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The evaluation of soil quality is an important activity for recognizing the sensitivity of the soil to damage and the need to consider the sustainable use of soils [35]. Defining soil quality without reference to the function of the soil is not possible because for a soil that is a good quality for one purpose may be a poorer quality for another purpose. Therefore, soil quality is not considered with respect to one function alone but with respect to its multifunctionality because of possible change of the land use in the future. The attribute measured as an indicator must be fit for the purpose of indicating soil quality, and the method of analysis must be fit for the purpose of providing appropriate information about the attribute [35].

A Case study for Soil Quality in Olive and Vineyard Fields. In agriculture, soil quality assessment is only meaningful when the results are used to maintain and improve soil quality. Becoming familiar with how to define and evaluate soil quality, and the common causes for declines in soil quality are important in managing it. Collecting a minimum data set helps to identify locally relevant soil indicators and to evaluate the link between selected indicators and significant soil and plant properties [1]. Soil quality index can be derived using a minimum data set of indicators (Figure 1).

Figure 1. Soil quality index derived from physical, chemical and biological indicators (Adapted from [24])

Doğan and Gülser (2012) studied the classification of soil quality for olive and vineyard fields of five different villages (Akçaköy, Çatalca, Efemçukuru, Görece and Yeniköy) in Menderes district of Izmir, Turkey. Soil samples were taken from 19 different olive fields and from 28 different vineyard fields. They classified the soil physical and chemical properties between 1.00 (ideal) and 0.20 (poor) according to soil requirements given in literatures for olive and vineyard (Table 6 and 7, respectively).

They used the following equation for evaluate the soil quality for olive and vineyard fields.

S na1.a2.a3... an

Where; S: Soil quality index; a1…an: Score of each soil parameter between 0.20 and 1.00.

Soil quality index (S) for olive and vineyard fields was classified as;

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Table 7

Suitable classes of soil properties for vineyard growth (Doğan and Gülser, 2012)

They found that only one of the 28 soil samples taken from vineyard fields was in very suitable (S1) class, 5 in suitable (S2), 9 in marginal suitable (S3) and 13 in non suitable (N) class. Restricting factors for vine growth in soils classified as S2 and S3 generally became lower pH, OM, P, Fe, Mn, Cu, Mg and K contents than suggested levels. In addition to restring factors in S2 and S3 classes, physical properties in soils classified as non suitable (N) were lower than suggested levels. Soil quality index values showed a significant positive relationship with vineyard yields at 1 % level (Figure 4).

Conclusion. Soil organic matter is one of the most important factors affecting soil quality with regulating physical, chemical and biological soil properties. To improve soil quality with increasing SOM content, stimulating biological activity, improving soil structure and reducing erosion, some soil management practices can be suggested as follows;

i)reducing tillage to slow decomposition of crop residues,

ii)avoiding the urge to soil work when it is wet, it causses soil compaction, degredation of soil structure and low crop yield,

iii)using crop rotations include legumes and deep-rooted and high residue crops to add nitrogen, recycle nutrients,

iv)adding organic soil amendments (like manure, agricultural wastes, compost, biosolids green manure etc.)

v)avoiding a bare soil surface. Cover plants and mulches increase SOM, recycle nutrients, reduce runoff and erosion.

An assement of soil quality can be a good for a specific soil management or specific crop production, but it can be a poorer for other managements and crops. Therefore, it is important that evaluation of minimum data set of soil quality indicators must be considered according to the basic agricultural practice or crop patern.

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UDC 631.95:577.15:311.3

Tayfun Aşkın

Ordu University, Faculty of Agriculture, Department of Soil Science

and Plant Nutrition, 52200, Ordu, Turkey

ARYLSULPHATASE ACTIVITY IN AGRICULTURAL ECOSYSTEMS:

A GEOSTATISTICAL APPROACH

Abstract. Analysis and interpretation of soil survey data are very important for effective management of agricultural ecosystems. Information on soil enzyme activities was used to determine soil biochemical characteristics for soil quality and health. Enzymes in soils originate from animal, plant and microbial sources and the resulting soil biological activity. Soil sulphatases play a major role in the mineralization processes of organic sulphur substrates includes the metabolic processes of all these organisms. The activity of soil sulphatases may be evaluated statistically due to application of geostatistical methods to soil science. The objective of this study was to assess the spatial variability of soil arylsulphatase activity (ASAc) within surface agricultural soils using the geostatistical techniques. The ASAc at the same transect of agricultural soils in

Gümüşhacıköy was determined using some soil properties. Thirty-nine surface soil samples (0-20 cm) were collected from agricultural areas and analyzed. The arylsulphatase activity varied from 0.11 to 4.29 g p-nitrophenol g-1 respectively. A spherical model was the best-fitted semivariogram model for ASAc. Semivariograms for ASAc exhibited spatial dependence with a range of influence approximately 21.5 km.

