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DIVISION S-6 - SOIL & WATER MANAGEMENT & CONSERVATION

Evaluating Soil Quality–Soil Redistribution Relationship on Terraces and Steep Hillslope

^a Institute of Mountain Hazards and Environment, CAS, Chengdu, Sichuan 610041, and Institute for Application of Atomic Energy Agency, CAAS, Beijing 100094, China
^b USDA-ARS, North Central Soil Conservation Research Lab., 803 Iowa Ave., Morris, MN 56267

* Corresponding author (lindstrom{at}morris.ars.usda.gov)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Soil redistribution from tillage and water erosion have the potential to modify the spatial patterns of soil quality on terraced and steep cultivated hillslopes. However, few studies have investigated this relationship. Our objectives were to quantify soil quality parameters along terraced and steep hillslopes and determine the relationship between soil redistribution from tillage erosion and water erosion on soil quality parameters in the Chinese Loess Plateau. Soil quality indicators, i.e., soil organic matter (OM), available P, N, bulk density (D_b), and clay and silt contents were measured at 5-m intervals on a terraced field and at 10-m intervals on a steep cultivated hillslope in a down slope transect. Soil redistribution rates from tillage and overland flow were obtained by ¹³⁷Cs technique integrated with a tillage erosion prediction model (TEP). Water erosion was the primary cause for the overall decline in soil quality on the steep cultivated hillslope while tillage erosion had a comparable contribution to overall level in soil quality on the terraced hillslope. Soil movement by tillage controlled the spatial patterns in OM, N, and P on both terraced and steep cultivated hillslopes. Selective removal of finer particles by water erosion caused a linear decrease in clay content of 0.02% m^-1 and corresponding increase in silt content of 0.04% m^-1 downslope on the steep cultivated hillslope. The impact of tillage erosion on OM, N, and P on the steep cultivated hillslope can be assessed using the change in adjacent slope gradients (X) through a soil quality-topography regression model, Y = aX + b.

Abbreviations: D_b, bulk density • k, tillage transport coefficient • OM, organic matter • TEP, tillage erosion prediction model

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
TERRACING OF STEEP HILLSLOPES in the Loess Plateau, northern China, has been used extensively for control of water erosion. This area perhaps experiences the most severe water erosion problems in the world. The significant role of downslope soil translocation by tillage on total soil redistribution is becoming clear and has been well documented over the last decade (Lindstrom et al., 1990, 1992; Govers et al., 1994,1996; Quine et al., 1993, 1994; Lobb et al., 1995; Poesen et al., 1997). There is increasing evidence that soil translocation by tillage within terrace boundaries can be the dominant process in soil redistribution (Quine et al., 1993, 1999). The use of ¹³⁷Cs technique with tillage erosion models provide a new perspective to assess the contribution of water and tillage to total soil redistribution within a landscape (Govers et al., 1996; Quine et al., 1993, 1994, 1999; Zhang et al., 1998). To what extent tillage erosion affects soil variability and soil quality remains to a large extent unknown (Van Muysen et al., 1999); therefore, a quantitative evaluation of on-site impacts of soil redistribution because of tillage and water erosion is important for establishing a cause–effect relationship (Pennock, 1998; Lal, 1999, p. 329).

Schumacher et al. (1999) modeled the spatial variation in productivity due to tillage and water erosion for a 50-yr period through an empirical model and the WEPP (Water Erosion Prediction Project) hillslope model. This study demonstrated that soil redistribution from the combined effects of tillage and water erosion results in a net increase in spatial variability of crop productivity and a likely decline in overall soil productivity. Poesen et al. (1997) found that tillage erosion was responsible for the patterns of rock fragment cover that controls the spatial variability of the hydrological response in southeast Spain. Pennock (1998) suggested that tillage erosion should be an important process affecting soil quality and crop productivity in agricultural landscapes.

