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Modeling the soil water balance based on time-dependent hydraulic conductivity under different tillage practices
Agricultural Water Management 63 (2003) 139–151
Modeling the soil water balance based on time-dependent hydraulic conductivity under different tillage practices D. Xu a , A. Mermoud b,∗ a
b
China Institute of Water Resources and Hydropower Research (IWHR), 20 West Chegongzhuang Rd., P.O. Box 366, Beijing, PR China Institute of Environmental Science and Technology, Swiss Federal Institute of Technology, ISTE/HYDRAM, ENAC, EPFL, 1015 Lausanne, Switzerland Accepted 25 March 2003
Abstract A simulation model with time-dependent hydraulic conductivity parameters was used to predict the effects of three different tillage practices: conventional tillage (CT), no-tillage (NT) and subsoiling tillage (ST) on the components of the soil water balance during the summer maize growing season. The predictive capability of the model was improved, particularly for the subsoiling tillage case. The simulation results show that temporal changes in soil hydraulic conductivity induced by different tillage practices can affect percolation, water storage, transpiration and evaporation. Differences in the simulated components of the water balance were found to be small between CT and NT practices, but larger in the ST case. Compared with the conventional and no-tillage methods, subsoiling promotes infiltration and deep percolation, thereby favoring a possible recharge of the groundwater. Actual evaporation is always lower in the subsoiled plots, whatever the hydrological year. Transpiration is similar for the three treatments, suggesting no significant differences in water availability, except in wet years where it is higher in subsoiled soils. © 2003 Elsevier B.V. All rights reserved. Keywords: Time-dependent hydraulic parameters; Tillage practices; Soil water balance; Simulation
1. Introduction The Xiongxian area situated in the center of the north China is characterized by a typical semi-arid temperate climate. During the rainy season, intensive rainfall in wet years often ∗
Corresponding author. Tel.: +41-21-693-37-26; fax: +41-21-693-37-39. E-mail address: [email protected] (A. Mermoud). 0378-3774/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0378-3774(03)00180-X
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causes surface sealing and soil compaction, resulting in localized waterlogging and poor soil infiltration capability. On the other hand, rainfall is insufficient in dry years to meet the needs of the crops, particularly at the initial growing stage. It is, therefore, important to modify existing tillage practices to promote efficient utilisation of rainwater, to increase water storage in dry years and to increase infiltration to improve groundwater recharge in wet years. In order to evaluate and predict the effect of different tillage practices on soil water conservation, soil water simulation models have been adapted for this purpose even though variability and heterogeneity of the soils and other factors such as macropores make soil water movement predictions very complicated and difficult. The ability of a soil water simulation model to simulate the hydraulic processes occurring in tilled soils adequately depends to a large extent on how well the input parameters represent the hydraulic properties of the soil at each stage of the simulation. Most approaches to soil water modelling are based on the assumption that the hydraulic properties governing the retention and transmission of soil water are constant during the whole simulation process. However, the tillage effect on soil hydraulic properties is subject to temporal variation. Moore et al. (1980), Klute (1982) and Mwendera (1992) reported that there is a need to describe temporal changes in soil hydraulic properties after tillage quantitatively, and then to use these descriptions to simulate soil water balance under different tillage practices. They state that rational and quantitative analysis of the effect of tillage practices on soil water conservation is possible through the use of soil water transport models, if the dynamic hydraulic properties reflecting the effects of tillage are considered. In the present study, simulations of the soil water movement in tilled soils are based on an approach incorporating time-varying hydraulic parameters. The objectives of this study were to (1) incorporate the temporal variations of soil surface hydraulic properties into the soil water simulation model, (2) to evaluate the effects of different tillage practices on soil water conservation by means of the improved model, and (3) to suggest improved tillage patterns in order to achieve efficient use of water and soil resources.
2. Materials and methods 2.1. Experimental plot The experimental plot is situated in the Xiongxian area located in the centre of north China. Average annual precipitation is 540 mm, of which more than 85% falls in the monsoon season (June to September). The average annual open water evaporation is 1200 mm and average annual temperature is 12.1 ◦ C. Usually, winter wheat (early October to mid June) and summer maize (mid June to end September) crops are grown consecutively within well-defined border-strip boundaries. The most common irrigation system is surface basin irrigation. The experimental plot (990 m2 ) was installed in the spring of 1997 near an existing experimental site established in 1994 to study different irrigation systems and including an automatic meteorological station, an observation system of the soil water state variables (tensiometers and neutron probe access tube) and a 10 m deep piezometer.
