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Division S-1—Soil Physics

Measuring Hysteretic Hydraulic Properties of Peat and Pine Bark using a Transient Method

Unité Mixte de Recherche A_462 ‘SAGAH’ INRA/INH/Université d‘Angers, Sciences Agronomiques Appliquées à l‘Horticulture, 2 rue Le Nôtre, 49045 Angers Cedex, France

^* Corresponding author (remi.naasz{at}inh.fr).

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
The precise and continuous measurement of the hydraulic properties of growing media is of vital importance for the effective management of irrigation and fertilization. The main purpose of this study was to use a transient method to characterize the hydraulic properties of two horticultural substrates (peat and composted pine bark) and, second, to test the applicability of well-known hydraulic models commonly used on mineral soils. Substrate water retention and hydraulic conductivity curves were determined in the laboratory by the Instantaneous Profile Method (IPM), during a drying–wetting cycle. The results showed hysteresis for peat in the () curves (30% vol.) and in the K() curves [one order of magnitude in K()], whereas this phenomenon was very limited for pine bark [approximately 10% vol. in () and approximately a half-order of magnitude in K()]. The K() curves for both substrates considerably decreased after the considerable variation in water content observed in (), that is to say, after –5 and –3 kPa for peat and pine bark, respectively. Fitting the van Genuchten retention model to observed data resulted in a high correlation (0.96 R² 0.99) and fitting unsaturated hydraulic conductivity models (VGM and BC) resulted in a lower correlation (0.43 R² 0.64). Our results seemed to be in agreement with other hydraulic studies of substrates. These overall results implied that hydraulic properties of tested substrates widely fluctuate in a narrow range of water potentials that could therefore rapidly affect water and air availability to the roots. Knowledge of K() and K() curves, in addition to the () curve, could contribute to alleviating stress conditions.

Abbreviations: AC, air capacity • BC, Brooks and Corey • IPM, instantaneous profile method • PVDF, polyvinylidenfluoride • TDR, time domain reflectometry • VG, van Genuchten • VGM, van Genuchten-Mualem • WHC, water holding capacity

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
KNOWLEDGE OF WATER dynamics is essential for a better understanding of how the soil–plant system functions, particularly in terms of fertilization–irrigation management and of pollutant leaching as well. This is also and especially true in soilless cultures such as container media, due to the specificity of this production system with its extensive use of chemicals. Indeed, plants growing in containers have a limited volume of substrate in which water, gas, and solute availability highly fluctuate over a short period of time (a few hours) (Polak and Wallach, 2001), involving frequent cycles of watering (fertigation) and drying during growing management. These variations in water content could lead to a reorganization of the solid phase due to shrinkage and swelling (Heiskanen, 1995) and to a change in wettability (Valat et al., 1991; Michel et al., 2001). As a result, most organic substrates exhibit pronounced hysteresis phenomena (da Silva et al., 1993; Otten et al., 1999; Heinen and Raats, 1999), which can greatly influence the properties of water and air circulation.

However, until now, most studies dealing with the physical properties of substrates have only attempted to characterize water and gas distribution related to water potential (Bunt, 1961; De Boodt and Verdonck, 1972; Rivière et al., 1990), disregarding their availability to the plants, estimated by hydraulic conductivity and gas diffusivity measurements. Assessment of these transient properties are difficult and tedious due to the wide variety of natural and artificial, organic and mineral substrates which could present considerably different structures and physical properties as well. Furthermore, compared with most soils, horticultural substrates are coarse porous materials affected by handling (Paquet et al., 1993) and root activity (Allaire-Leung et al., 1999). However, efforts have recently been made to assess these transient properties (Laurén and Heiskanen, 1997; Caron et al., 1998) and to apply standard soil mathematical functions (van Genuchten, 1980; Mualem, 1976) to horticultural substrates (Milks et al., 1989; Wallach et al., 1992; Otten et al., 1999) with the aim of modeling water flows in these unsaturated media. But more precise and reliable characterization of the hydraulic properties of substrates is still necessary for the effective management of fertigation and for subsequent plant growth in soilless culture production.

