1. Introduction
Crack propagation is an important natural phenomenon in agricultural fields, especially in fine-grained soils such as the Luvisol of Belgium [
1,
2]. Cracks may act as a bypass for water and solutes, lower the soil water content and increase its matric suction [
3]. Factors affecting cracks are numerous and complex (e.g., mineralogy, temperature, moisture, thickness and other boundary conditions) [
4,
5]. However, it is commonly observed that the processes of crack initiation and propagation are water dependent [
6]. Cracks form when the tensile strength of the soil is reached during desiccation [
5].
The physical (structure, porosity and aggregates) and chemical properties (pH, organic carbon, cations and anions) of soil are supposed to influence the crack formation/propagation [
7]. These parameters are essentially affected by agricultural practices. Tillage and residue management influence the soil porosity and the fibre/organic matter content, which in turn affects the movement of water and gas [
8]. For instance, the addition of organic matter and biochar were found to decrease crack formation [
8,
9].
Tillage also influences the soil structural heterogeneity and the soil compressibility, which can affect the formation and propagation of cracks [
10,
11]. In fact, soil heterogeneity can affect the distribution of water, creating a weak area during shrinking [
12,
13]. Moreover, tillage breaks soil aggregates, modifies the structure, which can lead to soil compaction, and changes the soil permeability [
7,
14]. However, there are still some gaps in understanding the interaction between the cracks and soil hydraulic properties under varied agricultural practices. Lakshmikantha et al. [
4] and Tang et al. [
11] were the pioneers of analysing cracks and soil water properties. Later studies have found some links between the crack maximum width and water permeability [
3,
15].
Some authors have reported that under saturated surface conditions the evaporation depended only on the vapour pressure difference between the atmosphere and the sample surface (excluding cracks) [
16,
17], and others have mentioned the possible effect of macropores (and cracks), as they assure the water connectivity between the saturated and partially air-filled soil (which is called the drying front) [
18].
Despite previous researchers having found that cracks enlarge the soil porosity, increase the soil permeability and reduce the air entry value (AEV), which is the minimum suction at which air starts to enter the largest pores [
19], it is still difficult to include cracks in the soil water retention curve (SWRC) and permeability due to their geometric complexity. Moreover, cracks change in their configuration and size during soil desiccation [
15]. This requires that researchers include a dynamical analysis of cracks in parallel to a soil hydrodynamical assessment [
19]. However, due to crack complexity in form, size, configuration and distribution, crack analysis is often neglected in soil hydrology research, especially as agricultural science is concerned [
7,
20].
Therefore, this study assesses the dynamics of soil cracking and soil hydrology during drying in a controlled experiment for one soil (Luvisol) under three contrasted treatments (two types of samples under different management systems, and one type of disturbed/mixed sample). Each treatment has three replicates. The purpose of using a remolded sample is to question the impact of the agricultural soil structure. Furthermore, many cracking studies have utilised disturbed samples to represent agricultural soil [
4,
6,
11]. Therefore, this study assesses the differences between the disturbed and undisturbed samples. Moreover, this study will also evaluate the potential interaction between soil cracking and soil hydrology and will discuss the observed differences among the treatments.
2. Materials and Methods
2.1. Sample Collection and Preparation
We collected nine topsoil (0–15 cm) samples composed of three reduced-tillage-residue-in (RTRI), three conventional-tillage-residue-out (CTRO) and three disturbed soils (DS) (composite remoulded sieved samples from RTRI and CTRO). The remoulded samples are interesting, as they erase the tillage history and thus give a reference for the comparison of results. The RTRI and CTRO plots were each 15 m × 45 m and located 2 m apart. The samples were collected from an agricultural field in Gembloux, Wallonia, Belgium. The crop rotation in the study area was composed of cover crop (rapeseed: Brassica napus L.; mustard: Brassica nigra L.; oats: Avena sativa L.; peas: Pisum sativum L.), maize (Zea mays), winter wheat (Triticum aestivum L.) and sugar beet (Beta vulgaris L.). The preceding crop was sugar beet.