Despite intensive studies of soil erosion over the last 40 yr on China's Loess Plateau, effects of erosion on soil quality and crop productivity have been mostly neglected because of two reasons. First is the inherent fertility of the loess and homogeneity in distribution of particle-size composition (Liu, 1985). The second reason is the lack of quantitative information on the soil erosion–soil quality relationship across landscape positions. Therefore, in China it has been concluded that soil erosion is unlikely to have serious impact on productivity for the homogenous loess soil (Walling and Quine 1993; Zhang et al., 1997). However, measurements of suspended sediment in the Yellow River of China, indicates that water erosion causes considerable losses of OM, N, P, and other soil nutrients (Zhu, 1984). Although water erosion has exerted a strong influence on soil quality parameters, we are suggesting that soil redistribution by tillage also plays an important role in soil quality parameters across the landscape.

There is a need for evaluating the soil redistribution–soil quality relationship on terraced and steep cultivated hillslopes in the Chinese Loess Plateau. Narrow summit positions and steep linear backslopes (slope gradients up to 40°) characterize the Chinese Loess Plateau. Crop production levels depend primarily on inherent fertility, which emphasizes the importance of tillage erosion in spatial variability in soil quality.

Against this background, studies were conducted on terraced and cultivated hillslopes on China's Loess Plateau. The objectives were (i) to examine spatial patterns of soil quality on terraced and steep cultivated hillslopes; (ii) to determine the contribution of tillage and water erosion to total soil redistribution on these two contrasting landscapes; (iii) to examine the correlation of tillage and water erosion with soil quality over the landscape; and (iv) to develop a possible landscape model for assessing variations in soil quality.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Study Area
The field sampling and investigation were conducted in the Yangjuangou Reservoir catchment (Li et al., 1997, p. 15). The catchment has an area of 2.02 km², 1025 to 1250 m above mean sea level, located near Yan'an city, northern Shaanxi province in China (36°42'N, 109°31'E). It is a secondary tributary of the Yanhe River.

These soils were developed from Malan loess with uniform soil texture (16% clay, 50% silt, and 34% sand), classified as Calciustepts in the U.S. taxonomic classification system (Soil Survey Staff, 1999) and represents the Chinese Loess Plateau where many erosion studies have been conducted in the past 40 yr. The distinctive characteristic of these landscapes are the narrow summits (averaging 30 m) and long linear backslopes (150–300 m). The study area has had a long history of cultivation dating back more than 1000 yr. Water erosion problems are the result of deforestation on steep slopes up to 40° and the extremely high erodibility of the loess soils (Li, 1995, p. 133).

Field Sampling
A detailed topographic survey was conducted on two hillslopes without terraces at 5-m intervals and on a terraced hillslope at 2- to 3-m intervals during April 1997. The two hillslopes without terraces (210-m horizontal length) had similar topographic features; one hillslope was cultivated and the other had a mixed land use (Fig. 1a and Table 1). A field boundary existed at the break between the summit position of the steep cultivated hillslope and the upper backslope position, which effectively acted as a field terrace. The upper portion of the mixed land-use hillslope, 114-m horizontal length, under permanent vegetation of grass or forest was used as the reference slope to characterize the on-site impacts of topography on soil quality. The lower hillslope portion of the mixed land-use hillslope (114–210 m) was managed as cultivated fields. The terraced slope, 37-m horizontal length, contained four terraced fields, constructed in 1958 (Fig. 1b). The hillslopes and terraces investigated were located on southwest facing slope within the catchment. All of the ¹³⁷Cs survey points coincided with the elevation survey points. Samples for determination of spatial patterns in ¹³⁷Cs were collected using a 6.74-cm–diam. hand-operated core sampler at 10-m intervals along each hillslope transect and 5-m intervals along the downslope transect on terraces. Two cores were collected at each sampling point to a depth of 40 to 60 cm and were then bulked to make a composite sample. Sampling to this depth ensured that all ¹³⁷Cs inventory of the soil profile was measured.

View larger version (23K): [in this window] [in a new window]   Fig. 1. Profile of the study of steep hillslopes showing (a)changes in slope gradients for the steep cultivated hillslope and (b)terraced slope on the Loess Plateau, near Yan'an, Shaanxi Province, China.