In this context, the first objective of this study was to use a transient experimental technique (IPM) generally used on soil, estimating the hydraulic properties of two organic materials commonly used as horticultural substrates (peat and composted pine bark) during evaporation as well as during infiltration experiments in the laboratory.

The second objective of our research was to test whether or not hydraulic models (van Genuchten, 1980; Mualem, 1976; Brooks and Corey, 1964) could be used to correctly describe the substrate properties during a desiccation/infiltration cycle.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Water Dynamics Theory
Water movement in unsaturated porous media is generally described by Richards' equation (Richards, 1931):

[1]

where t is time, represents the pressure head (m), is the volumetric water content (m³ m^–3) and z is depth (m, positive upward). Richards' equation can be solved numerically but knowledge of the hydraulic properties of the soil, namely the retention curve () and the hydraulic conductivity characteristic K(), are required. In this study, we used the van Genuchten (1980) function to describe the water retention characteristic:

[2]

where _r and _s are the residual volumetric water content and the volumetric water content at saturation (m³ m^–3), respectively, is a curve fitting parameter, sometimes interpreted as the inverse of the air entry value (1/_e) (m^–1), and n and m are fitting constants reflecting the steepness of the retention curve. The hydraulic conductivity characteristic is described using the pore-size distribution model of Mualem (1976), combined with the water retention characteristic (Eq. [2]) hydraulic conductivity can be expressed as a function of the pressure head (Eq. [3]) or water content (Eq. [4]):

[3]

[4]

where K_s is the hydraulic conductivity at saturation and is an empirical pore connectivity factor, used as a curve fitting parameter. Parameters n and m were used independently.

We also used the model of Brooks and Corey (1964) (BC) to describe the hydraulic conductivity characteristic:

[5]

where B is an empirical fitting parameter.

Materials and Sample Preparation
Weakly decomposed sphagnum peat and composted pine bark were chosen as substrates in our study for several reasons. Indeed, sphagnum peat is internationally considered as the main reference substrate in horticultural production (in terms of quality and quantity), and composted pine bark is one of the main peat additives (particularly in France) to improve the physical properties of growing media. Furthermore, both of these materials were chosen because they present considerably different structures and physical properties as well: sphagnum peat is a fine fibrous material while pine bark is large, coarse, and platy. Consequently, peat is generally considered as a retention media whereas pine bark is considered to be an aerated material.

Sieving was performed to obtain the finest fractions possible of these substrates in this study: peat with a particle-size range of 0 to 5 mm and pine bark with a particle-size range of 0 to 10 mm. The physical characteristics of the substrates (Table 1) were calculated, taking the average value of four replicates. Bulk density, organic matter, and pH were determined according to European procedures NF EN 13041 (2000), NF EN 13039 (2000), and NF EN 13037 (2000), respectively. Particle density was estimated by the pycnometer method using petroleum spirit (Galvin, 1976) and cation exchange capacity according to the procedure of Lemaire et al. (2003). Organic elemental analysis (C and N) was performed using the procedure of Dumas and Pregl (Deléens et al., 1997).

Laboratory Procedures
Experimental Design
For each core PVDF cylinder, the water potential and volumetric water content were determined by tensiometers and time domain reflectometry (TDR) probes, respectively, installed at two levels, h₁ and h₂, from the bottom of the cylinder (h₁ = 9 cm and h₂ = 3 cm, Fig. 1) . The mini-tensiometers (2.2 mm in diameter by 20 mm in length, ceramic cell, SDEC 220, Société Développement Et Commercialisation, Reignac, France) were connected to pressure transducers (differential pressure sensors, precision: ±0.03%, response time: 10^–3 s) monitored by a real-time multitasking computer to control the measurement and collect data. The TDR system consisted of TDR mini-probes (three wires, 80 mm long, with 4-mm uncoated stainless steel rods and a spacing of 10 mm). The probes were connected to a Tektronix 1502C system (Tektronix, Beaverton, OR) via a multiplexer run by WINTDR Software (Time Domain Reflectometry Soil Sample Analysis Program V. 5.1, Utah State University, Logan, UT).