Key words: spatial variability, arylsulphatase activity, site specific management.

Introduction. Soil is a complex system of in chemical, physical and biochemical properties factors to date attention is given to soil functionality, such as enzymatic activity essential for elemental transformation which can be measured by determining both physico-chemical parameters and biochemical activities simultaneously [4]. Biochemical actions are dependent on or related to the presence of enzymes. Many reactions involving soil organic matter transformations may be catalyzed by enzymes existing outside the microorganisms [10] and plant root system [24, 9]. Each soil may have a characteristic pattern of specific enzymes as described by Kuprevich and Scherbakova (1971). The differences in the level of enzymatic activity are caused primarily by the fact that every soil type [6], depending on its origin [14] and developmental conditions, is distinct from every other in its content of organic matter, in the composition and activity of living organisms inhabiting it, and consequently, in the intensity of biological processes. Obviously, it is probable that each type of soil has its own inherent level of enzymatic activity [5, 15, 1].

Large proportions of the sulphur in many soils are organically bound and the mineralization of these portions is of agricultural importance [20]. Organic sulphur compounds in soil can constitute huge of total sulphur and the assimilation of this sulphur by plants and microorganisms is preceded by soil enzymes. Several enzymes are involved in the decomposition of organic sulphur compounds. Those enzymes that hydrolyze S esters are commonly called sulphatases. Arylsulphatase is important in nutrient cycling because it releases plant available SO4. Also, it may be indirect indicators of fungi contain ester sulphate, the substrate of arylsulphatase [3].

Classic statistical procedures assume that variation is randomly distributed within sampling units. Actually soil properties are continuous variables whose values at any location can be expected to vary according to direction and distance of separation from neighboring samples. Recently, emphasis has been placed on the fact that the variations

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of a soil property are not completely disordered over the field and this spatial structure must be taken into account in the treatment of the data. Investigators have shown increasing interest in analyzing measured soil parameters for their interdependency over space, i.e., to study the dependency of a measured parameter on location in the field. Typically semivariograms and outocorrelogram have been used to study the spatial structure of soil properties.

The objective of present study was to assess the spatial variability of soil arylsulphatase activity; analysis of the spatial structure was based on the semivariogram analysis within an urban area.

Material and Methods

Study Site and Design

The study site is on the urban area in Gümüşhacıköy, Amasya, in the northwest

Turkey (400 53' N; 350 13' W). The primary grid consisted of 39 points spaced 4500 by 4500 m (Figure 1). The surface soils (0-20 cm depth) were sampled in the spring of 2001. Annual mean of precipitation is 400 mm and temperature is ranged from - 10 0C to 38 0C. Bulk soil samples were air dried and then crushed to pass through a 2 mm sieve.

Soil Analysis

Selected soil physico-chemical properties were determined by means of appropriate methods: soil particle size distribution by the hydrometer method, pH in 1:2.5 (w/v) in soil:water suspension by pH-meter, soil organic matter by modified WalkleyBlack method, CaCO3 content by Scheibler Calcimeter, cation exchange capacity (CEC) by Bower method [17].

Arylsulphatase (EC 3.1.6.1) activity (ASAc) was measured by the method of Tabatabai and Bremner (1970). To 1 g of soil, 0.25 ml toluene, 4 ml acetate buffer (pH 5.5) and 1 ml of 0.115 M p-nitrophenyl sulphate (potassium salt) solution were added and the samples were incubated for 1 h at 37 0C. The formation p-nitrophenol was determined spectrophotometrically 410 nm and results were expressed as g p-nitrophenol g-1 dry soil.

Statistical Analysis. All geostatistical analyses were performed on a PC using GS+ package program [8]. Descriptive statistics were determined using SPSS package program [21].

Figure 1. Location of the sampling sites in Gümüşhacıköy, Amasya

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Results and Discussion

Soil Properties. Some descriptive statistical results for selected soil physical and chemical properties are given in Table 1. The results can be summarized as; soil samples have mostly moderate coarse in texture, alkaline in pH, moderate in organic matter (average of 2.23 %), low in lime content (average of 2.35 %), and free alkaline problem (ESP 15 %) [19] (Table 1).

Sd; standard deviation, Se; standard error

Spatial Variability of Arylsulphatase Activity. Distance on arylsulphatase activity, pairs and semi variance values were calculated using GS+ package program (Table 2). The spherical model, had the smallest Reduced Sums of Squares (RSS) value and the biggest r2 value, was selected for spatial variability of the arylsulphatase activity in the study area by this computer package program [8].

The spatial distribution and model parameters of soil arylsulphatase activity in the study area are schematically illustrated in Figure 2. The zone of influence for arylsulphatase activity was approximately 21.5 km (Table 2).

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