The samples were prepared from a rectangular steel box of 25 cm length × 20 cm width × 1.6 cm height or thickness (
Figure 1). The thickness was chosen to enhance the drying-induced cracking process according to the literature and the pretesting experiment [
5,
20]. The undisturbed samples (NDS = RTRI and CTRO) were saturated with distilled water from the bottom by a capillary rise for 24 h before the commencement of the experiment. In order to assure pore saturation, the water was levelled until 4–5 mm below the sample surface. The initial water content obtained in the NDS was between 45–50%. This value was obtained from the mass difference between the saturated sample and the oven-dried (105 °C 24 h) samples converted into volume divided by the total volume of the wet sample. The soils from the DS were subjected to crushing, oven drying at 40 °C, sieving at 2 mm diameter and moulding at 45–50% of water content (similar to NDS). At this moisture percentage the DS samples obtained a bulk density similar to the NDS samples without the need for further soil compaction. The slurry was uniformly poured into a metal box of the same size as that used for the intact soil. The overall sample was slightly shaken so to fill the box evenly and to remove some of the trapped air in the sample. The specimen was then covered and settled for 2 h prior the drying experiment.
2.2. Sample Physical/Chemical Characteristic and Fibre (Roots, Stubble and Straw) Analysis
Intact soils were collected from the core rings near the sampling location in three replicates in order to measure the soil bulk density and porosity. The formula of the bulk density and porosity is given below:
where BD and ρ
s are the soil bulk density and the particle density (g·cm
−3), Ф is the soil porosity, m
s is the mass of the oven-dried soil (g) (at 105 °C for 24 h), V is the volume of the core cylinder (cm
3), ρ
m is the mineral density (2.65 g·cm
−3), ρ
m: organic matter density (0.8 g·cm
−3) and V
m and V
om are the volume fractions of the mineral and organic matter (%).
Clay, silt and sand fractions were quantified using the pipette method (ISO 11464:2006). The organic carbon, humus content and pH of each treatment were measured according to the standard procedures (ISO 10694:1995; ISO 10390:2005). The pH meter measured the soil pH at a soil/water ratio of 1:5. The total carbon (TC) was calculated from the quantity of CO2 coming from the burning sample. The total organic carbon (TOC) required a preremoval of carbonic mineral by HCl 2N before burning the sample.
Based on the literature, the soil was classified as a Cutanic Luvisol [
21] mainly composed of silt (80 ± 2%), clay (15 ± 1.5%) and sand (5 ± 2%). The C:N ratio was between 10 and 12 with a C content of 20–35 g per kg of soil [
22], and the bulk density was roughly 1.3–1.4 g·cm
−3. After the experiment, crop straw, stubble and roots were sieved (2 mm) from the soil, spread on white paper, scanned by camera and quantified using image analysis.
2.3. Drying and Chamber Preparation
The experiment took place at the Gembloux Agro-Bio Tech laboratory. Drying was conducted in a small chamber heated with a lamp bulb (Exo Terra
® ceramic heat emitter). We preheated the chamber prior to the experimental setup as proposed by Tran et al. [
23]. Precision balance monitored the change in the sample weight every 15 min. Water tensiometer and pressure transducers (0–400/500 kPa) connected to a CR800 Datalogger (a programmable device by Campbell Scientific
® controlling sensors) measured the soil water suction, and were inserted in a horizontal position 4 cm inside the sample in the middle width section of the metallic box. The top surface of the sample was exposed to a free evaporation and monitored by 12 MP Canon
® cameras (Canon PowerShot SX520 HS
®) which took a picture every 30 min. We installed a Plexiglas
® at the base of each sample to hold the specimen and to avoid evaporation from its basal. Friction in the squared form grid (1 mm height, 1 cm length) was added in the interfacial between the soil and the Plexiglas
®. The experiment lasted for 7–10 days. Between 330 and 480 pictures were taken for each sample.
