Assessment of spatial variability of soil properties in areas ... - wseas

Recent Researches in Environmental Science and Landscaping

Assessment of spatial variability of soil properties in areas under land use change due to the Alqueva dam construction THOMAS PANAGOPOULOS, RĐTA ANDRADE, VERA FERREIRA, CARLOS GUERRERO University of Algarve, Campus de Gambelas, 8005-139 Faro PORTUGAL [email protected]; [email protected]; [email protected] Abstract: - Land conversion from montado traditional ecosystem to intensive agricultural use may alter soil physical, chemical and biological properties depending on duration and the type of soil management practice. The objectives of this study were to evaluate effects of the increase in intensive cultivation practices on some soil chemical and physical properties and to characterize spatial variability of soil properties. The study area is located at Portel, Portugal and the land use changes to more intensive soil management occurred due to the recent Alqueva dam construction. Soil pH, electrical conductivity (EC), organic matter (OM), hydraulic conductivity and soil texture were analyzed in soil samples collected from a permanent pasture at a montado type agroforestry area and from an intensively cultivated irrigated field converted from a native grassland. In addition, spatial variability of the soil properties under each land use were defined using statistical and geostatistical analysis. Soil tillage caused significant changes in soil properties. Soil organic matter and silt content decreased significantly while EC, pH and clay content increased with intensive cultivation, while sand content was the same. The variation of the soil variables was fairly homogenized in the cultivated field compared to the montado grassland. Keywords: soil properties; soil variation; spatial dependency; land use change. unsuitable changes in soil quality. The changes in the soil quality and its effects to the ecosystem have been studied by some researchers [1,2]. The mean hydraulic conductivity is much lower in intensively cultivated areas compared to pasture [3]. Intensive soil tillage systems lead to change of soil properties [4]. The most important effect of soil tillage is the decreases of cation exchange capacity (CEC) which is attributable to the reduction of OM [5]. Soil organic carbon and total nitrogen decreased in cultivated soils compared to pasture [6]. Soil physical and chemical properties are strongly influenced by soil management systems and changes in land use [7]. The greater the intensity of irrigation the higher pH and electrical conductivity were found [8]. Soil properties vary spatially from a field to a larger regional scale affected by both soil forming factors and soil management practices, fertilization, and crop rotation [9]. The variation is a gradual change in soil properties as a function of landforms, geomorphic elements, soil forming factors and soil management [10]. Geostatistical methods have been used for predicting spatial variability of soil properties [11,12]. Conversion of native grassland to intensively cultivated lands has been increased in the recent years in the Alqueva dam watershed area. The objectives of this study were to evaluate effects of the land use

1 Introduction Research on soil erosion and its effect on agricultural productivity started in 1930s when soil scientists began to develop a quantitative procedure for estimating soil loss in the Corn Belt in the United States. Several factors were introduced to a soil loss equation, in which soil properties, slope and practice were primarily considered. The RUSLE P-factor reflects the impact of support practices on an the average annual erosion rate. It is the ratio of soil loss with contouring or strip cropping to that with straight row farming up-and-down slope. Management practices have greater effect on the degree of changes in soil properties. As with the other factors, the Pfactor differentiates between cropland, rangeland or permanent pasture. In general it was considered that as more infrequently the mechanical disturbance or other the support operation being performed the better is for the P factor and consequently the risk of erosion. Consequently, soil management systems play an important role in sustainable agriculture and environmental quality. Conversion of an area from native extensive use ecosystem, like the montados of Portugal, to intensively cultivated land may be the reason of soil degradation and decrease of land quality. Soil management systems such as soil tillage, fertilizers and extreme irrigation fairly often create

ISBN: 978-1-61804-090-9

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change on some soil chemical and physical properties and the spatial variability of these properties.

were planted 3 years ago, but the main land use is montado type silvopastoral system of holm oaks. Part of the property (655ha) is hunting permit tourist area to Sociedade Agro-Florestal do Panasquinho, Lda. (Portaria n.º279, 2007). The first part of the study area was an irrigation area on which was produced Lucerne (Medicago sativa) four times a year (Fig 1, B). Lucerne nutritional for cattle once dried, and it incorporates nitrogen in the soil, enhancing its nutrients. The cultivated field have been under conventional tillage system including plough (about 20 cm depth) in fall, fallowing cultivator (about 15 cm depths) and disc harrow (about 10 cm depths) subsequent to soil tillage. Inorganic fertilizers were applied to the cultivated field at a rate of 100 kg N ha-1, 100 kg P ha-1, and 100 kg K ha-1 as subsequent with combination of animal manure (30 tone ha-1). In that part of the property were collected 27 soil samples. The second part of the study area was located to part of the property suffered a wildfire 4 years ago, and now is a grassland with low density holm oaks (Fig 1, A). In that part of the property was collected 25 soil samples from grassland under holm oaks montado. The grassland on the montado is used as permanent pasture for the cattle. Tillage was done only once every 10 years to decrease shrub competition (at about 15 cm depths). However, the native grassland on montado did not expose to any fertilizer.

