, 1880-1920 nm, and 2200-2250. All spectra have intense absorption bands at 1450 nm, related to the first O-H overtone, and at 1900-1950 nm, related to the combination of stretch and deformation of the O-H group in water. The small absorption band at 1690 nm might be related to the -CH3 stretch first overtone or C-H groups in aromatic compounds, at 1782 nm related to the C-H stretch first overtone, at 2266 nm likely with C-H combination bands of methanol, and at 2302 nm with combination band of C-H stretch and deformation of C-H from the -CH2 group (Yu et al., 2007; Cozzolino et al., 2003) [START_REF] Cozzolino | Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins[END_REF][START_REF] Yu | Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS[END_REF] .

Calibration Analysis

The calibration results of DA model developed on raw spectra are much better than that on first and second derivative spectra. There are only 1 sample is misclassified in the DA model developed on raw spectra. While there are 74 and 80 samples incorrectly classified in the DA model developed on first and second derivative spectra. The reason for the performance of raw spectra better than that of second derivative spectra is that DA with raw spectra describes 100% of the variability, while DA with first and second derivative explains 54.4 and 37.1%, respectively (The data, 100%, 54.4 and 37.1%, are obtained from the calibration results of the three models.). Thus, raw spectra were used for calibration and validation analysis. Table 2 shows the calibration results of DA models developed on raw, first and second derivative spectra. The results were in agreement with the literature for Chinese rice wine variety analysis (Hanyan Yu et al. 2003) [START_REF] Yu | Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS[END_REF] .

Tab. 2. Calibration results of

Validation Analysis

Thirty samples (5 for each age of each variety) were selected randomly as the validation set. There were no samples misclassified. Therefore, NIR spectroscopy can be used to classify mature vinegar samples with different vinegar age. The ability of the NIR spectroscopy to discriminate or identify mature vinegar is based on the vibrational responses of chemical bonds to NIR radiation. It is that the major components of mature vinegar with different vinegar age are significantly different. Most of the changes were related to the primary structural components of organic molecules (C-H, N-H and O-H bonds), which provided the necessary information for classification of mature vinegar with different vinegar age by NIR spectroscopy. Meanwhile, the spectral difference between the sample groups with different mature vinegar age can be detected from Figure 1, correspondingly. The pairwise distance plots for the 1-, 3-and 5-year-old sample groups were showed in Figure 2, respectively. They show graphically the Mahalanobis distance between each standard and the two classes that are selected for the X-and Y-axis of the plot. The calibration standards for the class that is selected for the X-axis of the pairwise distance plot are similar to each other; the data points for those standards will be clustered in the upper left corner of the plot. Similarly, the data points for the standards selected for Y-axis will be clustered in the lower right corner of the plot. The greater the distance between the two clusters in the plot, the greater is the difference between the corresponding class and the other class. From Figure 2, it can be seen that the 3 sample groups can be clearly classified. The applicability of FT-NIR spectroscopic technique for predicting age of mature vinegar is presented in this work. The percentages of samples correctly classified in calibration and validation analysis were 99% and 100%, respectively. The results indicated that NIR spectroscopy together with DA is a powerful tool for discrimination between mature vinegar samples with different vinegar age. Further studies are needed to extend to other varieties of mature vinegar.

Fig. 1

 1 Fig.1 Average spectra of 1, 3 and 5 years sample groups in the whole mature vinegar sample 3.2 Chemometrics analysis

DA models developed on raw, 1st and 2nd derivative

Fig. 2

 2 Fig.2 Pairwise distance plot for 1-, 3-and 5-year-old sample groups in the whole sample set

Acknowledgement

The authors gratefully acknowledge the financial support provided by Shanxi Youth Science and Technology Research Fund (No. 2009021019-3).

The paper supported by by Shanxi Youth Science and Technology Research Fund (No. 2009021019-3).


Lihua Jiang
email: jianglh@caas.net.cn

Wensheng Wang

Xiaorong Yang

Nengfu Xie
email: nf.xie@caas.net.cn

Youping Cheng
email: youping@caas.net.cn

An Agricultural Tri-dimensional Pollution Data Management Platform based on DNDC Model

Keywords: DNDC, United Storage, Format Conversion 1 DNDC, United Storage, Format Conversion 1

DNDC is a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems. It is used in agricultural tri-dimensional pollution control widely. Learning from abroad advanced technologies and research methods, we have developed an agricultural tri-dimensional pollution data submission and management platform based on DNDC model. The platform is very important for sharing and building our agricultural carbon and nitrogen chain database.

Introduction

DNDC (DeNitrification-DeComposition) is a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems. The model can be used for predicting crop growth, soil temperature and moisture regimes, soil carbon dynamics, nitrogen leaching, and emissions of trace gases including nitrous oxide (N 2 O), nitric oxide (NO), dinitrogen (N 2 ), ammonia (NH 3 ), methane (CH 4 ) and carbon dioxide (CO 2 ). Studying on carbon and nitrogen chain in agricultural tri-dimensional pollution [1] and blocking carbon and nitrogen pollution sources is very important for prevention and treatment agricultural carbon and nitrogen pollution [2].

Development DNDC [3] model needed data management system can undoubtedly provides important scientific support for our country agriculture carbon and nitrogen emission reduction [4] database construction and data sharing.

Study and develop a data submission, management and supporting DNDC model platform for every level user in agriculture tri-dimensional [5] preventive treatment centre. Data submission and management module provides data management interface for administrators and is in favor of adjusting agriculture tri-dimensional pollution data. Assistant DNDC [6] model module can read out data from DNDC database for users and create txt files and also imports DNDC model results to database according to user' willing.

