Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China

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1 Geoderma 115 (2003) Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China Bo Sun a, *, Shenglu Zhou b, Qiguo Zhao a a Institute of Soil Science, Chinese Academy of Sciences, P.O. Box 821, Nanjing , China b Department of Urban and Resources Sciences, Nanjing University, Nanjing , China Abstract Studies on the effect of land use alteration on the spatial variability of soil properties are limited. This study addressed the spatial and temporal variability of soil properties and changes of soil quality in a hill region of subtropical China using geostatistical methods. Soil samples from 0- to 15-cm depth were collected within 105 locations, on a m grid basis over a 112-ha field, in 1985 and 1997, respectively. Soil properties showed large variability, with the highest coefficient of variation being observed for available P, the lowest for soil ph. Over the 12-year period, a significant decrease of soil organic matter appeared with original land use patterns of wasteland and paddy field, whereas other properties showed no significant changes. In addition to the alteration of wasteland, fertilization in upland increased available P and K, whereas the opposite appeared in forest restoration system. Changing wasteland into paddy field was found to increase soil fertility. A geostatistical analysis showed that all the soil properties (ph, organic mater, available P and K) and their changes between 1985 and 1997 were spatially structured. The nugget-to-sill ratio indicates a strong spatial dependence for soil ph, and a moderate spatial dependence for other properties. The ranges for soil properties in 1985 were equal to or larger than the diameter of the hills. Changing the land use patterns decreased the ranges for soil chemical properties. The ranges for soil ph and available K were similar to the radius of hills. Interpolation using kriging showed a spatial similarity among the soil properties. Soil properties decreased in the southeast quadrant of the research area where the land use was paddy field, while they increased around the center where arable upland was initially from wasteland. Thus, we propose a process to evaluate soil quality using the geostatistical methods as a potential analysis tool for monitoring changes at a farm scale. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Soil quality; Spatial temporal change; Geostatistics; Hill region; Subtropical China * Corresponding author. Fax: address: bsun@issas.ac.cn (B. Sun) /03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi: /s (03)

2 86 B. Sun et al. / Geoderma 115 (2003) Introduction Soil properties vary spatially and temporally from a field to a larger region scale, and are influenced by both intrinsic (soil formation factors, such as soil parent materials) and extrinsic factors (e.g., soil management practices, fertilization, and crop rotation). The heterogeneity and variation of soil properties should be monitored and quantified for a better understanding of the influence of such factors as management and pollution, and finally for leading to more efficient farming practices. Previously, changes in soil properties have been monitored through long-term field experiments, like Rothamsted Classical Experiments (Johnston et al., 1986) and the Long-term Ecological Research Program (Risser, 1991). However, this method is time-consuming and in many cases too expensive to be affordable. Moreover, the greater spatial variation makes it difficulty to evaluate the temporal changes of soil properties in a short term. The geostatistical methods consider the spatio-temporal variation of soil properties as a random process depending on both the time and space (Goovaerts, 1999). In the last two decades, the application of geostatistical methods by soil scientists focused on predicting spatial variability of soil properties with different kriging methods over small to large spatial scale (Yost et al., 1982; Trangmar et al., 1987; Miller et al., 1988; Voltz and Webster, 1990; Chien et al., 1997; Tsegaye and Hill, 1998; Lark, 2002). Recently, there has been increasing concern about how to estimate temporal changes of spatially varying soil attributes (Papritz and Webster, 1995; Heuvelink et al., 1997). Papritz and his colleagues used a co-kriging method with the pseudo-cross variogram to estimate temporal changes of spatially autocorrelated soil properties. These researches were focused on the methodology of geostatistics at plot scale. Moreover, the effect of undulating topography on the geostatistical analysis was seldom taken into account. Soil quality is defined as the capacity of a soil to sustain biological production within ecosystem boundaries, to maintain environmental quality, and to promote plant and animal health (Doran et al., 1994). Soil quality index is the main method to assess soil quality. Some approaches, e.g. multiple variable indicator kriging (MVIK), are used as an integrated index of soil quality (Doran et al., 1994; Doran and Jones, 1996). So far, no geostatistical case study has been reported on evaluation of spatial and temporal changes of soil quality, especially at farm and regional scale. The objectives of this study were (i) to characterize spatial and temporal variability of soil properties, (ii) to evaluate the effect of land use changes on the variability of soil properties at farm scale, (iii) to assess the suitability of geostatistical analysis for monitoring changes of soil quality in a hill region of subtropical China. 2. Materials and methods 2.1. The study area The experiment was conducted at Ecological Experiment Station of Red Soil of the Chinese Academy of Science (EESRS) covering an area of 112 ha. It is located in Yujiang county, Jiangxi province, P.R. China (28j15VN, 116j55VE). EESRS has a subtropical moist