2 Materials and methods 2. 1 Study area The landscape of Alqueva area is characterized by its hilly topography with significant altitude variations (mainly heights between 100 and 200 meters), extensive land use types with low population density. The Iberian Peninsula agro-sylvo-pastoral systems are characterized by savannah-type low density woodlands, with evergreen oak species and are called "montado" in Portugal and "dehesa" in Spain [13]. This study was conducted on the two contrasting land uses (native montado type agroforestry system and an intensively cultivated land). The study are is located in a semiarid region, with extensive dry periods and eventually heavy rains, and water supply was a concern for this area. The Alqueva dam, the largest in Portugal, and the Western Europe, with a surface area of 250km2, an extend of 83km and a storage capacity of 4.14km3 was constructed on the Guadiana river in Alentejo near Spain [14]. This dam was constructed as part of a multipurpose hydraulic project to provide water for irrigation, drinking, power generation and leisure activities. Regarding the irrigation project, the reservoir is connected to several other smaller reservoirs, within an elaborate irrigation scheme [14]. Due to Alqueva’s irrigation scheme, land use is changing in this area, from extensive cultures, like Montado, to intensive irrigation cultures. The soil of the study area is rocky and according to World Reference Base for Soil Resources (WRB, 2006), the two types of soil in this area are: Haplic luvisols (LVha) and Lithic leptosols (LPli). The vegetation follow the phytosociological series of Pyro bourgaenae - Querceto rodunfoliae sigmetum, which is adapted to poor soil conditions and extensive dry season. The most common tree species are Quercus rotundifolia, Quercus suber, Pyrus bourgaena and Doronicum plantaginuem. The main cultures are olive tree, wheat and vineyard. The case study is the private property "Quinta dos Gregos", with 900 ha, located close to Portel, Portugal (Fig. 1). Due to its proximity to Alqueva’s water reservoir the agroforestry property didn’t have to wait for the irrigation schemes to start using the abundant water due to dam. Direct pumping started about 10 years ago, with two major pumps of 250 horse power, at a distance of 1,5km from the reservoir. The property include several cultures of irrigated olive tree, from which the more recent ones

ISBN: 978-1-61804-090-9

Figure 1- Location of the study area.

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2.2 Soil Sampling Measurements

and

at the study area. The range is the distance at which is reached the sill, that is the lag-distance at which the semivariogram flattens, it is the value at which one variable becomes spatially independent. The nugget to sill ratio quantifies the importance of the random component and provides a quantitative estimation of the spatial dependence. of a measured parameter [11]. According to the value of this ratio, a parameter can be considered weak, moderate or strongly spatial correlated (table 1). The semivariogram is a point graphic and for modelling the spatial behaviour it was fitted standard models using the lowest possible RSE (Root Square Error). Spatial variation has been characterized using different models (spherical, circular, etc.) fitting the semivariograms. Choice of the best fitting model was based on the lowest RMSE (root mean square error) and confirmed by a visual inspection. Crossvalidation and ordinary kriging have been applied to extrapolate the values of unsampled field parts.

Laboratory

The soil samples were collected from 0 to 20 cm depth in each study area (Luzerna cultivated field and Montado native grassland). In order to predicting variations in short distances, the soil samples were collected from the total of 25 and 27 locations for each study area respectively, with 100 m interval. The 52 collected soil samples were air-dried and then dried for about 6 hours at 40ºC, and passed through a 2-mm sieve to remove rocks or limestone concretions [15]. The particle-size distribution was determined by Bouyoucos hydrometer method [16]. Soil organic matter content was determined using the Walkley and Back method [17] wet oxidation procedure. Soil pH and electrical conductivity were measured with glass electrode in a 1:2.5 soil/water suspension [18].

2. 3 Statistical and Geostatistical Methods Mean, variance, and coefficient of variation (CV) were computed for each soil properties. Normality test was performed to test the hypothesis assuming each property has a normal variable distribution and in the variables, and these variables without normal distribution subjected to log-transformation. Soil properties in each field were compared with Duncan’s test. Linear correlations among soil properties were also determined. The ArcGIS and geostatistical analyst software (ESRI Ltd) was used to construct semivariograms and spatial analysis for the variables. The semivariogram (SV) determines how samples are related to each other in space, this description is based in equation 1.