Platform Main Functions

This platform is made up of distributed data submission and management module, submitted data centralized management module and assistant DNDC module. The functions of platform are shown in figure 1.

Submitted data centralized management

Platform can realize central management of submitted data from bases and provides functional interface in which central administrator can modify construction of database directly. It is very convenient and visual for users to operate database. The operation of database construction includes adding and deleting tables and adding and deleting attributes of tables.

Assistant DNDC module

Functions of the module is shown in figure 2

Key Technologies

In developing process, we solve the key technology problems including distributed heterogeneous data submission, data submission item by item or in batch, format conversion from database to DNDC model, storage of DNDC model results, function management mechanism based on role and so on.

Distributed heterogeneous data submission technology

In distributed data submission process, the tables submitted by different bases are different. If in designing process, every base is shown all the tables, the system is not well targeted and not convenient for users. But if every base corresponding table is designed in advance, it is also not perfect, because it is possible to change the submitted data tables. The fields in tables are in the same reasons. Aiming at above problems, the platform provides table views and user personal control of submission interface fields display function. At first, users can select needed tables by random in platform and then navigation bar will embody users' personal choice and provide pointed interface. In the next place, users can select corresponding field attributes of tables in function interface and make up personal submission interface. It is convenient for different users to operate.

Data submission item by item or in batch technology

Distributed data submission and management module mainly realizes different bases and different information submission function. So submission function is the key and difficult problem of the platform. Because the quantity of bases using the platform is not limited and data bulk submitted by bases is not limited, if system only provides submission item by item function, when submitted data bulk is so large, system will waste a lot of time.

So system also provides submission in batch function. In submission data item by item, formats of submitted data must be controlled seriously when base administrator submits data to central databases. In submission item by item interface, platform provides description of submitted attributes data format and clear clue of input error.

User can realize submit a small quantity of unpacked data correctly. When user needs to submit a mass of packed data, data submitting in batch module is needed and shows superiority fully

Format conversion technology from database to DNDC model

The central database stores data from every base. Administrator stores them in fixed format to many stables in database. When DNDC model is used to stimulate agricultural tri-dimensional pollution preventive treatment, data in database is needed and a lot of them need to be adopted in fixed text document format.

Conclusion

Combined with our country ecology system actual situation, introducing foreign developed DNDC model and on carbon and nitrogen chain in agricultural tri-dimensional pollution [1] and blocking carbon and nitrogen pollution sources is very important for prevention and treatment agricultural carbon and nitrogen pollution [2].

Development DNDC [3] model needed data management system can undoubtedly provides important scientific support for our country agriculture carbon and nitrogen emission reduction [4] database construction and data sharing.

Study and develop a data submission, management and supporting DNDC model platform for every level user in agriculture tri-dimensional [5] preventive treatment centre. Data submission and management module provides data management interface for administrators and is in favor of adjusting agriculture tri-dimensional pollution data. Assistant DNDC [6] model module can read out data from DNDC database for users and create txt files and also imports DNDC model results to database according to user' willing.

Platform Main Functions

This platform is made up of distributed data submission and management module, submitted data centralized management module and assistant DNDC module. The functions of platform are shown in figure 1.

Submitted data centralized management

Platform can realize central management of submitted data from bases and provides functional interface in which central administrator can modify construction of database directly. It is very convenient and visual for users to operate database. The operation of database construction includes adding and deleting tables and adding and deleting attributes of tables.

Assistant DNDC module

Functions of the module is shown in figure 2

Key Technologies

In developing process, we solve the key technology problems including distributed heterogeneous data submission, data submission item by item or in batch, format conversion from database to DNDC model, storage of DNDC model results, function management mechanism based on role and so on.

Distributed heterogeneous data submission technology

In distributed data submission process, the tables submitted by different bases are different. If in designing process, every base is shown all the tables, the system is not well targeted and not convenient for users. But if every base corresponding table is designed in advance, it is also not perfect, because it is possible to change the submitted data tables. The fields in tables are in the same reasons. Aiming at above problems, the platform provides table views and user personal control of submission interface fields display function. At first, users can select needed tables by random in platform and then navigation bar will embody users' personal choice and provide pointed interface. In the next place, users can select corresponding field attributes of tables in function interface and make up personal submission interface. It is convenient for different users to operate.

Data submission item by item or in batch technology

Distributed data submission and management module mainly realizes different bases and different information submission function. So submission function is the key and difficult problem of the platform. Because the quantity of bases using the platform is not limited and data bulk submitted by bases is not limited, if system only provides submission item by item function, when submitted data bulk is so large, system will waste a lot of time.

So system also provides submission in batch function. In submission data item by item, formats of submitted data must be controlled seriously when base administrator submits data to central databases. In submission item by item interface, platform provides description of submitted attributes data format and clear clue of input error.

User can realize submit a small quantity of unpacked data correctly. When user needs to submit a mass of packed data, data submitting in batch module is needed and shows superiority fully.

Conclusion

Combined with our country ecology system actual situation, introducing foreign developed DNDC model and development carbon and nitrogen emission reduction motivation model can help scientists and engineers judge intuitively and make a strategic decision. So we build up a mutual platform in which collect and manage data for DNDC model. For studying agricultural tri-dimension pollution, validation, amendment and operation agricultural tri-dimension pollution carbon and nitrogen emission reduction motivation model needs a lot of data support, so standardization of database is very important. In the platform, database can receive all the collected data from bases administrator. Central system administrator can adjust database according to types of collected data in order to supporting our country actual situation and DNDC model.