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The objective of the experimental plot was to study the effects of three different tillage treatments on topsoil properties. The three studied treatments were as follows: (1) Conventional tillage (CT): This traditional method is the one generally used by local farmers. Plots are ploughed to a depth of 10–15 cm using a rotary cultivator, then seeds are drilled at 30 cm intervals in single rows spaced at 50 cm. Good contact of seeds with the soil is ensured by running a small roller along the rows. (2) No-tillage (NT): After winter wheat has been harvested, the plots are sprayed with a pre-emergence herbicide. Maize is then planted directly with seed drills, without preliminary tillage. (3) Subsoiling tillage (ST): Subsoiling is conducted using a subsoiler. Tines are spaced at 40 cm intervals with a working depth of 40 cm. This results in a structural disruption of the compacted subsurface layer. The soil is then rotatilled to a depth of 10–15 cm to break down large clods. The plots subsequently receive the same sowing and compressing treatment as conventional tillage plots. Each treatment was duplicated. To this end, the experimental plot was divided into six small plots, 33 m long and 5 m wide each. All the plots were equipped with a neutron access tube and planted with summer maize. Identical cultivation and management practices were used for all the tillage treatments. Weeds and insects were controlled when required. Maize (Xiyu 3 variety) was seeded on 18 June 1997, and harvested on 30 September. Two irrigations were conducted on 15 July (90 mm for CT and NT, 110 mm for ST) and 30 August (same amount). 2.2. Measurements The soil profile is typically characterized by six horizons (Xu, 1998): a topsoil layer with a high silt fraction (horizons I–III); a thin clay layer (horizon IV); a sandy loam layer (horizon V) and a silt loam layer (horizon VI). Some soil physical properties are given in Table 1. Hydraulic properties (soil water retention and unsaturated hydraulic conductivity functions) of the different soil horizons were described by the van Genuchten (VG) (van Genuchten, 1980) equations (Eqs. (1) and (2)). A detailed description of the methods used
Table 1 Soil physical properties at the experimental site Soil horizon
Depth (cm)
Particle size distribution Clay % (<0.002, mm)
Silt % (0.002–0.05, mm)
Sand % (mm)
Organic matter (%)
Bulk density (g/cm3 )
I II III IV V VI
0–20 20–40 40–70 70–100 100–220 220–400
26.1 26.2 26.4 36.9 5.7 20.9
69.9 69.5 69.0 59.1 49.9 72.5
4.0 4.3 4.6 4.1 44.4 6.7
1.06 0.94 0.81 1.07 0.34 0.34
1.35 1.40 1.46 1.30 1.46 1.48
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Table 2 van Genuchten equation parameters for the soils of the experimental site Soil horizon
Depth (cm)
θ r (cm3 /cm3 )
θ s (cm3 /cm3 )
α (cm−1 )
n
m
Ks (cm per day)
I II III IV V VI
0–20 20–40 40–70 70–100 100–220 >220
0.0 0.091 0.097 0.234 0.0 0.06
0.39 0.395 0.39 0.486 0.46 0.43
0.0173 0.0209 0.0185 0.0113 0.0041 0.0023
1.1975 1.2493 1.2223 1.27 2.3231 1.3866
0.1649 0.1996 0.1819 0.2126 0.5695 0.2788
13.3 13.3 13.3 18.0 24.4 0.5
to determine the VG equation parameters was given by Xu (1998); Table 2 summarizes the values of the VG model parameters for each soil horizon before the beginning of the experiment. Soil water content was measured at 5 days intervals to a depth of 1.1 m at 0.1 m increments. Additional measurements were made before and after irrigation and after heavy rainfall (≥20 mm). Hourly or daily data required for calculation of the reference evapotranspiration (ET0 ) were provided by the meteorological station. The depth of the groundwater table was measured in the piezometer every 5 days; it was usually contained between 3 and 6 m. Numbers and sizes of plant leaves were measured every 5 days on six randomly selected plants in order to calculate the leaf area index (LAI). Maximum root length was observed three times during the growing period (Liu et al., 1997). Root extraction profiles were determined from field observations in one of the plots. Root water uptake functions proposed by Feddes et al. (1978) and Hoogland et al. (1981) were derived from these extraction profiles. A detailed description of the different methods used can be found in Xu (1998). Crop production, measured in 1996 and 1997, showed that, for both years, the subsoiling tillage produced the highest grain yield and the conventional tillage the lowest; when compared with CT, the average yield increase was 8% for ST and 2% for NT (Xu, 1998). In order to quantify the temporal variations of the soil hydraulic properties following the three different tillage practices, measurements of soil surface properties were performed at initial (2–10 July), mid-season (21–27 August) and late season (23–29 September) stages of maize growth, as described below. Soil water retention properties: Soil water retention curves corresponding to the initial growth stage were determined at two depths (10 and 30 cm) for each tillage treatment. Eight locations were selected randomly within each small plot, and at each location, undisturbed soil samples (100 cm3 cylinders) were taken at 10 and 30 cm depths and brought back to the laboratory for analysis. Cores were fully saturated by placing them in deaerated water. A standard pressure membrane technique (Carter, 1993) was used for determining water retention at pressure head values of −60, −100, −300 (field capacity); −1000, −4000 and −15,000 cm (wilting point). The water content at saturation was determined by weighing the saturated samples, oven drying and reweighing them. The water retention data were fitted to the van Genuchten (1980) equation for describing the topsoil water retention curves and the unsaturated hydraulic conductivity
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functions: θ(h) = θr + K(h) = Ks
θ s − θr [1 + (α|h|)n ]m
{1 − (α|h|)n−1 [1 + (α|h|)n ]−m }2 [1 + (α|h|)n ]λm
(1)
(2)
where θ is the volumetric water content (cm3 /cm3 ), h the soil water pressure head (cm), θ s and θ r the saturated and the residual volumetric water content (cm3 /cm3 ), respectively, Ks the saturated hydraulic conductivity (cm per day), and α (cm−1 ), λ, n, and m are empirical parameters. λ was set equal to 0.5 (Mualem, 1976). Saturated hydraulic conductivity: Field saturated hydraulic conductivity Ks was measured at 10 and 30 cm at the three growth stages using a Guelph permeameter (Model 2800, Soil Moisture Equipment Corp., USA). The single-head technique was used (Reynolds and Elrick, 1987; Elrick et al., 1989). Measurements were performed in holes of diameter 6-cm and of depths 15 or 35 cm, at eight randomly selected locations within each treatment plot and the average values were calculated for the two depths. Infiltration rate: Steady-state infiltration rate or steady-state infiltrability is was measured at the three growth stages, using the constant-head pressure infiltrometer method. The measurements were made by inserting a 9.5 cm diameter steel ring 4 cm into the soil, applying a constant head of water, and measuring water flow out of the permeameter during subsequent infiltration. Estimates of the steady-state infiltration rate were obtained using the two-head technique (5 and 15 cm) as described by Reynolds and Elrick (1990). Eight randomly selected locations were selected for measurements and the average value was calculated within each treatment plot.