3 climate with an annual rainfall of 1785 mm and a mean annual temperature of 17.8 jc. The landscape is dominated by 35- to 55-m hills with a 5 8j slope (Fig. 1). The main soil type is red soil (Haplic Acrisol, according to FAO soil classification) derived from Quaternary red clay, apart from a small area of paddy soil (Fluvisol or Cambisol, according to FAO soil classification). The main vegetation is sparse pine (Pinus massoniana), and this land use pattern is referred to as wasteland in this study Soil sampling and chemical analysis B. Sun et al. / Geoderma 115 (2003) Sampling distance is dependent on the extent of research area. Zhou et al. (1996) investigated the spatial variability by a small grid of m over a m field in the same region, but larger grid is suitable for a larger area. In this study, soil samples from 0- to 15-cm depth were collected on 105 locations on a m grid over a field measuring km 2, in 1985 and 1997, respectively (Fig. 2). Bulked soil samples were collected with a soil auger within a radius of 30 cm at each location. The samples were air-dried and ground to pass through a 2-mm sieve. Soil organic matter was determined by dichromate-wet combustion method. Soil available K was extracted with 1 mol l 1 NH 4 Ac, and then measured by an atomic absorption spectrometer (ISSCAS, 1978). Soil available P was extracted with 0.5mol l 1 NaHCO 3 at ph 8.5 using the method of Olsen et al. (1954). Soil ph was measured with glass electrode in a 1:2.5 soil/water suspension. Among 105 sample points, only 10 locations were under paddy field in 1985, others were under wasteland. In 1997, 10 locations of wasteland had become woodland (WF), 34 arable upland (WU), 9 paddy field (WP). Other wasteland (42, WW) and all original paddy field (10, PP) had not changed. Fig. 1. Topography in the study area.

4 88 B. Sun et al. / Geoderma 115 (2003) Fig. 2. Sampling site and land use pattern on the study area in Statistical analysis Mean, standard deviation (S.D.), and coefficient of variation (CV) were computed for each soil parameter with the software package STATISTICA (StatSoft, 1995). The basic theory of geostatistics has been well established (Journal and Huijbregts, 1978) and reviewed (Trangmar et al., 1985). Experimental variogram estimator is asymptotically unbiased for any intrinsic random function, however it is very sensitive to outlying values because it is based on squared differences among data. Apart from identifying outliers by exploratory data analysis, robust variogram (Eq. (2)) is a good estimator to reduce the sensitiveness to outliers (Cressie and Hawkins, 1980): where c(h)is the sample semivariance between all observations, Z(X i ); N(h) is the ( ) XNðhÞ ZðX 2NðhÞ i Þ ZðX i þ hþ cðhþ ¼ i¼1 0:457 þ 0:494 NðhÞ number of pairs of Z(X i ) over a separate distance (lag) h. The stationary models, i.e. gaussian (Eq. (2)), exponential (Eq. (3)) and spherical equation (Eq. (4)), was fitted to experimental semivariograms: cðhþ ¼C 0 þ C 1 1 exp h2 a 2 cðhþ ¼C 0 þ C 1 1 exp h a ð1þ ð2þ ð3þ

5 B. Sun et al. / Geoderma 115 (2003) h h3 cðhþ ¼C 0 þ C 1 2a 2a 3 when hva ¼ C 0 þ C 1 when hza ð4þ where C 0 is the nugget, and a is the range of spatial dependence to reach the sill (C 0 + C 1 ). The software package S-PLUS 2000 (Mathsoft, 1996) was used to perform all geostatistical computations. 3. Results and discussion 3.1. Variation of soil properties between different land uses Table 1 shows the mean, standard deviation, coefficient of variation, minimum and maximum values for each of the soil properties determined. Among the four soil properties, soil available P shows the highest CV, while soil ph the lowest. The change of land use pattern did not affect CV for soil ph and organic matter, but increased CV for soil available K and decreased CV for soil available P. Other researches also documented a lower variance of soil ph compared to other soil chemical properties (Yost et al., 1982; Zhou et al., 1996; Tsegaye and Hill, 1998). Because ph values are on log scale of proton concentration in soil solution, there would be a much higher variability if soil acidity is expressed in terms of proton concentration directly. The CVs of available P and soil organic matter here were higher than other results measured in a small field, such as 78.8% for available P reported by Zhou et al. (1996) in a m field in the same hill region and 8.5% for soil organic matter reported by Tsegaye and Hill (1998) in a m field. However, the larger CV for soil available P is in agreement with study by Chien et al. (1997) in a km field. The larger variance in a larger area could be linked to the heterogeneity of land use pattern, fertilization or erosion. Table 1 Mean, standard deviation (S.D.), coefficient of variation (CV), minimum and maximum values of the tested soil chemical properties Soil property Sample size Mean S.D. CV (%) Minimum Maximum 1985 ph OM (g kg 1 ) AP (mg kg 1 ) AK (mg kg 1 ) ph OM (g kg 1 ) AP (mg kg 1 ) AK (mg kg 1 )