Sample Analysis
All samples were air dried and passed through a 2-mm sieve and weighed. All soil particles from this loess soil passed through the 2-mm sieve. Measurements of ¹³⁷Cs concentration were conducted on a subsample of 1000 g of each bulked core sample using a hyperpure coaxial Ge detector coupled to a multichannel analyzer. The ¹³⁷Cs content of samples was detected at 662 keV and using counting times of 80000 to 86400 s, which resulted in analytical precision of ±6% for ¹³⁷Cs. The results of ¹³⁷Cs were originally calculated on a unit mass basis (Bq kg^-1) and were then converted to an inventory value (Bq m^-2) using the total weight of bulked core soil sample and the sampling area. Soil bulk densities (Mg m^-3) calculations were based on volume of bulked soil cores and oven dried mass determinations (Pennock et al., 1994). Available soil N (mg kg^-1) was determined by using microdiffusion (Bremner, 1965), and available P (mg kg^-1) was determined using the method described by Olsen and Sommers (1982). Organic matter (% by weight) was measured by wet combustion (Nelson and Sommers, 1982). Particle-size distribution (%) was analyzed using the hydrometer method (Gee and Bauder, 1986).

Soil Redistribution Rate
Calculations of total soil redistribution rate at each sampling location of the terraced and steep hillslopes were derived from ¹³⁷Cs measurements based on a ¹³⁷Cs mass balance model developed by Walling and He (1997)(p. 29). The validity and value of the fallout ¹³⁷Cs approach has been demonstrated in numerous studies in a number of environments (Ritchie and McHenry, 1990; Walling and Quine, 1990, 1993; Loughran et al., 1987; Sutherland, 1992; Pennock et al., 1995). The basis of ¹³⁷Cs technique involves comparing the measured inventories (total activity in the soil profile per unit area, horizontal distance) at study sites with an estimate of the total atmospheric input obtained from a reference site (Walling and Quine, 1990; Walling & He, 1997, p. 29). By comparing ¹³⁷Cs measurements of the study site with the reference site, one can determine whether erosion (less ¹³⁷Cs present than at the reference site) or deposition (more ¹³⁷Cs than at the reference site) has occurred. In our studies, the following ¹³⁷Cs mass balance model (Walling and He, 1997, p. 29) was used for estimating total soil redistribution rates at individual sampling points:

(1)

where A(t) represents the cumulative ¹³⁷Cs activity per unit area (Bq m^-2); R represents the erosion rate (kg m^-2 yr^-1); d represents cumulative mass depth representing the average plough depth (kg m^-2); represents the decay constant for ¹³⁷Cs (yr^-1); I(t) represents the annual ¹³⁷Cs deposition flux (Bq m^-2 yr^-1); represents the percentage of the freshly deposited ¹³⁷Cs fallout removed by erosion before being mixed into the plough layer; P represents the particle-size correction factor.

Rates of soil translocation because of tillage at each ¹³⁷Cs sampling point were obtained using the TEP from the topographic data collected in the field (Lindstrom et al., 2000):

(2)

where R_T equals the soil translocation because of tillage (kg m^-2 yr ^-1); Q_S equals the downslope flux of soil (kg) per unit width of slope (m) per year; L_C equals the slope length (m) over which soil is lost (convex) or accumulated (concave).

Annual downslope soil transport (kg m^-1 yr ^-1), assuming tillage operations occur equally often in opposing directions, at any point in the landscape can be determined using:

(3)

where k represents the tillage transport coefficient (kg m^-1 yr^-1) per unit slope gradient, and q represents the tangent of slope gradient.