3. Simulations 3.1. Simulation model The mechanistic WAVE model (Vanclooster et al., 1996) used in this study is based on the vertical one-dimensional form of the Richards’ equation for modelling soil water flow. It also simulates crop growth and the movement of chemicals in the soil. The flow equation includes a sink term (Feddes et al., 1978) to account for water uptake by plant roots. The solution of the model is based on the finite difference method. Values of matric head or soil water content at each nodal point are required as initial condition, while both pressure head (Dirichlet type) and flux (Neumann type) conditions may be imposed at the soil surface as the upper boundary condition. The lower boundary condition can be specified in seven different ways. Soil water simulation with the WAVE model requires the specification of the soil hydraulic properties, of the geometry of the flow domain and of the parameters of the root water uptake function. The main input variables (rainfall, irrigation, reference evapotranspiration, leaf area index and crop coefficients) are specified on a daily basis, and boundary conditions are
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assumed to be constant within the time span of a day. The original model could not take into account possible temporal variations of the parameters. 3.2. Model adaptation Since topsoil surface properties usually change with time after tillage, the WAVE code was modified to simulate the effect of different tillage practices on soil water conservation. Infiltrability: In the WAVE model, the upper boundary condition is generally a flux condition, with the flux given by ∂h K(h) + 1 = −Qp (t) (3) ∂z PD Qp (t) = Ep − R + I + (4) t where Qp is the potential daily flux through the soil surface (cm per day), defined as positive upward, Ep the potential daily soil evaporation (cm per day), R the daily precipitation (cm per day), I the daily irrigation (cm per day), t time (day) and PD is the ponding depth at the soil surface. In this case, the pressure head at the surface is calculated as Qp (t) hs = h1 + z − −1 (5) K0 where hs and h1 are the pressure heads at the surface and at node 1, respectively, ∆z the distance from surface to node 1 and K0 is the average hydraulic conductivity value between surface and node 1, i.e. K0 = [K(hs ) + K(h1 )]/2. The saturated soil surface hydraulic conductivity Ks is an important factor affecting the actual flux through the surface; Ks can be considered to be close to the steady-state infiltration rate is with a value that decreases strongly with time after tillage (Fig. 1). Temporal changes in steady-state infiltration rate and, consequently, soil surface saturated hydraulic conductivity Ks , can be described mathematically by an exponential function (Fig. 1) that may be introduced into Eq. (2) so as to improve the characterization of the top boundary condition. For the subsoiling treatment, for example, assuming that the VG parameters remain constant, the K(h, t) relationship at the soil surface can be expressed as K(h, t) = 57.57e−0.011t
{1 − (0.025|h|)0.3 [1 + (0.025|h|)1.3 ]−0.231 }2 [1 + (0.025|h|)1.3 ]0.1155
(6)
Hydraulic conductivity: Tillage effects on soil hydraulic conductivity properties were deduced from temporal variations in the saturated hydraulic conductivity Ks determined at 10 and 30 cm depths (Fig. 2); here again, equations describing temporal variations of Ks were introduced into Eq. (2) to obtain time-dependant hydraulic conductivity values. Retention properties: The effects of the different tillage methods on the soil water retention characteristics (Xu and Mermoud, 2001) are shown in Fig. 3. The results suggest that tillage practices, especially the deep tillage method, have some effect on soil water-holding capacity. Since laboratory measurements of water retention data were made only at the initial growth stage, the water retention parameters obtained at this stage were used in the
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Fig. 1. Temporal variation of the steady-state infiltration rate is under different tillage practices (maize growing period, 1997). CT: conventional tillage, NT: no-tillage, ST: subsoiling tillage.
Fig. 2. Temporal variation of the saturated hydraulic conductivity Ks measured at 10 and 30 cm under different tillage practices (maize growing period, 1997).
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Fig. 3. Measured and fitted soil water retention curves at 10 and 30 cm under the three different tillage practices at the initial stage of the maize growing period, 1997.
simulations and supposed to be relatively constant in time. The field saturated water content θ fs at 10 and 30 cm depths was calculated from the laboratory determined θ s values on the basis of the empirical relationship: θfs = 0.85θs (Wu et al., 1996). Table 3 gives the values of the VG parameters and the time-varying saturated hydraulic conductivity at the soil surface, 10 and 30 cm depths, for the three different tillage methods. Note that the values of the VG parameters at the soil surface have been assumed to be similar to those measured at the 10 cm depth. 3.3. Model validation and simulations The modified model with time-varying hydraulic conductivity was tested on data collected in the field experimental plot during the maize growing period 1997, by comparing simulated and observed water content at 10 and 30 cm depths, for the three tillage treatments (Fig. 4). Compared with simulations performed with constant values of the soil hydraulic properties, the capability of the model to simulate observed water content values is improved, particularly for the subsoiling tillage case. Thus, it seems reasonable to accept that the modified model can be used to give reliable prediction of the effects of different tillage methods on the field water balance. Table 3 van Genuchten equation parameters and saturated hydraulic conductivity at depths 0, 10 and 30 cm, under the three considered tillage methods Tillage method
Depth (cm)
θ r (cm3 /cm3 )
θ fs (cm3 /cm3 )
α (cm−1 )
n
m
Ks (cm per day)
CT
Surface 10 30
0.0 0.0 0.091
0.390 0.390 0.395
0.0173 0.0173 0.0209
1.1975 1.1975 1.2493
0.1649 0.1649 0.1996
58.63e−0.0167t 16.49e−0.0045t 5.30e−0.0056t
NT
Surface 10 30
0.0 0.0 0.095
0.395 0.395 0.400
0.0170 0.0170 0.0215
1.1970 1.1970 1.2501
0.1646 0.1646 0.2001
54.15e−0.0188t 16.77e−0.0074t 5.60e−0.007t
ST
Surface 10 30
0.0 0.0 0.09
0.410 0.410 0.420
0.0250 0.0250 0.0281
1.3001 1.3001 1.3011
0.2308 0.2308 0.2314
57.57e−0.011t 22.25e−0.0059t 9.23e−0.0085t
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Fig. 4. Comparison between simulated and observed water content values collected at 10 and 30 cm (maize growing season, 1997) under the three tillage systems. Thin line: simulation performed with constant values of the soil hydraulic conductivity; thick line: simulation with time-varying conductivity.