6 90 B. Sun et al. / Geoderma 115 (2003) Although change of land-use patterns will affect the soil chemical properties, the impact could be different for different soil properties. Mean ph values show an increasing trend when wasteland was converted to other land uses, although only in the WF and WP the ph increase was significant (Fig. 3). Mean content of soil organic matter decreased considerably in 1997 in all land use patterns compared to 1985, with the decrease being significant in WW and PP. Changing wasteland into woodland significantly decreased soil available P, but into arable upland did the opposite. Although there was an increase of the mean value for available P and K in WP and PP, the increase was not significant according to t-test. Thus, keeping the original land use patterns of wasteland and paddy field had no significant effect on soil ph, available available P and K, but decreased significantly soil organic matter. Soil available P and K increased with fertilization in arable upland, but decreased in forest restoration without fertilization. Changing wasteland into paddy field led to an increase in the mean value of all soil properties. The changes of soil available P and K was associated with the nutrient balance in agroecosysems. Results from the experiment conducted by He and Li (2000) showed, that Fig. 3. Changes of soil chemical properties with land use patterns between 1985 and WW and PP represent keeping the land use pattern for wasteland and paddy field respectively, WF, WU and WP represent change from wasteland to woodland, arable upland and paddy field, respectively. Vertical bars represent S.E.s. Asterisks (* and **) above the columns represent the difference significant at p < 0.05 and p < 0.01 according to Student s t-test.

7 the input-to-output ratio of P and K was 0.43 and 0.41 for wasteland system, 0.86 and 1.65 for mixed forest system, 3.21 and 1.08 for the upland system of citrus interplanting with peanut and green manure, 1.70 and 1.22 for peanut green manure ecosystem, 2.10 and 1.33 for rice rice rape ecosystem, respectively (Table 2). The balance of P and K in different systems was linked to changes of soil available P and K over 12 years. The decrease of soil organic matter could be explained by the little input of litter and organic manure, rapid decomposition and loss with soil erosion of soil organic matter. Our study in EESRS showed a deficit for the balance of organic carbon in these systems (Table 2), which is the reason for the decrease of soil organic carbon in the region Spatial dependence of soil properties B. Sun et al. / Geoderma 115 (2003) Analysis of spatial dependence of soil chemical properties showed an isotropic behavior, which might be caused by a low variability of soil formation factors as well as soil management practices. Semivariogram models and best-fitted model parameters are given in Table 3 and Fig. 4. All soil properties showed positive nugget, which can be explained by sampling error, short range variability, random and inherent variability. The nugget-to-sill ratio can be used to classify the spatial dependence of soil properties. In this study we used similar criteria to those reported by Cambardella et al. (1994). The variable is considered to have a strong spatial dependence if the ratio is less than 25%, and has a moderate spatial dependence if the ratio is between 25% and 75%; otherwise, the variable has a weak spatial dependence. The nugget-to-sill ratio showed a strong spatial dependence for soil ph and soil available K in 1985, which might be attributed to the strong leaching process of soil nutrients in this subtropical region and to the parent material (Quaternary red clay) with a high exchangeable Al and a low exchangeable K (Sun et al., 2000). Soil organic matter and available P were moderately spatially dependent, imprinted Table 2 Nutrient balance of different land use systems (kg ha 1 yr 1 ) Nutrient Item Land-use system Waste land Mixed forest a Peanut green manure Citrus + peanut green manure Rice rice green manure b P Input Output c Balance K Input Output Balance Organic C Formation Runoff loss d Mineralization Balance a Mixed forest is mainly consisted mainly with P. Massoniana and S. Surperba; b Annual yield of rice is kg ha 1 ; c Include the uptake of 14.5 kg P ha 1 by branched and leaves with an annual biomass of 11.4 t ha 1 and the runoff of 0.69 kg P ha 1 ; d Data is from a runoff plot experiment in EESRS during