Figure1. Platform main functions 2 . 1

 21 Figure1. Platform main functions

  . The system provides two different batching submission means: ① Import from Excel files to database. If user has already stored collected data in definite format in Excel files and can use the batching submission interface to import them to central database quickly after mapping. It can save a lot of importing item by item time. ② Import data from local database to central database. In many cases, user stores collected data in local database in favor of partial management. At the moment, user can use batching submission module to copy local data to central database quickly.

  development carbon and nitrogen emission reduction motivation model can help scientists and engineers judge intuitively and make a strategic decision. So we build up a mutual platform in which collect and manage data for DNDC model. For studying agricultural tri-dimension pollution, validation, amendment and operation agricultural tri-dimension pollution carbon and nitrogen emission reduction motivation model needs a lot of data support, so standardization of database is very important. In the platform, database can receive all the collected data from bases administrator. Central system administrator can adjust database according to types of collected data in order to supporting our country actual situation and DNDC model. Introduction DNDC (DeNitrification-DeComposition) is a computer simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems. The model can be used for predicting crop growth, soil temperature and moisture regimes, soil carbon dynamics, nitrogen leaching, and emissions of trace gases including nitrous oxide (N 2 O), nitric oxide (NO), dinitrogen (N 2 ), ammonia (NH 3 ), methane (CH 4 ) and carbon dioxide (CO 2 ). Studying

Figure1. Platform main functions 2 . 1

 21 Figure1. Platform main functions

  including database connecting, data format conversion from database to DNDC model, storage of results from DNDC stimulation model to database . DNDC model sometimes will use some text files for example climate files, fertigation files, flooding files in site mode and initial data files in region mode and these files can created in assistant DNDC module. User can read result files from DNDC model to database in the platform. Within the power of privilege, user can select different formats result files in different modes and store them in corresponding database.

5 System function management mechanism based on role The

  Formats of the data in database should be converted to format which DNDC model can recognize and use. Format conversion is a dynamic form. Platform shows all choices possibly used in text files, every table name relevant to DNDC model in database and all fields name of the tables. When user uses the platform, he can decide items in text file and the table and fields in database.

	4	Technology innovation
	4.1	Distributional

3.4 Storage DNDC model results to database technology User uses DNDC model to stimulate and get some results. According to different stimulation in different modes, the result files have two formats: txt and csv. The format of result data in above two formats result files is the same and assistant DNDC module can read out and show data items of result files. If user wants read data item in result files in some table in database, he can dynamically select data item in choice box. In the process of reading in, user can decide how to map data items into the fields in tables or whether read data items to correspond tables in database. And txt result file data can be read in five tables in the database and user can decide whether read in tables or which tables should be read in. Csv result file data can be read in one table in database. When user selects fields corresponding to data item, he can import all data to database. When the above two types of files are imported to database, the user' name is stored in database automatically in order that central administrator can adjust database management. 3.system has three user roles: central administrator, base administrator and DNDC user. Base administrator collects local base data and submits to platform. Central administrator has all management privilege and can limit base administrator' management privilege. When DNDC user uses DNDC model, central administrator will give privilege to use assistant DNDC model. DNDC user can be base administrator and central administrator and also other privileged user.

heterogeneous agricultural tri-dimensional pollution data submission technology

  It is convenient for different users to operate different operations. Platform provides data submission item by item and batching submission. In submission data item by item ① interface, formats of submitted data must be controlled seriously when base administrator submits data to central databases. Platform provides description of submitted attributes data format and clear clue of input error. User can realize submit a small quantity of unpacked data correctly. ② Batching submission: Platform provides two different batching submission methods: importing from Excel files to central database and importing from local database to central database. By the batching submission module provided by system, users can copy data in local database quickly to central database.

	4.2

The platform solves distributed heterogeneous agricultural tri-dimensional pollution data submission in many nodes technology and provides table views and personal control of submission interface fields display function.

At first, user can select needed tables at random in the platform and navigation bar will embody user' personal selection and provides pointed interface. Secondly, user can select corresponding field attributes of tables and create personal submission interface.

Mutual access technology of DNDC model software and database

  In the process of DNDC model accumulation software working, users should input relevant data and read in some fixed format text files under some circumstances. All data should be read out from database, so DNDC software is needed to be able to visit database and can read the data. The result data stimulated by DNDC model should be stored in database and DNDC software is required to mutual access with database. Assistant DNDC module can realize DNDC and database mutual access and data transmission. When data is read from database and built up txt files, the platform not only shows txt files but also tables and fields in database. So user can decide how to map by himself. When part of result data from DNDC model is stored in database, result files has different formats and store in different formats. After user selects the loading result files, platform will show every data item and field names of tables in database. User can decide how to map data item in result files to fields of tables in the database and read in result data to database. It is convenient to user.