The effects of tillage on soil water conservation are largely affected by the prevailing climatic conditions. Therefore, the model was used to simulate the effects of the three different tillage practices on the soil water behavior for a wet (1995), normal (1996) and dry (1997) years. All simulations were run from maize emergence (late June) to harvesting time (end September). The soil profile, formed by six distinct layers, was divided into a number of discrete compartments of 5 cm thickness to a sufficient depth (usually 10 m) in order to create a link between saturated and unsaturated domains. The time-varying soil hydraulic parameters presented in Table 3 were used to describe the dynamic hydraulic properties of horizons I and II, while the hydraulic parameters of the other horizons (Table 2) were supposed to remain constant throughout the simulation process. The measured values of the soil water content were introduced as initial condition. The observed groundwater level was specified as the lower boundary condition. The seasonal distribution of rainfall and irrigation, reference evapotranspiration (ET0 ), leaf area index and crop coefficient (Kc ), as well as crop phenology parameters observed in the experimental
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site were introduced into the model. ET0 values computed by the FAO Penman–Monteith method (Allen et al., 1994) and locally calibrated Kc values (Teixeira et al., 1996) were used to estimate potential evapotranspiration, from which potential transpiration was determined by means of the LAI used as a division parameter. The root water uptake functions S(z, h) (Feddes et al., 1978) were derived from field observations (Xu, 1998).
4. Results and discussion Table 4 presents, for the three considered hydrologic years and the three different tillage practices, the simulated values of the components of the field water balance in the 0–1 m profile, during the summer maize growth season. Whatever the hydrological year, percolation is similar for CT and NT, but significantly different for the ST treatment. In the wet year, losses are increased by more than 6% by subsoiling. In the dry year, application of a higher irrigation amount in the subsoiled plots (as defined by the experimental protocol) results in a 25% increase in the cumulative downward flux at 1 m depth, implying important irrigation percolation losses; for an irrigation supply of 180 mm, percolation is still higher (130.1 mm) than for CT and NT, but the increase is small (less than 3%). Regarding changes in water storage, differences are low between CT and NT and more important in the ST case, whatever the hydrological year. Those differences are related mainly to a higher percolation, especially in wet years, and to a smaller evaporation under ST. Fig. 5 presents the temporal changes of the simulated water storage S in the 0–40 cm soil layer, where the root density is the highest (Xu, 1998), under the three studied tillage treatments and for the three considered climatic years. For the wet year, the differences among treatments are not significant. However, in normal and dry years, the water storage is higher under NT and CT than under ST during the initial stage of the growing season. At this stage, when compared with NT and CT, the average water storage under ST is 13% (normal year) and 14% (dry year) lower. The differences Table 4 Field soil water balance (0–1 m profile) under different tillage practices during the summer maize growth season for the three considered hydrologic years Change in water storage, S (mm)
Percolation, Transpiration, P (mm) Ta (mm)
Evaporation, Ea (mm)
0 0 0
+48.9 +51.2 +19.6
375.2 374.6 398.9
152.5 151.7 164.4
46.3 45.4 40.1
434.6 434.6 434.6
0 0 0
+31.1 +31.2 +37.2
177.4 178.4 182.7
144.7 144.6 145.8
81.4 80.4 69.9
197.2 197.2 197.2
180 180 220
−14.5 −12.2 +5.1
126.9 126.2 159.4
197.5 197.5 197.8
67.3 65.7 54.9
Hydrologic year
Tillage treatment
Rainfall, R (mm)
Wet
CT NT ST
622.9 622.9 622.9
Normal
CT NT ST
Dry
CT NT ST
Irrigation, I (mm)
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Fig. 5. Simulated water storage (0–40 cm) under different tillage treatments during the maize growth season: (a) wet, (b) normal and (c) dry climatic year.
can be attributed to higher drainage in the ST plots. During the rest of the growing season, the differences between the tillage treatments remain relatively low. Water from rain or irrigation exceeding soil infiltration capacity is temporally detained in the basins separated by ridges so that no surface runoff occurs as long as the ponding depth is smaller than the height of the ridges. The water stored at the surface is absorbed progressively by the soil. Tillage practices influence the infiltration time. Longer infiltration time may cause waterlogging, producing anaerobic conditions, plant damages and a decrease or even an interruption of the root water uptake, resulting in a reduction of the actual transpiration Ta . As shown in Table 4, the wet year is characterized by lower values of Ta under CT and NT than under ST, implying that subsoiling is likely to reduce the infiltration time and alleviate waterlogging. In normal and dry years, transpiration is similar for all the treatments, suggesting no significant differences in water availability, whatever the treatment. In the subsoiled plots, the actual evaporation Ea is always lower than for the other treatments (Table 4). This can probably be attributed to the fact that under ST the top layer is less compacted and the infiltration time shorter, causing a reduction in evaporation losses.
5. Conclusions and recommendations Tillage effects on soil water conservation depend on numerous factors, especially the pedological and climatic context. Under the weather conditions prevailing in the study area,
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the simulated components of the water balance are very similar between CT and NT, but larger differences are observed in the ST case, whatever the hydrological year. Compared with the conventional and no-tillage methods, subsoiling promotes infiltration and deep percolation. In wet years, the subsoiling practice results in a larger than 6% increase in deep percolation; the relatively rapid drainage suggests that subsoiling reduces the infiltration time, thereby diminishing the risk of waterlogging. In dry years, percolation losses below the root zone are still higher than in the no-tilled and traditionally tilled soils, but the difference is smaller (less than 3%). Whatever the hydrological year, actual evaporation is lower in the ST plots; transpiration is similar for the three treatments, except in wet years where it is higher in subsoiled soils. In summary, if it is wished to augment groundwater recharge without affecting crop growth, ST would be the preferred option, particularly in a wet year, when not only deep percolation is increased but the risk of waterlogging is reduced. If, however, it is wished to provide a good supply of water to the crop or to prevent excessive recharge (to reduce the risk of soil salinisation, for example), then CT and NT are better options since they result in increased water storage in the root zone during the early part of the growing season and less percolation losses; in that case, the no-tillage treatment is probably the more interesting option, given that operational expenses for initial land preparation are reduced.
Acknowledgements This study was funded by an EC research project entitled “Improved Water and Soil Management for Sustainable Agriculture in the Huang-Huai-Hai Rivers Plain in China” (Contract TS3∗ CT93-0250), financially supported by the European Commission and the Swiss Federal Government. The technical assistance provided by Y. Liu and K.L. Ding in field experiments and data collection is gratefully acknowledged. An anonymous reviewer made valuable and comprehensive comments on the manuscript; the authors gratefully express their gratitude for his thoughtful and thorough review.