8 92 B. Sun et al. / Geoderma 115 (2003) Table 3 Parameters for variogram models for soil chemical properties and their changes Property Year Model Range (m) Nugget Sill Nugget/ Sill (%) ph 1985 Spherical Spherical OM 1985 Spherical Gaussian AP 1985 Spherical Spherical AK 1985 Spherical Exponential DpH Exponential DOM Spherical DAP Spherical DAK Exponential by intrinsic (soil-forming processes) and extrinsic factors (soil fertilization and cultivation practices). Due to fertilization soil available K lost the strong spatial dependence in Chien et al. (1997) also reported a similar moderate spatial dependence of soil properties in a10km 2 area. The range of influence is considered as the distance beyond which observations are not spatially dependent. In 1985, this distance ranged from 448 m for soil available P to 757 m Fig. 4. Empirical semivariograms (dots) and the fitted models (lines) of soil properties. The points of blank circle (o), solid circle (.) and blank square (5) represent the empirical semivariograms of soil properties measured in 1985, 1997, and of the changes over 12 years, respectively.

9 for soil ph. These ranges are roughly equal to the diameter of the hills (470 m). This is consistent with the results reported by Miller et al. (1988), who showed that the range of influence for soil properties approximates the diameter of hills. Because of the similar conditions of climate, parent material, and land use in the study area, topography is the main factor of the zones of influence. The decrease of the ranges of the semivariograms for soil properties in 1997 compared with 1987 reflected the influence of soil management practices (Table 3). The ranges for soil ph and available K were similar to the radius of hills, which is usually the length of sloping upland under similar cropping system and management practices. Previous studies showed the range for soil properties approximates burned sites and areas of exposed subsoil on recently cleared forest land (Trangmar et al., 1987). The influence of cultivation on the decrease of range for soil organic matter and available P was smaller than for soil ph and available K. The possible reason is that the little input of organic manure did not increase remarkably soil organic matter, while loss through soil erosion was high in both arable upland and woodland. Although the large input of phosphorous fertilizer increased soil total P, most of the residual P was fixed by these soils which are rich in iron and aluminum oxides. Therefore, the content of available P in most of soils remained at a lower level. In contrast, fertilizer K and lime in arable upland and root-sphere of trees influenced soil available K and ph considerably Kriging of spatio-temporal changes of soil properties When the soil properties are measured at few sampling locations in long time series, the spatial temporal changes of soil properties can be modeled as a multivariate time series. In contrast, if soil properties are observed in a large area at few sampling times, the spatiotemporal changes of soil properties can be simplified by multivariate spatial random processes (Papritz and Flühler, 1994). Papritz and Webster (1995) used co-kriging to estimate the mean temporal changes of soil ph in two simulated fields. Chevallier et al. (2000) described the evolution of spatial structure by semivariograms of the soil C content during 5 years in a plot of 0.4 ha. The latter one is simpler. Table 2 shows that the change values of soil properties between 1985 and 1997 have also a spatial structure (Table 3). Therefore, the spatial and temporal changes of soil properties for unsampled locations can be directly interpolate with classical kriging based on semivariogram of their changes over time. Although the correlation analysis revealed no significant correlation among the observed values, kriging results showed similar spatial structure among the estimated values for soil properties. Soil properties decrease in the southeast quadrant of the research area where the land use was paddy field, while they increase around the center where upland was came from the wasteland (Fig. 5). This illustrates the reliability of kriging estimate directly to the spatial temporal changes of soil properties Evaluation of soil quality changes B. Sun et al. / Geoderma 115 (2003) Evaluations of soil quality were often aimed to particular problems, such as soil erosion, soil pollution and soil nutrient depletion (Doran and Jones, 1996). Soil fertility is

10 94 B. Sun et al. / Geoderma 115 (2003) Fig. 5. Contour maps of kriged changes of soil ph, organic matter (OM, g kg 1 ), available P (AP, mg kg 1 ) and available K (AK, mg kg 1 ) between 1985 and 1997.