.5 System function management mechanism based on role The

  The system provides two different batching submission means: ① Import from Excel files to database. If user has already stored collected data in definite format in Excel files and can use the batching submission interface to import them to central database quickly after mapping. It can save a lot of importing item by item time. ② Import data from local database to central database. In many cases, user stores collected data in local database in favor of partial management. At the moment, user can use batching submission module to copy local data to central database quickly.The central database stores data from every base. Administrator stores them in fixed format to many stables in database. When DNDC model is used to stimulate agricultural tri-dimensional pollution preventive treatment, data in database is needed and a lot of them need to be adopted in fixed text document format. Formats of the data in database should be converted to format which DNDC model can recognize and use. Format conversion is a dynamic form. Platform shows all choices possibly used in text files, every table name relevant to DNDC model in database and all fields name of the tables. When user uses the platform, he can decide items in text file and the table and fields in database.User uses DNDC model to stimulate and get some results. According to different stimulation in different modes, the result files have two formats: txt and csv. The format of result data in above two formats result files is the same and assistant DNDC module can read out and show data items of result files. If user wants read data item in result files in some table in database, he can dynamically select data item in choice box. In the process of reading in, user can decide how to map data items into the fields in tables or whether read data items to correspond tables in database. And txt result file data can be read in five tables in the database and user can decide whether read in tables or which tables should be read in. Csv result file data can be read in one table in database. When user selects fields corresponding to data item, he can import all data to database. When the above two types of files are imported to database, the user' name is stored in database automatically in order that central administrator can adjust database management. system has three user roles: central administrator, base administrator and DNDC user. Base administrator collects local base data and submits to platform. Central administrator has all management privilege and can limit base administrator' management privilege. When DNDC user uses DNDC model, central administrator will give privilege to use assistant DNDC model. DNDC user can be base administrator and central administrator and also other privileged user.

	3.3	Format conversion technology from database to DNDC model
	3.4	Storage DNDC model results to database technology
	34	Technology innovation
	4.1	Distributional

heterogeneous agricultural tri-dimensional pollution data submission technology

  It is convenient for different users to operate different operations. Platform provides data submission item by item and batching submission. In submission data item by item ① interface, formats of submitted data must be controlled seriously when base administrator submits data to central databases. Platform provides description of submitted attributes data format and clear clue of input error. User can realize submit a small quantity of unpacked data correctly. ② Batching submission: Platform provides two different batching submission methods: importing from Excel files to central database and importing from local database to central database. By the batching submission module provided by system, users can copy data in local database quickly to central database.

	4.2

The platform solves distributed heterogeneous agricultural tri-dimensional pollution data submission in many nodes technology and provides table views and personal control of submission interface fields display function.

At first, user can select needed tables at random in the platform and navigation bar will embody user' personal selection and provides pointed interface. Secondly, user can select corresponding field attributes of tables and create personal submission interface.

Mutual access technology of DNDC model software and database

  In the process of DNDC model accumulation software working, users should input relevant data and read in some fixed format text files under some circumstances. All data should be read out from database, so DNDC software is needed to be able to visit database and can read the data. The result data stimulated by DNDC model should be stored in database and DNDC software is required to mutual access with database. Assistant DNDC module can realize DNDC and database mutual access and data transmission. When data is read from database and built up txt files, the platform not only shows txt files but also tables and fields in database. So user can decide how to map by himself. When part of result data from DNDC model is stored in database, result files has different formats and store in different formats. After user selects the loading result files, platform will show every data item and field names of tables in database. User can decide how to map data item in result files to fields of tables in the database and read in result data to database. It is convenient to user.

Acknowledgements

This work is supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), Special fund project for Basic Science Research Business Fee, AIIS(Grant No. 2010-J). Acknowledgements This work is supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), Special fund project for Basic Science Research Business Fee, AIIS(Grant No. 2010-J).


Haifeng Li
email: haifengl1984@sina.com

Fanjiang Zeng
email: zengfj@ms.xjb.ac.cn

Dongwei Gui

Jiaqiang Lei

Characteristics of Soil Environment Variation in Oasis-Desert Ecotone in

Keywords: Soil moisture, Soil nutrient, Size distribution, Cele Oasis

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

Profile of region of interest

Cele Oasis (oasis is used in a broad sense here) lies in the middle section of the south edge of Taklimakan Desert and at the north foot of Kunlun Mountain, located at 35°17′55″-39°30′00″N and 80°03′24″-82°10′34″E. Elevation in Cele County has range 1280-6780 m above sea level. The research site was limited to 1340-1380 m.

The mean annual precipitation is 35.1 mm, mean annual evaporation is 2595.3 mm, and mean annual temperature is 11.9°C; a typical continental arid climate. It is windy all year around, with the prevailing wind direction northwest. Soil type is mainly aeolian sandy soil, with the original soil mostly Quaternary System diluvial-alluvial deposits [START_REF] Zeng | Water and nutrient dynamics in surface roots and soils are not modified by short-term flooding of phreatophytic plants in a hyperarid desert[END_REF] , of light texture, high sand and low clay contents, excellent permeability and poor fertility-water retention [START_REF] Zeng | Cele Oasis: Environmental characteristics of Desert Transitional Area and Dominant plants adaptation[END_REF] . The oasis is surrounded by natural vegetation in the east and west parts, while its southern part connects with mobile dunes and the Gobi desert. The experiment and observations were implemented at the west of the oasis (35°01′20.7″N and 80°43′45.9″E).

Experimental Plot Selection and Index Measurement

Farmland was reclaimed in the oasis-desert ecotone in 1994, and one test plot (FP) was constructed, with cotton planted and irrigation by channeling water from the Cele River floods or flood-irrigation with groundwater. The long-term fertilizer input amount was equivalent to that used by local farmers. Thus, it represents the main production unit during the oasis expansion process. One control plot (CP) was selected close to the reclaimed farmland plot as a reference; vegetation on this plot was mainly Alhagi sparsifolia Shap. at coverage of about 38.9%, representing the natural status of the oasis-desert ecotone. The area of each plot was 1 ha. After 15 y of continuous cultivation, observations of soil moisture, nutrients and soil particle-size were analyzed and compared in 2009 with those of the control plot.

Since groundwater depth in the test region is > 15 m [ ] 1 , the impact of groundwater on agricultural soil moisture was small. The impact of cultivation on the soil environment is mostly in the upper layer, thus soil environmental characteristics only within the depth of 0-100 cm are discussed.