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Modeling the soil water balance based on time-dependent hydraulic conductivity under different tillage practices D. Xu a , A. Mermoud b,∗ a
b
China Institute of Water Resources and Hydropower Research (IWHR), 20 West Chegongzhuang Rd., P.O. Box 366, Beijing, PR China Institute of Environmental Science and Technology, Swiss Federal Institute of Technology, ISTE/HYDRAM, ENAC, EPFL, 1015 Lausanne, Switzerland Accepted 25 March 2003
Abstract A simulation model with time-dependent hydraulic conductivity parameters was used to predict the effects of three different tillage practices: conventional tillage (CT), no-tillage (NT) and subsoiling tillage (ST) on the components of the soil water balance during the summer maize growing season. The predictive capability of the model was improved, particularly for the subsoiling tillage case. The simulation results show that temporal changes in soil hydraulic conductivity induced by different tillage practices can affect percolation, water storage, transpiration and evaporation. Differences in the simulated components of the water balance were found to be small between CT and NT practices, but larger in the ST case. Compared with the conventional and no-tillage methods, subsoiling promotes infiltration and deep percolation, thereby favoring a possible recharge of the groundwater. Actual evaporation is always lower in the subsoiled plots, whatever the hydrological year. Transpiration is similar for the three treatments, suggesting no significant differences in water availability, except in wet years where it is higher in subsoiled soils. © 2003 Elsevier B.V. All rights reserved. Keywords: Time-dependent hydraulic parameters; Tillage practices; Soil water balance; Simulation
1. Introduction The Xiongxian area situated in the center of the north China is characterized by a typical semi-arid temperate climate. During the rainy season, intensive rainfall in wet years often ∗
Corresponding author. Tel.: +41-21-693-37-26; fax: +41-21-693-37-39. E-mail address: [email protected] (A. Mermoud). 0378-3774/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0378-3774(03)00180-X
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causes surface sealing and soil compaction, resulting in localized waterlogging and poor soil infiltration capability. On the other hand, rainfall is insufficient in dry years to meet the needs of the crops, particularly at the initial growing stage. It is, therefore, important to modify existing tillage practices to promote efficient utilisation of rainwater, to increase water storage in dry years and to increase infiltration to improve groundwater recharge in wet years. In order to evaluate and predict the effect of different tillage practices on soil water conservation, soil water simulation models have been adapted for this purpose even though variability and heterogeneity of the soils and other factors such as macropores make soil water movement predictions very complicated and difficult. The ability of a soil water simulation model to simulate the hydraulic processes occurring in tilled soils adequately depends to a large extent on how well the input parameters represent the hydraulic properties of the soil at each stage of the simulation. Most approaches to soil water modelling are based on the assumption that the hydraulic properties governing the retention and transmission of soil water are constant during the whole simulation process. However, the tillage effect on soil hydraulic properties is subject to temporal variation. Moore et al. (1980), Klute (1982) and Mwendera (1992) reported that there is a need to describe temporal changes in soil hydraulic properties after tillage quantitatively, and then to use these descriptions to simulate soil water balance under different tillage practices. They state that rational and quantitative analysis of the effect of tillage practices on soil water conservation is possible through the use of soil water transport models, if the dynamic hydraulic properties reflecting the effects of tillage are considered. In the present study, simulations of the soil water movement in tilled soils are based on an approach incorporating time-varying hydraulic parameters. The objectives of this study were to (1) incorporate the temporal variations of soil surface hydraulic properties into the soil water simulation model, (2) to evaluate the effects of different tillage practices on soil water conservation by means of the improved model, and (3) to suggest improved tillage patterns in order to achieve efficient use of water and soil resources.
2. Materials and methods 2.1. Experimental plot The experimental plot is situated in the Xiongxian area located in the centre of north China. Average annual precipitation is 540 mm, of which more than 85% falls in the monsoon season (June to September). The average annual open water evaporation is 1200 mm and average annual temperature is 12.1 ◦ C. Usually, winter wheat (early October to mid June) and summer maize (mid June to end September) crops are grown consecutively within well-defined border-strip boundaries. The most common irrigation system is surface basin irrigation. The experimental plot (990 m2 ) was installed in the spring of 1997 near an existing experimental site established in 1994 to study different irrigation systems and including an automatic meteorological station, an observation system of the soil water state variables (tensiometers and neutron probe access tube) and a 10 m deep piezometer.