11 the key function of soil quality. Based on fuzzy and geographic information system, soil fertility was assessed by the concept of integrated fertility index (IFI) in Southeastern China (Sun et al., 1995). In the previous work, 11 indexes were selected, which were divided into two groups represented, respectively, states of soil nutrients (N) and environments of nutrient supplication (E), i.e. content of soil organic matter, total N, total and available P 2 O 5, total and available K 2 O, cation exchange capacity and thickness of surface layer. The scores of indexes N i and E i were calculated by membership functions, then IFI was calculated by the following equation (Eq. (5)): IFI ¼ Xm i¼1 Wn i N i! B. Sun et al. / Geoderma 115 (2003) X n j¼1 We i E i! ð5þ where Wn i and We i are the weight of indexes in two groups, m and n are the number of indexes in two groups. Finally, mapping soil fertility was based on soil map using ILWIS. According to the previous work and the above analysis, a new method for assessing and monitoring changes of soil quality (fertility) is proposed as follows: (a) collect soil samples with the selected grid according to the extent of the area; (b) select soil quality indicators and analyze soil samples; (c) estimate the value of soil properties in the unsampled area with geostatistical analysis; (d) set up standard scoring functions (SSF) and corresponding thresholds for soil quality indicators and calculate their scores; (e) calculate the soil quality index as the sum of weighted product for each indicator; (f) draw the assessment map of soil quality using a kriging method. More is better type membership function is used to calculate the scores for soil organic matter, soil available P and available K, and an optimum range type membership function for soil ph. As all soil ph values in the research area are lower than 6.5, only former part of the later type membership function is used. Therefore, calculating scores of indicator ph can be simplified as the More is better type membership (Eq. (6)): 8 0 xvl >< x L f ðxþ ¼ LVxVU ð6þ U L >: 1 xzu where x is the monitoring value of indicator, f(x) is the score of indicator ranged between 0 and 1, L and U are the lower and the upper threshold value, respectively. The weight and values L and U for the indicators are determined by the effect of soil properties on plant growth based on our knowledge in the hill region in subtropical China (Sun et al., 1995; Wang and Gong, 1998). L is the value under which the plant growth is severely limited, whereas U is the value over which plant growth is most suitable. L and U

12 96 B. Sun et al. / Geoderma 115 (2003) are 4.5 and 6.0 for soil ph, 10 and 30 g/kg for soil organic matter, 3 and 15 mg/kg for soil available P, 40 and 100 for soil available K, respectively. The weight is 0.35, 0.25, 0.25 and 0.15 for soil organic matter, available P, available K, and ph, respectively. The soil quality index (SQI) is calculated with the following equation (Eq. (7)): SQI ¼ Xn i¼1 W i N i ð7þ where W i is the weight, N i is the score of indicator and n the number of indicator. Fig. 6. Contour maps of kriged soil quality index in 1985 (a) and 1997 (b) and of kriged changes between 1985 and 1997 (c).

13 B. Sun et al. / Geoderma 115 (2003) The changes of SQI (CSQI) are evaluated by the difference (%) between two sampling times (t 2 and t 1 ): CSQI ¼ SQI t 2 SQI t1 SQI t1 100% ð8þ SQI can be divided into three classes (I, II, III). Class I (high quality) is suitable for the growth of plant with an SQI value of >0.6, class II (medium quality) has slight limitations with a range value of between 0.3 and 0.6, class III (low quality) has severe limitations with an SQI value of < 0.3. The kriging map of the SQI in 1985 showed high-quality soils distributed in the southeast quadrant of the area where the land use was paddy field; middle quality in the southwest corner where the land use was arable upland; and low quality in the middle part. However, the high- and low-quality soils changed into the middle quality, while the middle quality soils remained in the same class in 1997 (Fig. 6). Thus, this method allows us to monitor soil quality change through reclamation and cultivation, and to plan sustainable land use at a farm scale. 4. Conclusions The highest variation was observed for soil available P, while the lowest for soil ph. Soil organic matter decreased significantly from 1985 to 1997 even the original land use patterns for wasteland and paddy field were not changed. With the reclamation of wasteland, the fertilization in upland increased soil available P and K considerably, whereas the opposite occurred in forest restoration system. All the soil chemical properties and their differences between two sampling times had a spatial structure. The nugget-tosill ratio revealed a strong spatial dependence for soil ph, and a moderate spatial dependence for other properties. The ranges for soil properties sampled in 1985 were roughly equal to the diameter of the hills. Changing the land use patterns decreased the ranges. The ranges for soil ph and available K were about the radius of hills which is usually the length of sloping upland. The contour map produced by kriging showed a spatial similarity among the estimated values for soil properties. The estimated temporal changes of soil chemical properties showed a highest decrease in the area of previous paddy field, while the highest increase in a upland area changed from previous wasteland. Classic statistical method allows us to analyse the changes of soil properties between different sampling times and land use patterns. Kriging gives a spatial structure analysis and a view of soil quality changes by contour plots. This geostatistical method can be used as an analysis tool for monitoring soil quality changes. Acknowledgements We thank Chen, Z.C. and Wu, X.J. for their work on soil sample collection and location mapping in This work received support through grants from National Key Basic

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