Measurement of soil moisture

Volumetric water content of plot soil was measured with a neutron gauge (CNC503DR model by Beijing Nucleon Apparatus Company) during 1 January to 31 December 2009: every 10 cm was taken as one layer within 0-60 cm, and every 20 cm within 60-100 cm, and measurement was conducted once every 5 d. Precipitation was field-measured with a rain-gauge bucket at a meteorological observation site close to the test plot.

Sampling and analysis of soil nutrient

Soil sampling was from the farmland and control plots after cotton harvesting in October 2009. Three points were selected at random in each plot, and samples collected from five soil layers: 0-20, 20-40, 40-60, 60-80 and 80-100 cm. All samples were placed in plastic bags, closed with a seal and sent back to the laboratory for drying in the shade and root-removing treatment. Seven indexes of soil nutrients were analyzed by standard soil test procedures [START_REF]Editorial Committee: Soil Physical and Chemical Analysis and Description of Soil Profile[END_REF] , i.e. soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP) and available potassium (AK). Soil samples from each plot were also used for particle-size analysis using a laser granulometer (Mastersizer 2000, Malvern Instruments, England).

Analysis and evaluation of soil nutrient indexes

To comprehensively reflect the status of soil nutrients, soil nutrient properties were analyzed and compared using the soil retrogression index (RI) [START_REF] Davidson | Changes of soil carbon inventories following cultivation of previously untilled soils[END_REF] . For specific calculation and analysis, the soil properties of the control plot were used as the baseline. RI value of each soil layer of farmland was obtained using the formula that follows:

1 RI ( ) / 100% / n i i i i x x x            n (1)
Where, RI is soil-nutrient composite index, x i  is measured soil nutrient index of each layer from the control plot, and x i is the measured value of the same-layer soil nutrient index from the farmland plot (SOM, TN, TP, TK, AN, AP and AK). If RI > 0 for each layer, this indicated that soil nutrient composite indexes from the layer of the farmland plot was higher than that from the same layer of the control; and vice versa if RI < 0.

Statistical analysis

Significance of differences between means was compared by t-tests, respectively, between soil nutrient indexes of farmland and control plots. One-way ANOVA was used to compare soil characteristics of the different layers in farmland and control plots, respectively. The least significance range (LSR) method was used for multiple comparisons, and Pearson's correlations method for correlation analysis. ANOVA, LSR and Pearson correlations were all implemented with SPSS 16.0 software.

Results and Analysis

Differentiation characteristics of soil moisture

There was one year of continuous observations in 2009 of mean soil moisture variation within the soil depths of 0-100 cm in farmland and control plots (Table 1). There was a significant difference within 0-10 cm between farmland and control plots (P < 0.05), and a significant difference within all the other layers (P < 0.001) (Table 1). The maximum value (14.83%) was at 60-80 cm for the farmland plot and the minimum value (3.97%) was at the surface layer. This differed to the control plot where the maximum was at 80-100 cm and the minimum at the surface layer.

Soil moisture of the two plots also showed obvious differentiation characteristics and a particular regularity over time. The soil moisture status at different depths, for each month of the one year is shown in Fig. 1. The soil moisture of each layer of the farmland plot was generally always higher than the same layer of the control plot within 0-100 cm during the different periods (Fig. 1). However, during the fallow periods of January-March and October-December, the difference between farmland and control plots was small, in particular at a depth of 30 cm. During the farming period of March-September, the farmland plot had obviously higher soil moisture than the control plot. The soil moisture content curve for the farmland plots was multimodal at 0-30 cm, bimodal at 40-80 cm, and unimodal at 80-100 cm, i.e. a rule of time variation from multimodal to unimodal presented itself with increased depth.

Variation of soil moisture content at different depths of the two plots over different periods is shown in Fig. 2. The difference in soil moisture between farmland and control plots at different depths was clear (Fig. 2). Soil moisture of the control plot had a generally monotonic increase with increased depth. There was a unimodal increase in the farmland plot with increased depth; increasing first and then decreasing, with the peak during

July-September at depths of 50-60 cm, and the peak for other months all at depths of 60-80 cm (average soil moisture was 14.6%), similar to the annual mean at this depth (Table 1).

Pearson's correlation analysis indicated that average soil moisture of each layer of the farmland plot within 0-100 cm had no significant correlation with precipitation;

neither did average soil moisture farmland at 0-10 cm with irrigation volume.

However, at 10-20 cm soil moisture was significantly (P < 0.05) and positively correlated with irrigation volume, as was soil moisture of each layer within 20-100 cm (P < 0.01). Soil moisture of the control plot at 0-10 and 10-20 cm was significantly (P < 0.01) and positively correlated with precipitation. However, there was no significant correlation with precipitation for soil moisture of each layer within 20-100 cm.

Comprehensive variation characteristics of soil nutrient index

Based on t-tests of nutrient indexes of the two plots, SOM, TN, TP, AN and AP contents of farmland were all significantly (P < 0.05) higher than those of the control plot at 0-20 cm. There were no significant differences in the other indexes between farmland and control plots; however, the AK content of farmland was somewhat lower. The difference in SOM, TN and AP contents was significant between farmland and control plots at 20-40 cm, but not in any other index except that AK of the farmland plot was higher than that of the control. At 40-60 cm, AP content of farmland was significantly higher than that of the control plot, but not for any other soil nutrient index, although all soil nutrient indexes of farmland were somewhat higher. At 60-80 cm, SOM content of farmland was significantly higher than that of the control plot, but not for any other soil nutrient index. At 80-100 cm, there were no significant differences in all soil nutrient indexes between farmland and control plots.

