Hydrological Properties of a Clay Loam Soil as Affected by Biochar Application in a Pot Experiment

: Improving soil-water relations by amending soil with biochar might play a signiﬁcant role in increasing water availability for agricultural crops as well as decreasing water loss through drainage or runoff. While the effects of biochar on the hydrological properties on coarse-textured soils are generally positive and well-documented in the literature, studies on biochar effects on ﬁne-textured soils are rather scarce and even contradictory. Therefore, the aim of this paper was to investigate the impact of biochar on the bulk density, water retention curve (together with several water capacitive indicators) and water inﬁltration rate in a clay loam soil. A pot experiment was carried out under lab conditions in which biochar was mixed with soil at rates of 0 (B0 or control), 2, 4, 6, 8 and 10% dw (B2, B4, B6, B8 and B10, respectively). Water retention of soil–biochar mixtures at different matrix potentials was determined using a pressure plate apparatus. From these measurements, a series of capacitive indicators was derived and the ﬁtting of the van Genuchten model was also performed. Water inﬁltration into soil–biochar mixtures was measured by means of a mini-disk inﬁltrometer and the obtained data were analyzed both directly and by ﬁtting the Philip’s model. Biochar signiﬁcantly affected the considered soil properties. As the biochar rate increased, the bulk density decreased and water retention increased (B6, B8 and B10 > B2, B4 and B0), while the inﬁltration rate decreased (B0 > B2, B4, B6, B8 and B10). Although the experiment was performed on sieved and repacked soil samples under lab conditions, the results conﬁrmed that biochar has the potential to increase plant-available water, while possibly reducing drainage water in a clay loam soil by lowering the inﬁltration rate.


Introduction
Over the last decade, the use of biochar as a soil organic amendment has attracted increasing interest for its environmental and agronomic benefits, which is well-documented in several reviews and meta-analyses [1][2][3][4]. Biochar is a carbon-rich (60-80% C) material that can be obtained from different feedstocks (forest wood, manure, sewage sludge, agriculture residues) under a high-temperature treatment (from 300 to 1200 • C) and in an oxygen-limited environment (pyrolysis or gasification) [5][6][7]. With regard to environmental sustainability, biochar, due to its long-term stability in soil, has been reported to have great climate mitigation potential [8] for its ability to sequester atmospheric CO 2 and reduce greenhouse gas (GHG) emission [9]. Concerning its agronomic implications, biochar has been shown to increase physical, chemical and microbiological soil fertility as well as reduce nutrient leaching and improve crop yield response [10][11][12][13]. In recent years, biochar application has also been suggested for controlling soil and water pollution by holding contaminants that are both inorganic (nitrogen, phosphorous, heavy metals) and organic (pesticides, herbicides, antibiotics) [14]. These biochar properties are mainly due to its In a previous paper from our research team [13], we reported the results of biochar application to a loam-texture soil in a 2-year field experiment made of four consecutive crop-growing cycles. The applied biochar played an important role in reducing water drainage loss, thereby limiting nutrient leaching. However, a "threshold effect" was observed: when a lower biochar amount was applied, the increase in water retention prevailed over the increase in water conductivity, causing diminished drainage; conversely, when a higher biochar amount was added, the reverse effect occurred, and drainage was greater. This must be considered our starting study hypothesis that must be verified. We did not perform any direct measurements of hydraulic soil properties in that previous field experiment, and our supposed explanation cannot be demonstrated until a new field experiment is performed. Nevertheless, prior to performing this further field trial, we considered working out a pot experiment in order to shed some light on this specific issue. Therefore, strictly controlled lab conditions were applied to a fine-textured soil similar to the one previously used. We are aware that, compared to in situ soils, the use of sieved and repacked soil in pots can be of concern when studying soil physical properties. Indeed, soil structure, pore architecture and pore size distribution are deeply altered when sieved and repacked soil is used, and the consequent soil hydraulic properties can change dramatically. However, under these strictly controlled experimental conditions, more extensive and accurate measurements can be performed and the effects of different biochar addition rates can be better compared and discriminated.
The aim of the present study was to evaluate whether biochar application could affect soil hydraulic properties in a clay loam soil in pots under lab conditions. The effect of different rates of biochar addition on bulk density, water retention curve, together with a set of water-capacitive indicators and water infiltration rates, was analyzed. No attempts were made to extrapolate the obtained results to "in-field" soil conditions, but deeper insights into how biochar operates in a soil mixture at different addition rates can surely promote more focused research activities in the open field. Figure 1 shows the sequential phases applied when performing our experiment: from soil and biomass sampling, through biochar production and preparing soil-biochar mixtures, to measurements and data interpretation. 021, 11, x FOR PEER REVIEW 3 of 20 the mechanisms are the improvement of aggregation and aggregate stability by biochar [39]. However, some exceptions to this general scheme were also detected [40][41][42][43].