γ(h ) =

1 N (h ) ∑ [Z i − Z i+h ] 2 2

Table 1 – Relation between the value of the nugget/sill and the spacial dependence (Modified from: Cambrardella [11]. Value of Nugget/Sill Spatial dependence Weak Moderate-Weak

0.25 – 0.50

Moderate-Strong

< 0.25

Strong

3 Results and discussion

(1)

Soil properties were quite different in the two land uses. Mean organic matter was significantly higher in the native grassland (5.2%) as compared to the cultivated field (2.1%) (Table 2). This confirm other studies that soil organic carbon was higher at notillage soils compared with minimum tillage [19]. Some researchers reported that the highest organic matter content was found in grasslands compared to agricultural fields [5,20]. The depletion of organic matter in the cultivated field can be associated with the intensive tillage and the removal of plant residue. The decrease in organic matter may result in increase to risk of erosion according to the RUSLE model. The soil pH (7.1) was significantly higher in the cultivated land compared to the montado grassland (5.9). According to Chatterjee and Lal [21] minimum tillage soils had a higher soil pH values than plow tillage soils. The soil pH in the cultivated land was greater due to high salt concentration of the irrigation

The most related samples have lower values of variance (γ(h)). N(h) is the number of samples that can be grouped using vector h; Zi represents the value of the sample; Zi+h is the value of another sample located at a distance ||h|| from the initial sample Zi. The semi-variogram is the graphical representation of the spatial relation between the samples, where the Y axis accommodates values of variance (γ(h)) calculated in Eq. [1], while the X axis holds the distance values of the grouping vector h. The SV was calculated for all the measured parameters. The spatial structure of each variable has been defined from semivariogram parameters: nugget, sill and range. Nugget is the variance at distance zero and represents the sampling error. Sill is the semivariance value at which the semivariogram reaches the upper bound after its initial increase, it is the variance in which the samples are not no longer spatially related

ISBN: 978-1-61804-090-9

> 0.75 0.50 - 0.75

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both fields. The decrease in clay content can explain with vertical eluviations of clay with the dense irrigation or removal of clay by runoff [20].

water. EC declined slightly at the montado grassland field compared to the intensive cultivation. The intensively cultivated field had the lowest mean clay content (12.88%) while sand content was the same in

Table 2. The descriptive statistics and changes of the studied soil properties for the Montado grassland and the Lucerne intensively cultivated field. Variable Mean S.D. C.V. Minimum Maximum Lucerne Cultivated Field OM (%) pH EC (µS/cm) Clay (%) Silt (%) Sand (%)

2,080 7.13 107.14 12.89 33.74 53.36

1.097 0.306 49.212 3.829 8.993 9.414

1.20 0.09 2421.86 14.67 80.88 88.62

0.45 6.53 40.50 5.25 7.99 37.72

5.44 7.85 205.00 21.88 46.93 80.75

Montado grassland OM (%) pH EC (µS/cm) Clay (%) Silt (%) Sand (%)

5.21 5.89 100.64 16.89 29.53 53.58

1.676 0.253 38.318 6.525 5.112 7.163

2.81 0.06 1468.26 42.57 26.14 51.31

2.25 5.38 55.50 5.28 12.99 40.08

10.35 6.30 217.50 29.22 39.72 70.74

Table 3. Spearman’s correlation matrix for soil properties. Variable Lucerne Cultivated Field OM (%) pH EC (µS/cm) Clay (%) Silt (%) Sand (%) Montado grassland OM (%) pH EC (µS/cm) Clay (%) Silt (%) Sand (%)

OM

pH

EC

Clay

Silt

Sand

1.000

-0.238 1.000

0.681** -0.071 1.000

0.145 0.308 -0.031 1.000

-0.168 0.110 -0.360 -0.101 1.000

0.102 -0.231 0.357 -0.311 -0.914** 1.000

1.000

0.147 1.000

0.401* -0.348 1.000

0.013 0.248 0.217 1.000

-0.137 -0.229 0.126 -0.248 1.000

0.094 -0.147 -0.185 -0.791** -0.331 1.000

* Significant at the 0.05 probability level, ** Significant at the 0.01 probability level

The variation coefficient (CV) was the lowest for soil pH and the highest for EC among the soil

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properties. The correlation coefficients between sand and clay, and OM and EC were significant (p