2.4. Determination of Moisture Content, Evaporation, Soil Water Retention Curve and Pore Size Distribution during Drying
2.4.1. Krischer’s Curve
The moisture content was calculated from the weight difference between the soil at a given time and the oven-
dried soil (105 °C for 24 h after 7–10 days of experimentation). The soil evaporation demonstrated the rate of water dissipation through the sample area per second (converted in cm day
−1) [
24]. The representative curves and their parameters in the result section were averaged from the three replicates. The Krischer curve treated the drying rate q (cm
3 day
−1) as a function of the water content (
θ) adjusted with the initial water content (
θo) of 0.5. The drying rate formulation is given below:
where
q is the drying rate (cm·day
−1), Soil mass (
t,
t − 1) is the change of soil mass during 10 min transformed into water volume (cm
3), A is the sample surface area (cm
2) and Δ
t is the time interval between the two measurements (day).
The maximal drying rates (qmax) and the critical water contents (Wcrit) were deduced from the graph of the drying curve versus the time. The critical water content corresponds to the time of the curve deflection after the plateau of the maximum drying rates [
25].
2.4.2. Soil Water Retention Curve (SWRC)
The SWRC represents the volumetric water content (
θ) against the water suction (in kPa). The volumetric water content is obtained from the weight difference between the wet and the oven-dried soil (105 °C for 24 h) converted into volume (water volume (cm
3) = water mass (g)/water density (1 g·cm
−3)) and over the total volume of the wet sample (800 cm
3 = 25 cm × 20 cm × 1.6 cm). In the calculation of the SWRC, the effective saturation is often used according to the following equation:
where
Se is the effective saturation,
θ is the water content at time t,
θs is the saturated water content and
θr is the residual water content (i.e., the water remaining in dry soil). Residual water content is the remaining water content at high tension obtained when the gradient
d (volumetric water content)/
dh (i.e.,
h: suction) becomes zero. In the calculation, the residual water content is considered to be zero.
The nonlinear equation of van Genuchten [
26] and of Durner [
27] fitted the monomodal and bimodal pore structures, respectively. The bimodal function of Durner [
27] is a combination of two VG models which considers the macropore (first modal) and the micropore (second modal). The fitting curves were calculated using the SWRC-fit package developed by Seki [
28]. The double model is given below:
where
α (cm
−1),
n1,
n2,
m1 and
m2 are the empirical parameters and m is calculated by
m = 1 − 1/
n, and
w1 and
w2 are the weighing factors.
w2 is set to zero for the monomodal curve, making the second part of the
Se formula equal to zero.
The three SWRC curves representing each treatment were obtained by averaging the volumetric water content and the water suction of the three replicates. The measured water suction was unfortunately limited to 400–500 kPa due to the classical water-filled tensiometer cavitation. For instance, we utilised a ceramic cylinder (0.5 cm diameter and 2.5 cm long) connected to a pressure transducer (BLPR2-IM-040606
® of World Precision Instrument
®) by a plastic tube filled with degassed water. The degassing procedure used vacuum instruments (i.e., a glass chamber and a pump) for 72 h. The problem of missing data was addressed by extrapolating the water suction in the range 400–15,000 kPa from the available water content data between 0–500 kPa using the fitted SWRC models (monomodal and bimodal). The quality of the fit is presented in
Appendix C and
Appendix D.
2.4.3. Pore Size Distribution (PSD)
The water retention curves were converted into PSD. The cumulative pore volume fraction of the
ith fraction, if we divide the SWRC in
n fractions, is equal to the ratio of the measured water content to the saturated water content, which is equal to Se at the
ith fraction:
where
Sei is the effective saturation (
Se) at
ith fraction,
vi is the volume fraction at
ith fraction and
n is the total number of the fraction (i.e., the number of portions of the SWRC).
The corresponding pore diameters (
di) at the
ith fraction is converted from the water matric suction using the Jurin’s law, which is simplified in the equation of [
29]:
The curve of the pore size distribution (PSD) was obtained by pairing the cumulative pore volume fractions in Equation (8) and the calculated pore diameter from Equation (7) [
10]. The obtained PSD was classified in three classes for statistical comparison. Pore diameters below 50 µm were classified as storage/residual/binding pores, those between 50 and 500 µm were transmissive pores and >500 µm were fissures [
10].