Multiple comparisons among the nutrient indexes at different depths for the two plots are shown in Table 2. Values in each column with the same letter are not significant (LSR) between different soil depth within each plot; * Significant at P < 0.05; ns: Not significant at P < 0.05 ANOVA and LSR multiple comparison results showed that soil nutrient discrepancies at different depths of the farmland plot were reflected mainly in SOM, TN, AN, AP and AK indexes; there were no significant differences in TP and TK indexes. SOM, TN, AN and AP contents of farmland at 0-20 cm were all significantly higher than those of layers deeper than 20 cm. There was no significant difference in SOM content of layers deeper than 20 cm. Within 0-60 cm, SOM content showed a decreasing trend with increased depth. SOM content had a significant impact on TN content [START_REF] Brubaker | Soil properties associated with landscape positions[END_REF] , and so TN content showed a decreasing trend with increased depth within 0-100 cm depth. AK content of farmland at 40-60 cm was significantly higher than in all other layers; however, there was no significant difference in AK content of other layers.

There were no significant differences in SOM, TN and TK contents of each soil layer of the control plot within 0-100 cm. Discrepancy in TP content was mainly at 0-40 and 80-100 cm, with no other significant differences in TP content in other layers. The discrepancy in AN index was reflected in AN content at 40-60 cm, which was significantly higher than in any other layer; there was no significant difference in AN content among other soil layers. AP content at 0-20 cm was significantly higher than that of other layers; however, there was no significant difference among other soil layers. AK content of soil layers within 0-20 cm was significantly higher than that of all other layers deeper than 40 cm, but there was no significant difference in AK content of soil layers deeper than 20-40 cm.

Composite index RI of soil nutrients in each layer of the farmland plot is shown in Fig. 3. Within 0-100 cm, RI > 0 for all layers of the farmland plot, indicating that the composite index of soil nutrients in each soil layer was higher than that of the control plot. At 0-20 cm, RI of farmland was 186% higher than in the control plot, indicating that after oasis-desert ecotone soil was reclaimed and became farmland, that the long-term irrigation and fertilization management increased soil nutrient levels. RI was 91% higher at 20-40 cm, indicating that soil nutrient conditions of this layer had greatly improved compared with the control plot. Within 40-100 cm, RI of each layer was < 40% and decreased gradually with increased depth, indicating that farmland reclamation only improved soil nutrient conditions of the oasis-desert ecotone within a certain depth, i.e. improvement was gradually weakened with increased depth. The soil nutrient composite indexes between each layer deeper than 20 cm and the surface layer (0-20 cm) of both plots were compared, i.e. the soil properties of each plot at 0-20 cm were taken as the baseline. RI of layers at 20-40, 40-60, 60-80 and 80-100 cm of farmland and control plots were calculated with formula (1), and all results were negative (Fig. 4), indicating the soil nutrient composite indexes of both farmland plot and control plot at 0-20 cm were higher than those of deeper layers down to 100 cm.

Differentiation characteristics of soil particle size

The soil particle-size distributions of each layer of both plots for 0-100 cm were within the range 0.35-2000 μm (Table 3). The t-tests indicated no significant differences in clay contents between farmland and control plots at 0-20 cm, but the value of the farmland plot was slightly higher than that of the control plot. Silt contents of farmland were significantly higher (P < 0.01), and sand contents significantly lower (P < 0.01) than the control plot (Table 3).

There were no significant differences in clay, silt and sand contents between farmland and control plots at depths of 20-40, 40-60, 60-80 and 80-100 cm, respectively (P < 0.05). The impact of cultivation on variation of soil particle-size showed a particular regularity: silt and clay contents in farmland clearly increased and sand contents decreased.

ANOVA indicated no significant differences in clay contents of each layer between farmland and control plots within 0-100 cm (P < 0.05), and similarly for silt contents and sand contents. The soil layer with the highest clay contents in the farmland plot was at 80-100 cm, the highest silt contents at 0-20 cm, and the highest sand contents at 40-60 cm. The soil layer with the highest clay contents in the control plot was at 80-100 cm, the highest silt contents at 80-100 cm, and the highest sand contents at 0-20 cm.

Discussion

The research on soil moisture of the oasis-desert zone in the middle reaches of the Black River within depths of 0-50 cm indicated that soil moisture content of the surface layer in a oasis-desert ecotone was lower than that of oasis farmland [START_REF] Zhang | Heterogeneity of Soil Moisture and Salt Contents and Its Eco-environmental Effects in Oasis-desert Belt in Arid Zone[END_REF] . A similar result was obtained in our study, and in addition the soil moisture content of each layer within 0-100 cm in the natural status of the oasis-desert ecotone was found to be lower than that of farmland.

The research on soil moisture characteristics of the oasis-desert ecotone at Minqin indicated a vertical variation of soil moisture with an increasing trend from surface to deeper layer within 0-120 cm [START_REF] Wang | Research on laws of water balance at transitional zone between oasis and desert in Minqin[END_REF] . Soil moisture of the desert-oasis ecotone in the middle reaches of the Black River within depths of 0-200 cm increased with increased depth [START_REF] Liu | Water requirements and stability of oasis ecosystem in arid region, China[END_REF] . Our results indicated that within 0-100 cm, the soil moisture of the control plot in the oasis-desert ecotone had a generally monotonic increasing trend with increased depth, consistent with previous findings. Because of poor soil texture in the surface layer of the control plot, weak water-retention capacity and the impact of evaporation and infiltration, the soil moisture content of the surface layer was significantly lower than that of deeper layers.