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The objective of the experimental plot was to study the effects of three different tillage treatments on topsoil properties. The three studied treatments were as follows: (1) Conventional tillage (CT): This traditional method is the one generally used by local farmers. Plots are ploughed to a depth of 10–15 cm using a rotary cultivator, then seeds are drilled at 30 cm intervals in single rows spaced at 50 cm. Good contact of seeds with the soil is ensured by running a small roller along the rows. (2) No-tillage (NT): After winter wheat has been harvested, the plots are sprayed with a pre-emergence herbicide. Maize is then planted directly with seed drills, without preliminary tillage. (3) Subsoiling tillage (ST): Subsoiling is conducted using a subsoiler. Tines are spaced at 40 cm intervals with a working depth of 40 cm. This results in a structural disruption of the compacted subsurface layer. The soil is then rotatilled to a depth of 10–15 cm to break down large clods. The plots subsequently receive the same sowing and compressing treatment as conventional tillage plots. Each treatment was duplicated. To this end, the experimental plot was divided into six small plots, 33 m long and 5 m wide each. All the plots were equipped with a neutron access tube and planted with summer maize. Identical cultivation and management practices were used for all the tillage treatments. Weeds and insects were controlled when required. Maize (Xiyu 3 variety) was seeded on 18 June 1997, and harvested on 30 September. Two irrigations were conducted on 15 July (90 mm for CT and NT, 110 mm for ST) and 30 August (same amount). 2.2. Measurements The soil profile is typically characterized by six horizons (Xu, 1998): a topsoil layer with a high silt fraction (horizons I–III); a thin clay layer (horizon IV); a sandy loam layer (horizon V) and a silt loam layer (horizon VI). Some soil physical properties are given in Table 1. Hydraulic properties (soil water retention and unsaturated hydraulic conductivity functions) of the different soil horizons were described by the van Genuchten (VG) (van Genuchten, 1980) equations (Eqs. (1) and (2)). A detailed description of the methods used
Table 1 Soil physical properties at the experimental site Soil horizon
Depth (cm)
Particle size distribution Clay % (<0.002, mm)
Silt % (0.002–0.05, mm)
Sand % (mm)
Organic matter (%)
Bulk density (g/cm3 )
I II III IV V VI
0–20 20–40 40–70 70–100 100–220 220–400
26.1 26.2 26.4 36.9 5.7 20.9
69.9 69.5 69.0 59.1 49.9 72.5
4.0 4.3 4.6 4.1 44.4 6.7
1.06 0.94 0.81 1.07 0.34 0.34
1.35 1.40 1.46 1.30 1.46 1.48
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Table 2 van Genuchten equation parameters for the soils of the experimental site Soil horizon
Depth (cm)
θ r (cm3 /cm3 )
θ s (cm3 /cm3 )
α (cm−1 )
n
m
Ks (cm per day)
I II III IV V VI
0–20 20–40 40–70 70–100 100–220 >220
0.0 0.091 0.097 0.234 0.0 0.06
0.39 0.395 0.39 0.486 0.46 0.43
0.0173 0.0209 0.0185 0.0113 0.0041 0.0023
1.1975 1.2493 1.2223 1.27 2.3231 1.3866
0.1649 0.1996 0.1819 0.2126 0.5695 0.2788
13.3 13.3 13.3 18.0 24.4 0.5
to determine the VG equation parameters was given by Xu (1998); Table 2 summarizes the values of the VG model parameters for each soil horizon before the beginning of the experiment. Soil water content was measured at 5 days intervals to a depth of 1.1 m at 0.1 m increments. Additional measurements were made before and after irrigation and after heavy rainfall (≥20 mm). Hourly or daily data required for calculation of the reference evapotranspiration (ET0 ) were provided by the meteorological station. The depth of the groundwater table was measured in the piezometer every 5 days; it was usually contained between 3 and 6 m. Numbers and sizes of plant leaves were measured every 5 days on six randomly selected plants in order to calculate the leaf area index (LAI). Maximum root length was observed three times during the growing period (Liu et al., 1997). Root extraction profiles were determined from field observations in one of the plots. Root water uptake functions proposed by Feddes et al. (1978) and Hoogland et al. (1981) were derived from these extraction profiles. A detailed description of the different methods used can be found in Xu (1998). Crop production, measured in 1996 and 1997, showed that, for both years, the subsoiling tillage produced the highest grain yield and the conventional tillage the lowest; when compared with CT, the average yield increase was 8% for ST and 2% for NT (Xu, 1998). In order to quantify the temporal variations of the soil hydraulic properties following the three different tillage practices, measurements of soil surface properties were performed at initial (2–10 July), mid-season (21–27 August) and late season (23–29 September) stages of maize growth, as described below. Soil water retention properties: Soil water retention curves corresponding to the initial growth stage were determined at two depths (10 and 30 cm) for each tillage treatment. Eight locations were selected randomly within each small plot, and at each location, undisturbed soil samples (100 cm3 cylinders) were taken at 10 and 30 cm depths and brought back to the laboratory for analysis. Cores were fully saturated by placing them in deaerated water. A standard pressure membrane technique (Carter, 1993) was used for determining water retention at pressure head values of −60, −100, −300 (field capacity); −1000, −4000 and −15,000 cm (wilting point). The water content at saturation was determined by weighing the saturated samples, oven drying and reweighing them. The water retention data were fitted to the van Genuchten (1980) equation for describing the topsoil water retention curves and the unsaturated hydraulic conductivity
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functions: θ(h) = θr + K(h) = Ks
θ s − θr [1 + (α|h|)n ]m
{1 − (α|h|)n−1 [1 + (α|h|)n ]−m }2 [1 + (α|h|)n ]λm
(1)
(2)
where θ is the volumetric water content (cm3 /cm3 ), h the soil water pressure head (cm), θ s and θ r the saturated and the residual volumetric water content (cm3 /cm3 ), respectively, Ks the saturated hydraulic conductivity (cm per day), and α (cm−1 ), λ, n, and m are empirical parameters. λ was set equal to 0.5 (Mualem, 1976). Saturated hydraulic conductivity: Field saturated hydraulic conductivity Ks was measured at 10 and 30 cm at the three growth stages using a Guelph permeameter (Model 2800, Soil Moisture Equipment Corp., USA). The single-head technique was used (Reynolds and Elrick, 1987; Elrick et al., 1989). Measurements were performed in holes of diameter 6-cm and of depths 15 or 35 cm, at eight randomly selected locations within each treatment plot and the average values were calculated for the two depths. Infiltration rate: Steady-state infiltration rate or steady-state infiltrability is was measured at the three growth stages, using the constant-head pressure infiltrometer method. The measurements were made by inserting a 9.5 cm diameter steel ring 4 cm into the soil, applying a constant head of water, and measuring water flow out of the permeameter during subsequent infiltration. Estimates of the steady-state infiltration rate were obtained using the two-head technique (5 and 15 cm) as described by Reynolds and Elrick (1990). Eight randomly selected locations were selected for measurements and the average value was calculated within each treatment plot.