View larger version (33K): [in this window] [in a new window]   Fig. 1. Schematic representation of the experimental design.

As a result of the particularities of materials used, three different models were tested to fit the results of the measured /K_a relationship. Two of them are based on empirical polynomial functions: a third-order polynomial model (Eq. [6]) developed by Topp et al. (1980), which can be considered as the most common one used for many soils, and a second-order polynomial model (Eq. [7]) defined by Pepin et al. (1992), already used on organic substrates:

[6]

[7]

where ₀, ₁, ₂, and ₃ are fitting coefficients.

The third model (Eq. [8]) used is a partly deterministic model (the -model) based on the theory of mixtures, which assumes that the porous media is a ternary system of water, air, and the solid fraction. The model version tested here is the one reported by Roth et al. (1990) and Todoroff and Langellier (1998):

[8]

where K_a is the dielectric constant of the medium, K_water, K_soil, and K_air are dielectric constants of water, the solid fraction and air, respectively, while represents the porosity. The coefficient accounts for the effects of the geometrical arrangement of the medium components and the consequences on its dielectric performance. According to Dirksen and Dasberg (1993), and K_soil were considered to be fitting coefficients and K_water and K_air were defined as 79.63 and 1, respectively, at 22°C.

Evaporation and Infiltration Experiments
After the sample preparation procedure, tensiometers and TDR were installed, and the drying and rewetting experiments could begin when readings indicated that the cores were in hydrostatic equilibrium. The experiment lasted for approximately one month and represented approximately 8000 sets of water content–water potential data.

The evaporation experiment was based on a concept similar to the evaporation method of Wind (1969), revaluated and modified by Wendroth et al. (1993) and Tamari et al. (1993). We adapted this method using an instantaneous profile procedure (Paige and Hillel, 1993; Stolte et al., 1994; Wessolek et al., 1994), which provides a detailed description of (TDR) and (tensiometers) as a function of time and space. During the evaporation experiment, the bottom of the column was sealed to prevent water loss (zero-flux bottom boundary condition) and the top of the sample was subjected to evaporation using small ventilators. The experiment was terminated when the uppermost tensiometer in the substrate core reached a suction level of approximately –20 kPa and infiltration could then begin.

The infiltration experiment was inspired by the Upward Infiltration Method (UIM) developed by Hudson et al. (1996). This study used constant flux bottom boundary conditions, which reduced the need for using the flux as an optimization parameter since the flux is independent of the soil properties (Simunek and van Genuchten, 1997). We preferred using constant pressure head bottom boundary conditions with a Mariotte system, recently tested on soil by Simunek et al. (2001) and Young et al. (2002). Three pressure levels were used during each infiltration experiment (stepwise increase): –15, –5, and 0 kPa.

Experimental Constraints
The introduction of TDR mini-probes and mini-tensiometers in the substrate column could possibly lead to a small change in the structure of materials and, as a result, in the hydraulic properties. The volume occupied by all sensors in the column was calculated and only represented <0.5% of the whole substrate core volume.

Furthermore, measurement of the water content by TDR technique during the calibration experiment was performed in a different way than during the drying–rewetting experiments. For calibration, a TDR mini-probe was installed vertically and was directly in contact with the column of substrate. A vertical water content gradient in the samples close to saturation could not be avoided but these water content variations were partially taken into account by the measurement itself: according to Ferré et al. (1996), time domain reflectometry measurements give a weighted average value of water content layers when the calibration function shows a linear relationship between and k_a (which is similar to Topp's equation). The drying–rewetting experiments are based on the use of two mini-probes installed in a horizontal position and going through the entire thickness of the PVDF column, over a distance of 5.3 mm, influencing k_a and water content measurements as a result. Since we knew the dielectric constant of PVDF (k_a = 6.7), we approximated and verified the effective k_a of the substrate column.