Only limited data for the appropriate k value for animal-powered tillage has been reported. Thapa et al. (1999) determined a mean annual soil k of 423 kg m^-1 yr^-1 for moldboard plowing in the Philippines based on two cropping cycles per year. No significant difference in k values were determined between up and downslope tillage versus contour tillage. Quine et al. (1999) determined k values of 108243 and 113 kg m^-1 yr^-1 in China, Lesotho, and Zimbabwe for animal-powered tillage when the direction of tillage was always downslope. However, in the case for continuous downslope tillage an addition constant must be added to account for the unidirectional tillage. Quine et al. (1999) concluded that net downslope translocation by animal-powered tillage always in the downslope direction may exceed those associated with mechanized agriculture. Therefore, we assigned a k value of 250 kg m^-1 yr^-1 per unit slope gradient as a reasonable approximation to simulate soil translocation because of animal-traction tillage on both the terraced field and cultivated hillslope in the Loess Plateau, China. Average water erosion rates for each sampled location were estimated by the differences between total soil redistribution and tillage erosion rates.

Statistics and Landscape Analysis
Correlation coefficient and several statistical parameters (Stein et al., 1997) were calculated to compare the relationship between the variability patterns of soil quality and soil redistribution rates at individual positions within the hillslope landscape and at selected landscape positions. Regression modeling techniques were conducted to develop a simple soil quality–topography model for evaluating on-site impacts of erosion at the hillslope scale.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Spatial Patterns of Soil Quality
On the terraced hillslope, the most distinctive patterns observed in soil quality were a decrease in OM and N levels and an increase in D_b in the upper portions of the terraces (n = 4, duplicate samples from Fields I and III) through the mid portion (n = 6, duplicates samples from Fields I, II, and IV) compared with the lower end of each terrace (n = 8, duplicate samples from all terraced fields) (Fig. 2) . Organic matter averaged 0.68% in the upper portions of the terraces as compared with 0.93% at the lower portions. Nitrogen content increased from 27 to 34 mg kg^-1 from the upper to lower portions of the terraces while D_b showed a substantial decrease (1.29 to 1.18 Mg m^-3) from the upper to lower portions of the terrace. These patterns are in agreement with redistribution patterns in ¹³⁷Cs inventory. Over the last 38 yr, total ¹³⁷Cs concentration had decreased 15% within the terrace system; showing a 76% decrease at the upper portions of the terraces, a 25% decrease in the mid sections, but showing a gain of 20% at the lower portion of the terraces. Changes in available P were variable; an increase was observed in the lower portion of the upper terrace but little or no change on the three lower terraces.

View larger version (17K): [in this window] [in a new window]   Fig. 2. Spatial distribution in soil quality parameters and 137Cs inventory along the terraced slope transects.

View larger version (19K): [in this window] [in a new window]   Fig. 3. Spatial distribution in clay and silt contents through the upper to the lower portions of the backslope of a steep cultivated hillslope.

(4)

View larger version (17K): [in this window] [in a new window]   Fig. 4. Change in adjacent slope gradients versus (a)OM, (b) N, and (c) P contents on the steep cultivated hillslope.

Individual slope gradients on the steep cultivated hillslope were calculated from the elevation measurements shown in Fig. 1a. Changes in slope gradients at sampling points were determined as the difference (Eq. [4]) between the slope segment immediately above the representative sampling point and the slope segment immediately below the sampling point. Slope gradient determinations in the terraced fields (Fig. 2) were from elevation measurements within the terraced fields excluding elevation measurements in the grassed portion of the terrace.

Positive slope changes, shown in Fig. 1a, represent a concave slope configuration. The higher values in OM, N, and P found in the summit and foot portions of the steep cultivated hillslope can be attributed to concave slopes in these hillslope positions and are directly related to lower net levels of soil loss as indicated by ¹³⁷Cs measurements. Soil translocation by tillage is directly related to slope gradients while tillage erosion (soil loss or gain) is directly related to changes in slope gradients and soil translocation rates as controlled by the tillage system (Govers et al., 1996). Soil is lost from convexities and deposited in concavities. The positive changes in slope gradients over the summit and foot position of the steep cultivated hillslope signifies concave slopes that result in soil deposition from tillage translocation. A similar situation exists in the mid backslope position (horizontal distance 70–114 m), i.e., a concave slope and higher OM, N, and P values (Fig. 1a and Table 2).