3. Simulations 3.1. Simulation model The mechanistic WAVE model (Vanclooster et al., 1996) used in this study is based on the vertical one-dimensional form of the Richards’ equation for modelling soil water flow. It also simulates crop growth and the movement of chemicals in the soil. The flow equation includes a sink term (Feddes et al., 1978) to account for water uptake by plant roots. The solution of the model is based on the finite difference method. Values of matric head or soil water content at each nodal point are required as initial condition, while both pressure head (Dirichlet type) and flux (Neumann type) conditions may be imposed at the soil surface as the upper boundary condition. The lower boundary condition can be specified in seven different ways. Soil water simulation with the WAVE model requires the specification of the soil hydraulic properties, of the geometry of the flow domain and of the parameters of the root water uptake function. The main input variables (rainfall, irrigation, reference evapotranspiration, leaf area index and crop coefficients) are specified on a daily basis, and boundary conditions are
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assumed to be constant within the time span of a day. The original model could not take into account possible temporal variations of the parameters. 3.2. Model adaptation Since topsoil surface properties usually change with time after tillage, the WAVE code was modified to simulate the effect of different tillage practices on soil water conservation. Infiltrability: In the WAVE model, the upper boundary condition is generally a flux condition, with the flux given by ∂h K(h) + 1 = −Qp (t) (3) ∂z PD Qp (t) = Ep − R + I + (4) t where Qp is the potential daily flux through the soil surface (cm per day), defined as positive upward, Ep the potential daily soil evaporation (cm per day), R the daily precipitation (cm per day), I the daily irrigation (cm per day), t time (day) and PD is the ponding depth at the soil surface. In this case, the pressure head at the surface is calculated as Qp (t) hs = h1 + z − −1 (5) K0 where hs and h1 are the pressure heads at the surface and at node 1, respectively, ∆z the distance from surface to node 1 and K0 is the average hydraulic conductivity value between surface and node 1, i.e. K0 = [K(hs ) + K(h1 )]/2. The saturated soil surface hydraulic conductivity Ks is an important factor affecting the actual flux through the surface; Ks can be considered to be close to the steady-state infiltration rate is with a value that decreases strongly with time after tillage (Fig. 1). Temporal changes in steady-state infiltration rate and, consequently, soil surface saturated hydraulic conductivity Ks , can be described mathematically by an exponential function (Fig. 1) that may be introduced into Eq. (2) so as to improve the characterization of the top boundary condition. For the subsoiling treatment, for example, assuming that the VG parameters remain constant, the K(h, t) relationship at the soil surface can be expressed as K(h, t) = 57.57e−0.011t
{1 − (0.025|h|)0.3 [1 + (0.025|h|)1.3 ]−0.231 }2 [1 + (0.025|h|)1.3 ]0.1155
(6)
Hydraulic conductivity: Tillage effects on soil hydraulic conductivity properties were deduced from temporal variations in the saturated hydraulic conductivity Ks determined at 10 and 30 cm depths (Fig. 2); here again, equations describing temporal variations of Ks were introduced into Eq. (2) to obtain time-dependant hydraulic conductivity values. Retention properties: The effects of the different tillage methods on the soil water retention characteristics (Xu and Mermoud, 2001) are shown in Fig. 3. The results suggest that tillage practices, especially the deep tillage method, have some effect on soil water-holding capacity. Since laboratory measurements of water retention data were made only at the initial growth stage, the water retention parameters obtained at this stage were used in the
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Fig. 1. Temporal variation of the steady-state infiltration rate is under different tillage practices (maize growing period, 1997). CT: conventional tillage, NT: no-tillage, ST: subsoiling tillage.
Fig. 2. Temporal variation of the saturated hydraulic conductivity Ks measured at 10 and 30 cm under different tillage practices (maize growing period, 1997).
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Fig. 3. Measured and fitted soil water retention curves at 10 and 30 cm under the three different tillage practices at the initial stage of the maize growing period, 1997.
simulations and supposed to be relatively constant in time. The field saturated water content θ fs at 10 and 30 cm depths was calculated from the laboratory determined θ s values on the basis of the empirical relationship: θfs = 0.85θs (Wu et al., 1996). Table 3 gives the values of the VG parameters and the time-varying saturated hydraulic conductivity at the soil surface, 10 and 30 cm depths, for the three different tillage methods. Note that the values of the VG parameters at the soil surface have been assumed to be similar to those measured at the 10 cm depth. 3.3. Model validation and simulations The modified model with time-varying hydraulic conductivity was tested on data collected in the field experimental plot during the maize growing period 1997, by comparing simulated and observed water content at 10 and 30 cm depths, for the three tillage treatments (Fig. 4). Compared with simulations performed with constant values of the soil hydraulic properties, the capability of the model to simulate observed water content values is improved, particularly for the subsoiling tillage case. Thus, it seems reasonable to accept that the modified model can be used to give reliable prediction of the effects of different tillage methods on the field water balance. Table 3 van Genuchten equation parameters and saturated hydraulic conductivity at depths 0, 10 and 30 cm, under the three considered tillage methods Tillage method
Depth (cm)
θ r (cm3 /cm3 )
θ fs (cm3 /cm3 )
α (cm−1 )
n
m
Ks (cm per day)
CT
Surface 10 30
0.0 0.0 0.091
0.390 0.390 0.395
0.0173 0.0173 0.0209
1.1975 1.1975 1.2493
0.1649 0.1649 0.1996
58.63e−0.0167t 16.49e−0.0045t 5.30e−0.0056t
NT
Surface 10 30
0.0 0.0 0.095
0.395 0.395 0.400
0.0170 0.0170 0.0215
1.1970 1.1970 1.2501
0.1646 0.1646 0.2001
54.15e−0.0188t 16.77e−0.0074t 5.60e−0.007t
ST
Surface 10 30
0.0 0.0 0.09
0.410 0.410 0.420
0.0250 0.0250 0.0281
1.3001 1.3001 1.3011
0.2308 0.2308 0.2314
57.57e−0.011t 22.25e−0.0059t 9.23e−0.0085t
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Fig. 4. Comparison between simulated and observed water content values collected at 10 and 30 cm (maize growing season, 1997) under the three tillage systems. Thin line: simulation performed with constant values of the soil hydraulic conductivity; thick line: simulation with time-varying conductivity.