Hydraulic Property Measurements
Water Retention Curve
The water retention characteristic was directly measured by means of TDR and tensiometer sensors during evaporation and infiltration experiments. Data were then fitted with the van Genuchten water retention model (Eq. [2]), considering all parameters (_r, _s, , n, and m) as fitting coefficients.

Unsaturated Hydraulic Conductivity
Using the IPM, the unsaturated hydraulic conductivity was calculated by direct measurement from water content and water pressure data obtained during evaporation and infiltration experiments with the Darcy equation (Green et al., 1986). The water flow through the column q is calculated from the temporal changes in water storage at two depths (h₁ and h₂) as follows:

[9]

and the unsaturated hydraulic conductivity is then obtained by dividing the fluxes calculated above with the hydraulic gradient (d) at the same positions (dz = h₁ – h₂) and times:

[10]

Hydraulic conductivity models (van Genuchten-Mualem [VGM] and BC functions) and the van Genuchten retention model were simultaneously fitted to conductivity and retention data using the nonlinear least squares procedure RETC (van Genuchten et al., 1991). In the program, all hydraulic parameters (_r, _s, , n, m, K_s, and or B) contained in Eq. [2] and [3] and in Eq. [2] and [5] were considered as seven independant coefficients. Their values were estimated for the best fit between retention and conductivity models to the observed data during program optimization.

	RESULTS AND DISCUSSION

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Time Domain Reflectometry Calibration
Substrate moisture is plotted against the dielectric constant for peat and pine bark in Fig. 2 . Fitting the peat data simultaneously to Eq. [6] to [8] showed that the water content–dielectric constant relationship could also be represented by empirical equations (third-order polynomial regression of Topp et al. (1980) and second-order polynomial equation of Pepin et al., 1992) as a partly deterministic function (-model from Todoroff and Langellier, 1998), where all three R² values > 0.99 (Table 2). On the contrary, the Topp equation gave better results for pine bark (R² = 0.94) than the Pepin empirical regression (R² = 0.901) and model (R² = 0.89). Therefore, we used Topp's polynomial regression to estimate water content values for the two substrates.

View larger version (23K): [in this window] [in a new window]   Fig. 2. A comparison between observed data (symbols) and curves calculated with the equations of Topp et al. (1980), Pepin et al. (1992), and the -model (Roth et al., 1990). Each curve was simultaneously fitted to the data from the three replicate experiments for (a) peat and for (b) pine bark.

View larger version (39K): [in this window] [in a new window]   Fig. 3. Measured (symbols) and fitted (lines, VG) water retention curves during drying and wetting for (a) peat and (b) pine bark. Squares, diamonds, and triangles correspond to Replicates 1 to 3, respectively.

Comparison of fitted curves with observed data (Fig. 3) showed the flexibility of the van Genuchten model (VG) describing substrate retention properties, as previously shown by Milks et al. (1989) and Weiss et al. (1998). Regardless of the substrate, a high correlation between measured and fitted data was obtained for both the drying and wetting curves and throughout the tested potential range (Table 3). Parameters resulting from the VG are presented and compared with other substrates studies in Table 2. Our parameters are in good agreement with the steady-state measurements of Milks et al. (1989) but in poorer agreement with those of da Silva et al. (1993) or the transient results of Otten et al. (1999) with this kind of material. This could be the result of the fitting procedure, where only the parameter n was fitted in the studies of da Silva et al. (1993) and Otten et al. (1999), while in our study n and m parameters were fitted as in the study of Milks et al. (1989).

View this table: [in this window] [in a new window]   Table 3. Hydraulic model parameters of Eq. [2]: fitted values of s, r, , n, and m.

Hydraulic Conductivity
Calculated and fitted K() curves (Fig. 4 and 5) for both substrates were established over a wide range of water content. The hydraulic conductivity of peat decreased by about four orders of magnitude for water contents varying from 85 to 35%. However, K() varied slowly between 85 and 50% (more than one order of magnitude) and then decreased about three orders of magnitude between 50 and 35%. For the pine bark, higher K() decreased (five orders of magnitude) for a smaller water content range varying between 80 and 50%. The decrease was approximately one order of magnitude, between 80 and 55%), and was then very rapid (four orders of magnitude) around 50 to 55%. These results showed that the hydraulic conductivity K() of both substrates considerably decreased after the considerable variation in water content previously observed (Fig. 3), that is to say, after –5 and –3 kPa for peat and pine bark, respectively.