Changes in adjacent slope gradients may be used to develop a soil quality–topography relationship. A soil quality–topography landscape relationship can be described by regression analyses of the values in soil quality versus changes in adjacent slope gradients at the same locations on the steep hillslope (Fig. 4). The magnitude (Y) in soil OM, N, and P can be described using a simple linear regression model through changes in adjacent slope gradients (X): Y = aX + b, where a and b are constants. Constant a depends on magnitude of changes in adjacent slope gradients and is an effective constant of topography on soil quality. Constant b depends on the initial status of soil quality at upper portions of the cultivated hillslope. The mean relative error between predicted and measured values on the steep cultivated hillslope is 0.6% for OM, 1.2% for N, and 2.7% for P. If the estimated values in soil quality are accepted, it is possible to assess the magnitudes and patterns of soil quality at different landscape locations based solely on changes in adjacent slope gradients.

Contribution of Tillage and Water Erosion to Total Soil Redistribution
To compare the relationship between tillage and water erosion on soil redistribution over the terraced and steep cultivated hillslopes, total soil redistribution was determined by the ¹³⁷Cs mass balance model developed by Walling and He (1997)(p. 29). Soil redistribution by tillage was determined by using the TEP model developed by Lindstrom et al. (2000). Soil redistribution by water erosion was determined by difference. The relationships between tillage and water erosion using this procedure are shown in Table 5 for the terraced hillslope and in Table 6 for the steep cultivated hillslope. Soil deposition shown in Table 6 were determined on the basis of changes in adjacent slope gradients (C_SL, Eq. [4]) within each slope segment of the steep cultivated hillslope. Soil deposition levels equal to tillage erosion rates shown in Table 5 would also take place on each of the terraced fields. Soil is not moved past field boundaries by tillage erosion.

Relating Soil Redistribution to Soil Quality
To link soil redistribution rates to soil quality parameters, correlation coefficients for ¹³⁷Cs level and erosion processes were determined (Table 7). Soil redistribution rates because of tillage erosion gave a better correlation with soil quality parameters than overland flow in terraced and steep cultivated hillslope landscapes, particularly on the steep cultivated hillslope. Clearly tillage erosion has resulted in higher levels of soil OM and available nutrient contents at the lower boundaries of the terraced fields and the foot portions of the cultivated hillslope (Fig. 2 and Tables 2 and 3). Similar increases in soil OM and available nutrients occurred in the mid section of the cultivated backslope where a variation in adjacent slope gradient (C_SL) and a pronounced concave area was observed. Thus, tillage erosion plays a major role in determining spatial variability on soil quality parameters in the Chinese Loess Plateau.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

Based on our study results, the following conclusions were made:

1. Water erosion is the direct force driving the overall decline in soil quality on steep cultivated hillslopes whereas tillage erosion has a comparable contribution to overall level in soil quality on terraces.

2. Spatial variability patterns in soil quality parameters on terraced and cultivated hillslope are strongly controlled by tillage erosion. Soil OM and available nutrients positively increased with the increase in soil accumulation because of tillage erosion rates in the landscape.

3. The increased variability in soil quality parameters because of tillage erosion on steep cultivated hillslopes not dissected by terraces can be quantified on the basis of a simple linear regression model through changes in adjacent slope gradients at different landscape locations. This model has a high precision for prediction of OM and N and P contents (mean relative error <3%).

	ACKNOWLEDGMENTS

 
Y. Li is grateful to the financial support by Alexander von Humboldt Foundation (IV CHN 1039279) for his research stay in Germany and by USDA–ARS for his visiting research in N.C. Soil Conservation Research Lab, Morris, MN. Field sampling and soil analysis were assisted with J. Yang, G. Xiahou, J. Chen, Y. Zhu, and S. Wu. Financial support for this project was provided by "Hundred Talents" Project of the Chinese Academy of Sciences and the International Atomic Energy Agency, Vienna under Research Contract No. 8814 and No. 9042.

Received for publication December 6, 1999.

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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