Materials and Methods
In a previous paper from our research team [13], we reported the results of biochar application to a loam-texture soil in a 2-year field experiment made of four consecutive crop-growing cycles. The applied biochar played an important role in reducing water drainage loss, thereby limiting nutrient leaching. However, a "threshold effect" was observed: when a lower biochar amount was applied, the increase in water retention prevailed over the increase in water conductivity, causing diminished drainage; conversely, when a higher biochar amount was added, the reverse effect occurred, and drainage was greater. This must be considered our starting study hypothesis that must be verified. We did not perform any direct measurements of hydraulic soil properties in that previous field experiment, and our supposed explanation cannot be demonstrated until a new field experiment is performed. Nevertheless, prior to performing this further field trial, we considered working out a pot experiment in order to shed some light on this specific issue. Therefore, strictly controlled lab conditions were applied to a fine-textured soil similar to the one previously used. We are aware that, compared to in situ soils, the use of sieved and repacked soil in pots can be of concern when studying soil physical properties. Indeed, soil structure, pore architecture and pore size distribution are deeply altered when sieved and repacked soil is used, and the consequent soil hydraulic properties can change dramatically. However, under these strictly controlled experimental conditions, more extensive and accurate measurements can be performed and the effects of different biochar addition rates can be better compared and discriminated.
The aim of the present study was to evaluate whether biochar application could affect soil hydraulic properties in a clay loam soil in pots under lab conditions. The effect of different rates of biochar addition on bulk density, water retention curve, together with a set of water-capacitive indicators and water infiltration rates, was analyzed. No attempts were made to extrapolate the obtained results to "in-field" soil conditions, but deeper insights into how biochar operates in a soil mixture at different addition rates can surely promote more focused research activities in the open field. Figure 1 shows the sequential phases applied when performing our experiment: from soil and biomass sampling, through biochar production and preparing soil-biochar mixtures, to measurements and data interpretation.