The effects of tillage on soil water conservation are largely affected by the prevailing climatic conditions. Therefore, the model was used to simulate the effects of the three different tillage practices on the soil water behavior for a wet (1995), normal (1996) and dry (1997) years. All simulations were run from maize emergence (late June) to harvesting time (end September). The soil profile, formed by six distinct layers, was divided into a number of discrete compartments of 5 cm thickness to a sufficient depth (usually 10 m) in order to create a link between saturated and unsaturated domains. The time-varying soil hydraulic parameters presented in Table 3 were used to describe the dynamic hydraulic properties of horizons I and II, while the hydraulic parameters of the other horizons (Table 2) were supposed to remain constant throughout the simulation process. The measured values of the soil water content were introduced as initial condition. The observed groundwater level was specified as the lower boundary condition. The seasonal distribution of rainfall and irrigation, reference evapotranspiration (ET0 ), leaf area index and crop coefficient (Kc ), as well as crop phenology parameters observed in the experimental
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site were introduced into the model. ET0 values computed by the FAO Penman–Monteith method (Allen et al., 1994) and locally calibrated Kc values (Teixeira et al., 1996) were used to estimate potential evapotranspiration, from which potential transpiration was determined by means of the LAI used as a division parameter. The root water uptake functions S(z, h) (Feddes et al., 1978) were derived from field observations (Xu, 1998).
4. Results and discussion Table 4 presents, for the three considered hydrologic years and the three different tillage practices, the simulated values of the components of the field water balance in the 0–1 m profile, during the summer maize growth season. Whatever the hydrological year, percolation is similar for CT and NT, but significantly different for the ST treatment. In the wet year, losses are increased by more than 6% by subsoiling. In the dry year, application of a higher irrigation amount in the subsoiled plots (as defined by the experimental protocol) results in a 25% increase in the cumulative downward flux at 1 m depth, implying important irrigation percolation losses; for an irrigation supply of 180 mm, percolation is still higher (130.1 mm) than for CT and NT, but the increase is small (less than 3%). Regarding changes in water storage, differences are low between CT and NT and more important in the ST case, whatever the hydrological year. Those differences are related mainly to a higher percolation, especially in wet years, and to a smaller evaporation under ST. Fig. 5 presents the temporal changes of the simulated water storage S in the 0–40 cm soil layer, where the root density is the highest (Xu, 1998), under the three studied tillage treatments and for the three considered climatic years. For the wet year, the differences among treatments are not significant. However, in normal and dry years, the water storage is higher under NT and CT than under ST during the initial stage of the growing season. At this stage, when compared with NT and CT, the average water storage under ST is 13% (normal year) and 14% (dry year) lower. The differences Table 4 Field soil water balance (0–1 m profile) under different tillage practices during the summer maize growth season for the three considered hydrologic years Change in water storage, S (mm)
Percolation, Transpiration, P (mm) Ta (mm)
Evaporation, Ea (mm)
0 0 0
+48.9 +51.2 +19.6
375.2 374.6 398.9
152.5 151.7 164.4
46.3 45.4 40.1
434.6 434.6 434.6
0 0 0
+31.1 +31.2 +37.2
177.4 178.4 182.7
144.7 144.6 145.8
81.4 80.4 69.9
197.2 197.2 197.2
180 180 220
−14.5 −12.2 +5.1
126.9 126.2 159.4
197.5 197.5 197.8
67.3 65.7 54.9
Hydrologic year
Tillage treatment
Rainfall, R (mm)
Wet
CT NT ST
622.9 622.9 622.9
Normal
CT NT ST
Dry
CT NT ST
Irrigation, I (mm)
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Fig. 5. Simulated water storage (0–40 cm) under different tillage treatments during the maize growth season: (a) wet, (b) normal and (c) dry climatic year.
can be attributed to higher drainage in the ST plots. During the rest of the growing season, the differences between the tillage treatments remain relatively low. Water from rain or irrigation exceeding soil infiltration capacity is temporally detained in the basins separated by ridges so that no surface runoff occurs as long as the ponding depth is smaller than the height of the ridges. The water stored at the surface is absorbed progressively by the soil. Tillage practices influence the infiltration time. Longer infiltration time may cause waterlogging, producing anaerobic conditions, plant damages and a decrease or even an interruption of the root water uptake, resulting in a reduction of the actual transpiration Ta . As shown in Table 4, the wet year is characterized by lower values of Ta under CT and NT than under ST, implying that subsoiling is likely to reduce the infiltration time and alleviate waterlogging. In normal and dry years, transpiration is similar for all the treatments, suggesting no significant differences in water availability, whatever the treatment. In the subsoiled plots, the actual evaporation Ea is always lower than for the other treatments (Table 4). This can probably be attributed to the fact that under ST the top layer is less compacted and the infiltration time shorter, causing a reduction in evaporation losses.
5. Conclusions and recommendations Tillage effects on soil water conservation depend on numerous factors, especially the pedological and climatic context. Under the weather conditions prevailing in the study area,
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the simulated components of the water balance are very similar between CT and NT, but larger differences are observed in the ST case, whatever the hydrological year. Compared with the conventional and no-tillage methods, subsoiling promotes infiltration and deep percolation. In wet years, the subsoiling practice results in a larger than 6% increase in deep percolation; the relatively rapid drainage suggests that subsoiling reduces the infiltration time, thereby diminishing the risk of waterlogging. In dry years, percolation losses below the root zone are still higher than in the no-tilled and traditionally tilled soils, but the difference is smaller (less than 3%). Whatever the hydrological year, actual evaporation is lower in the ST plots; transpiration is similar for the three treatments, except in wet years where it is higher in subsoiled soils. In summary, if it is wished to augment groundwater recharge without affecting crop growth, ST would be the preferred option, particularly in a wet year, when not only deep percolation is increased but the risk of waterlogging is reduced. If, however, it is wished to provide a good supply of water to the crop or to prevent excessive recharge (to reduce the risk of soil salinisation, for example), then CT and NT are better options since they result in increased water storage in the root zone during the early part of the growing season and less percolation losses; in that case, the no-tillage treatment is probably the more interesting option, given that operational expenses for initial land preparation are reduced.
Acknowledgements This study was funded by an EC research project entitled “Improved Water and Soil Management for Sustainable Agriculture in the Huang-Huai-Hai Rivers Plain in China” (Contract TS3∗ CT93-0250), financially supported by the European Commission and the Swiss Federal Government. The technical assistance provided by Y. Liu and K.L. Ding in field experiments and data collection is gratefully acknowledged. An anonymous reviewer made valuable and comprehensive comments on the manuscript; the authors gratefully express their gratitude for his thoughtful and thorough review.
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