View larger version (46K): [in this window] [in a new window]   Fig. 4. Calculated (symbols) and predicted (lines) hydraulic conductivity as a function of volumetric water content for (a) peat and (b) pine bark. The drying and wetting hydraulic conductivity curves (van Genuchten–Mualem [VGM]) were predicted (Eq. [4]) from the fitted drying and wetting water retention curves (Eq. [2]).

View larger version (45K): [in this window] [in a new window]   Fig. 5. Calculated (symbols) and predicted (lines) hydraulic conductivity as a function of volumetric water content for (a) peat and (b) pine bark. The drying and wetting hydraulic conductivity curves (BC) were predicted (Eq. [5]) from the fitted drying and wetting water retention curves (Eq. [2]).

View larger version (50K): [in this window] [in a new window]   Fig. 6. Calculated (symbols) and predicted (lines) hydraulic conductivity as a function of water potential for (a) peat and (b) pine bark. The drying and wetting hydraulic conductivity curves (VGM) were predicted (Eq. [3]) from the fitted drying and wetting water retention curves (Eq. [2]).

The appearance of hysteretic phenomena could be related to the different structure of these two materials, fine and fibrous in the case of peat, and coarse and platy for pine bark. With regard to peat, these hysteresis phenomena were probably due to the change in the solid phase organization (swelling/shrinkage phenomena) and consequently in the pore interconnection, as well as to variations in wettability as was suggested by Michel et al. (2001). As was shown by Valat et al. (1991) and Michel et al. (2001), peat can acquire a hydrophobic character during extreme desiccation and could cause difficulties rewetting the substrate.

	CONCLUSIONS

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Substrate water retention and hydraulic conductivity curves were determined in the laboratory by a transient procedure (IPM), during a drying–wetting cycle. The relative simplicity and rapidity of this experimental procedure and its ability to directly estimate hydraulic characteristics make the method very appealing and seemed to be well adapted for determining these substrate characteristics. Nevertheless, methodological improvements should be implemented, particularly with regard to the shrinkage–swelling phenomena. Organic materials as peat showed substantial volume change (until 20%, Heiskanen, 1995) leading to a reorganization of the solid phase and could certainly affect hydraulic properties of substrate.

Results showed differences in the physical behavior of the two substrates studied. Hysteresis phenomena were evident in the () and the K() curves for peat, whereas this phenomenon was very limited for pine bark. Furthermore, the K() curve suggested that the hydraulic conductivity of both substrates considerably decreased after the high variation in water content observed in Fig. 3, that is to say, after –5 and –3 kPa for peat and pine bark, respectively.

The use of the VG (van Genuchten, 1980) retention model to describe the water retention characteristics of our two materials revealed a high correlation and seemed to be in agreement with other hydraulic studies of substrates and, more particularly, with results obtained on peat soils, due to the specific fine raw materials used. The use of unsaturated hydraulic conductivity models (VGM and BC) showed less agreement but they were also in agreement with results previously obtained on substrates.

These overall results implied that the hydraulic properties of tested substrates widely fluctuate within a narrow range of water potentials. Threshold values of water availability, obtained for the two substrates, seemed not to correspond to the common empirical value of –10 kPa (De Boodt and Verdonck, 1972) and could provide inaccurate predictions as to potential plant response. In practice, inaccurate predictions could rapidly affect water and air availability and lead to stress conditions in the root environment. Knowledge of K() and K() curves, in addition to the () curve, could contribute to alleviating water stress conditions and improve quality of growing media. Moreover, hydraulic properties (unsaturated hydraulic conductivity and water retention) should be associated with an aeration index of plant growth (e.g., gas relative diffusivity) and both combined in water–gas flow model to better understand the substrate-plant system and improve fertigation management.

Received for publication October 17, 2003.

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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