The research findings on Kerqin sandy land indicated that precipitation influenced soil moisture content of the surface layer at 0-0.8 m depth only, due to the consumption of most precipitation by vegetation via transpiration [START_REF] Zhao | The Process of Desertification and its Mechanisms of restoration in Horqin Sandy Land[END_REF] . In this paper, Pearson's correlation analysis indicated a significant positive correlation between soil moisture of aeolian sandy soil of the control plot at 0-20 cm and precipitation, while no such correlation occurred for layers deeper than 20 cm, indicating that precipitation had an impact on soil moisture content mainly at 0-20 cm, and below that depth the soil moisture may be affected by other factors such as infiltration.

Usually agricultural soil moisture is affected by various factors such as precipitation, irrigation volume, crop growth and climatic conditions. Some findings have indicated that farmland moisture characteristics of arid areas in northern China are affected mainly by evapotranspiration rate [START_REF] Kang | Effects of limited irrigation on yield and water use efficiency of winter wheat in the Loess Plateau of China[END_REF] . In the event of no groundwater recharge, then precipitation or irrigation would be the main sources of water for farmland, and different precipitation (irrigation) volumes will induce variations in soil moisture [START_REF] Deng | Transforming model of soil moisture between layers of 0~50 cm and 0~100 cm[END_REF] . Soil moisture of the farmland plot showed a unimodal increasing trend with increased depth, which increased first and then decreased; of these the peak value was during July-September at a depth of 50-60 cm, and was the result of inconsistent irrigation volumes and irrigation intervals. The soil moisture of each layer of the farmland plot was significantly and positively correlated with irrigation volume within 10-20 cm (P < 0.05) and within 20-100 cm (P < 0.01), indicating that irrigation was the major factor affecting agricultural soil-moisture characteristics within a depth of 1 m. Research by Sun showed that soil moisture during the cotton growth period at Cele was 6.64-13.3% with irrigation [START_REF] Sun | Preliminary Study on Water-saving Irrigation Model of Three Crops in Cele[END_REF] . The average soil moisture range of the farmland plot (0-80 cm) was 10.83-12.89% during the cotton growth period (April-October) in this paper, similar to the findings of other researchers -the results can be taken as a guide for local field irrigation.

The acquisition, accumulation and consumption of soil organic matter, nitrogen and phosphorus differ according to diverse land uses and soil tillage [START_REF] Risser | :Range condition analysis:past, present and future[END_REF] ; and coverage variation caused by oasis growth also had a significant impact on soil nutrient characteristics [ ] 1 . The oasis-desert ecotone has less organic matter accumulation under an original state, while farmland becomes fertile with application of farmyard manure and inorganic fertilizer during growing seasons following crop planting. A composite index of soil nutrient was obtained, based on various soil properties, which effectively reflects soil quality and is helpful for visual comparison and evaluation [ 25 , 26 ] . The comparison of RI between the two plots showed that farmland had significantly higher values than that of the control plot, indicating that soil nutrient improvement in the ecotone was positive with certain substance and energy inputs.

Soil nutrients reached a maximum in both plots at 0-20 cm, possibly indicating that irrigation and cultivation factors had an impact mainly on the cultivated horizon of farmland; while litter fall of natural vegetation in the ecotone participated in the nutrient cycle, also making soil nutrients in the surface layer of the control plot relatively higher.

Silt and clay contents of the farmland plot were higher than those of the control, but sand content was lower presumably due to the impact of artificial irrigation and cultivation on accumulation of fine soil particles during the oasis expansion process [ ] [START_REF] Gui | Characterizing variations in soil particle size distribution in oasis farmlands-A case study of the Cele Oasis[END_REF] . Sand and silt contents were the major component in upper and lower layers of both farmland and control plots, and the proportions were: sand > silt > clay (< 2.00%), which is the same as the soil size-grade distribution characteristics of the middle Heihe River basin [START_REF] Zhao | Zhang ZH:Water requirements of maize in the middle Heihe River basin[END_REF] , also reflecting poor soil texture in arid regions.

Increasing decomposition of vegetation and inputs of organic matter after reclamation were responsible for the improved soil nutrients. Bouyoucos indicated that an increase in organic matter improved the soil moisture content [START_REF] Bouyoucos | Effect of organic matter on water-holding capacity and the wilting point of mineral soils[END_REF] , and organic matter provides the cementation for water-stable soil aggregates [START_REF] Ma | Zhang L:Relationship Between Water-Stable Aggregates and Nutrients in Black Soils After Reclamation[END_REF] . The soil porosity of original sand improved with the increasing trend of organic matter, silt contents and clay contents, thus improving the soil structure.

Summary

With farmland being the main land use, as a result of population and economic pressures, there are important and positive impacts on the soil environment during the process of expansion into the oasis-desert ecotone. The contrastive analysis of physical and chemical properties (e.g. soil moisture, nutrient and particle sizes) between a 15-y-cultivated farmland plot and a control plot in the ecotone was helpful to obtain a preliminary understanding of variation in soil environmental characteristics in the oasis-desert ecotone.

There was a significant difference in soil moisture of each layer between farmland and control plots within depths of 0-100 cm due to irrigation. The soil moisture of each layer of the farmland plot during the farming period (i.e. April-September) was generally higher than that of the same layer of the control plot. The agricultural soil moisture showed a time-variation rule from multimodal to unimodal with increased depth. The soil moisture of the control plot showed a generally monotonic increasing trend with increased depth. However, the farmland plot showed a unimodal increasing trend of initial increase and then a decrease with increased depth, with the peak value at 50-60 cm during July-September and at 60-80 cm during other months.