2.5. Image Processing and Analysis
Prior to the image processing, the images were corrected (tilt correction, geometry correction, etc.) using GIMP
® software. The image processing followed five main steps as proposed by Lakshmikantha et al. [
4], Tang et al. [
11], Le Roux et al. [
30] and Li et al. [
31] using ImageJ
® and PCAS
® packages. The first steps converted the RGB image into grey images and then into black (cracks) and white (background) pixels. This binarization process was based on the Otsu thresholding technique. The second step removed all the impurities and noises (e.g., dots and outliers) from the images using a series of techniques (e.g., filter, smooth, sharp, erode, dilate, noise removal and thickening). The skeletonization process during the third step transformed the binary image into skeleton, or thinned medial axis, images. The remaining steps (4–5) consisted in identifying cracks and measuring their width and length (
Figure 2).
The crack width and the crack length were estimated from the black pixels of the binary image using the PCAS software [
30]. The sum of the medial axis (i.e., crack branches) between the nodes gave the total crack length. The crack width was estimated from the Feret diameter of the fractal images. The Feret diameter is defined as the orthogonal distance between two parallel tangents [
32]. The crack width was calculated from the average width of each fractal image.
2.6. Statistical Analysis
All the graphs represent the averaged data from the three replicates per treatment (i.e., each treatment which has three replicates). The relationship between the evaporation rates/water content/water suction and crack length were analysed using the Pearson correlation. The strength of the relationship was assessed statistically with linear regression. The parameters of the Krischer’s curve and the SWRC (monomodal and bimodal curves) were compared statistically among the treatments (i.e., RTRI, CTRO and DS). Repeated measures of ANOVA with mixed models (restricted maximum likelihood (REML) and compound symmetry) from XLSTAT software analysed the pore distribution class between the treatments. All statistical analyses were performed at a confidence level of 95%. The RMSE (root mean square error) and R2 (determination coefficient) of the extrapolated water suction from the SWRC versus the observed data were also calculated.
4. Conclusions
Crack formation during soil desiccation appears to be strongly linked to the soil structure (i.e., highly affected by the remolding for the DS) and to a lesser extent the agricultural management system. We have assumed that the difference between the disturbed and undisturbed samples reside in their structural characteristics despite no specific soil structural analysis done in this study. Using the disturbed and undisturbed soil samples, we observed that:
- (1)
The crack initiation and development were driven differently (among treatments) by the water suction. The crack initiation required a stronger negative suction and a lower water content in the DS compared to the NDS. Concerning the NDS, the crack started at a slightly weaker (i.e., lower) suction (<100 kPa) in the RTRI than in the CTRO;
- (2)
For the NDS, the soil structure was assumed to restrict further expansion during stronger suction. The DS could swell and shrink greatly, thereby producing thicker and longer cracks. This could be triggered by the lack of sample structure and the removal of frictional elements (i.e., sand, roots and fibre). The crack expanded faster in the NDS when the critical water content was reached and the evaporation started to drop (i.e., the end of constant evaporation rate period; CRP);
- (3)
The soil water permeability was commonly related to the soil pore size distribution. The RTRI and the DS contained significantly higher macropores (>50 µm) than the CTRO. The rate and the duration of the CRP in the RTRI compared to the CTRO was related to the large pores (>50µm) and the potentially earlier forming cracks (at <100 kPa). This helped to explain the lower evaporation rate in the DS despite its high cracking length;
- (4)
The cracks seem not to have affected the SWRC during the first drying, since they arrived late (>100 kPa) in the curve (for both the CTRO and the DS). However, the shrinkage observed brought about structural changes inside the sample even at a lower suction. After the first drying, the cracks changed the pore size distribution of the sample, which should affect the subsequent wettings and dryings.
The results of this study have revealed that soil disturbance (DS) changed the soil cracking pattern while decreasing the rate of evaporation and probably reducing the soil permeability. Therefore, care should be taken when analysing cracks through remoulded samples. Moreover, despite the small differences among the undisturbed samples, the RTRI presented slightly higher cracks, higher evaporation and higher macropores than the CTRO. Future studies should include other agricultural practices (e.g., the use of biochar or sticking mucilage, etc.) under laboratory and/or field conditions.