Under the preconditions of inputs of certain substances and energy, soil nutrient conditions of farmland were obviously improved, and the soil nutrient index was significantly higher than that of the control plot. The improving effect of farmland reclamation on soil nutrient conditions in the oasis-desert ecotone was limited however, and such an improving effect decreased with increased depth. For both farmland and control plots, the soil nutrient composite indexes at 0-20 cm were clearly higher than those of other layers within 20-100 cm; but the soil nutrient content in both farmland and control plot in oasis-desert ecotone is not high compared with other regions in China. Cultivation and management had a positive impact on soil particle-size distribution in the oasis-desert ecotone: silt contents and clay contents in the farmland soil obviously increased while sand contents decreased.

Fig. 1 .

 1 Fig. 1. Seasonal variation of soil moisture at different depths of two plots

Fig. 2 .

 2 Fig. 2. Vertical variance of soil moisture of two plots during different periods

Fig. 3 .

 3 Fig. 3. Composite index of soil nutrient in every layer within 1-m depth of the farmland plot (taking control plot as baseline)

Fig. 4 .

 4 Fig. 4. Composite index of soil nutrient in every layer deeper than 20 cm of both plots (taking 0-20 cm as baseline)

Table 1 . Soil moisture discrepancy test at different depth of two plots

 1

	Depth(cm)	Between plots (t-test) Sig. (2-tailed)	Within plot (analysis of variance) FP CP
	0-10	0.026 *	3.97±1.77 e	2.60±0.73 f
	10-20	0.002 **	6.10±2.31 de	3.38±1.06 ef
	20-30	0.000 **	7.67±2.67 de	3.85±0.87 de
	30-40	0.000 **	9.06±3.18 cd	4.24±0.81 cd
	40-50	0.000 **	10.70±4.04 bc	4.63±0.91 bcd
	50-60	0.000 **	13.16±5.02 ab	4.95±1.14 abc
	60-80	0.000 **	14.83±4.19 a	5.17±1.02 ab
	80-100	0.000 **	12.03±3.53 abc	5.50±1.13 a
	F Value		13.383 **	12.193 **

Table 2 . Soil nutrient attribute mean and variance analysis of every layer of two plots (mean ± SD)

 2

	Plot	Depth (cm)	SOM(g/kg) TN(g/kg)	TP(g/kg)	TK(g/kg)	AN(mg/kg)	AP(mg/kg)	AK(mg/kg)
		0-20	4.15±0.08a	0.23±0.01a	0.61±0.02a	23.87±0.32a	10.60±0.87a	15.61 ±5.02a	118.33±13.58b
		20-40	3.27±0.36ab	0.16±0.02b	0.59±0.02a	23.77±0.34a	2.77±2.20c	5.54 ±0.38b	153.67±31.21b
	FP	40-60 60-80	2.68±0.68ab 2.80±0.39b	0.14±0.06b 0.14±0.04b	0.57±0.06a 0.59±0.05a	24.48±0.98a 24.78±0.61a	6.07±1.07b 4.73±1.20bc	1.61 ±0.40b 4.71 ±0.32b	198.00±36.43a 129.33 ±4.16b
		80-100	2.41±0.77b	0.12±0.03b	0.59±0.04a	23.78±0.67a	3.54±0.88bc	3.15 ±0.34b	137.33 ± 9.64b
		F Value	5.284 *	4.667 *	0.448 ns	1.641 ns	15.948 *	21.782 *	6.024 *
		0-20	2.20±0.24a	0.10±0.01a	0.57 ±0.01a	23.60±0.04a	1.67±1.13 b	2.33±0.96a	154.67±25.72a
		20-40	2.11±0.20a	0.11±0.02a	0.58 ±0.02ab	23.78±0.27a	2.43±1.56 b	0.92±0.30b	135.67±13.32ab
	CP	40-60 60-80	2.03±0.10a 2.02±0.22a	0.10±0.02a 0.11±0.01a	0.54 ±0.01bc 0.54 ±0.02bc	23.58±0.66a 24.17±0.52a	9.39±2.21 a 4.63±1.02 b	0.67±0.14 b 1.11±0.22 b	115.67 ±7.51b 113.33 ±4.58b
		80-100	2.21±0.31a	0.11±0.01a	0.53 ±0.01c	23.08±0.96a	3.14±1.66 b	1.15±0.16 b	121.33 ±7.51b
		F Value	0.486 ns	0.306 ns	5.680 *	1.345 ns	11.455 *	5.514 *	4.179 *

Table 3 . Soil size distribution of both plots (mean ± SD)

 3

	Plot	Depth/cm	Clay (0.35-2.0μm)	Soil size composing /% Silt (2.0-50μm)	Sand (50-2000 μm)
		0-20	1.84±0.41	28.05±4.28	70.11±4.65
		20-40	1.95±0.38	27.70±5.34	70.35±5.69
	FP	40-60	1.67±1.11	25.55±10.31	72.79±11.26
		60-80	1.87±0.38	26.54±4.72	71.60±5.02
		80-100	1.97±0.27	26.14±5.96	71.89±6.20
		0-20	1.13±0.90	20.95±4.33	77.92±5.12
		20-40	1.69±0.85	24.95±5.32	73.36±6.12
	CP	40-60	1.85±0.40	24.51±5.23	73.65±5.62
		60-80	1.59±0.91	23.78±5.18	74.63±5.76
		80-100	1.86±0.51	25.23±6.63	72.91±7.14

Acknowledgements:

The project was supported by the National Basic Research Program of China (973 program 2009CB421302) and National Natural Science Foundation of China (NO.41001171). The authors also thank the anonymous reviewers for their valuable comments.