Soil and Biochar Preparation and Characterization
Soil samples were collected from the 0-0.20 m upper soil layer in Foggia, South Italy (latitude 41 • 27 N, longitude 15 • 32 E). The soil was oven dried at 105 • C until reaching a constant weight, after which it was crushed and passed through a 2 mm sieve. The particle Agronomy 2021, 11, 489 4 of 20 size distribution was analyzed by the pipette-gravimetric method and the soil type was determined according to the USDA (United States Department of Agriculture) soil classification [44]. The pH was measured on a 1:2.5 (w/v) soil/water suspension with deionized water by a digital pH meter (GLP 22 pH-meter, Crison Instruments, Barcelona, Spain) and the electrical conductivity (EC) on saturated soil paste extract by a digital conductivity meter (GLP 31 EC-meter, Crison Instruments, Barcelona, Spain). Total nitrogen (total N) was determined by the Kjeldahl method [45] and the available phosphorus (P 2 O 5 ) by extraction with sodium bicarbonate [46]. Organic carbon was determined by the dichromate oxidation method [47] and converted to organic matter (OM) by the conventional factor 1.724. The soil was of a clay loam texture, poor in nitrogen but rich in available phosphorus, with low organic-matter content, an alkaline pH and low electrical conductivity (Table 1). Biochar applied to soil was produced at the STAR*Facility Centre, Foggia University, from residual vine biomasses (Vitis vinifera L.) collected from a local vineyard. The pruning residues (15% humidity) were chipped into particles of approx. 50 mm, mixed and then pyrolized at a temperature of 750 • C for 8 h, in a pilot scale with a fixed-bed tubular reactor (30 L capacity). The heating rate was 10 • C min −1 . Once cooled, it was ground and passed through a 2 mm sieve.
The pH and electrical conductivity (EC) were determined on a 1:20 (w/v) soil/biochar suspension with deionized water, after shaking and waiting an equilibrium time of 90 min before measurement by a pH meter (GLP 22+ Crison Instruments, Barcelona) and an ECmeter (GLP 31+ Crison Instruments, Barcelona), respectively. The proximate analysis-the fixed carbon, volatile solids, ash and water content--was carried out by a TGA Analyzer (LECO-TGA701). The ultimate analysis of the carbon (C), hydrogen (H) and nitrogen (N) content was performed by dry combustion using a CHN Elemental Analyzer (CHN LECO628). In the case of C org , dry combustion was preceded by inorganic C removal with HCl. Similarly, sulfur (S) was determined using a S module (S LECO628) combined with the CHN Elemental Analyzer. Oxygen (O) was calculated by difference. The carbon stability of biochar was evaluated indirectly by the molar ratios of hydrogen to organic carbon (H/C org ) and oxygen to organic carbon (O/C org ). The micro-and macro-elements were determined by digesting the dried biochar sample in concentrated HNO 3 , in a closed vessel microwave digester (CEM-Mars6). The metals in the solution were then analyzed by inductively coupling plasma optical-emission spectroscopy (ICP-OES Agilent 720). The physicochemical characteristics of the obtained biochar are listed in Table 2. It was characterized by the typical alkaline pH value [48] and resulted in a higher EC than the receiving soil. The prepared biochar fully complied with the standards established by both EBC (European Biochar Certificate) [49] and IBI (International Biochar Initiative) [50] because the C content was higher than 50%, the C org higher than 60%, the H/C org ratio lower than 0.7 and the O/C org ratio lower than 0.4. Taking these properties into account, the available biochar was assigned to the Class 1 grade, while it also offered long-term carbon stability and persistence.
After soil-biochar mixture preparation, 5 L cylindrical plastic pots (internal Ø = 20 cm; h = 16 cm) were filled with the same mixtures. An equal volume of each soil-biochar mixture was placed in every pot without intentional compaction and each experimental treatment was done three times. Therefore, 18 pots made up the experimental setting to address the bulk-density measurement (Section 2.3) and the infiltration trial (Section 2.7). The other 18 pots (prepared according to the same procedure) were used to extract smaller soil samples to address the pressure plate apparatus (Section 2.4). Moreover, three identical pots were filled with an equal volume of biochar only. All these pots were sealed with a plastic sheet and kept at room temperature (22 ± 0.5 • C) for three months until the experimental tests were performed.

Bulk Density of Soil-Biochar Mixtures
The bulk density of both the soil-biochar mixtures and biochar alone was determined immediately before the start of the hydrological tests. After three months of storage, the soil-biochar mixtures in the pots did not show any kind of shrinkage. By gently trimming at the top it was possible to bring all the pots to full and equal volume without causing any disturbance to the underlying mixture. Pots were weighed and the bulk density (g cm −3 ) of each soil-biochar mixture (ρ m ) was determined as the ratio of the net soil-biochar mixture weight to the pot volume. Considering that treatment B0 represented the non-amended soil, its bulk density corresponded to the bulk density of the soil alone (ρ s ), that is, without biochar addition. The same procedure was also applied to the three pots filled with biochar only. In this way, it was possible to estimate the bulk density of biochar alone (ρ b ).

Water Content of Soil-Biochar Mixtures at Different Water Potentials
A pressure-plate apparatus equipped with a porous ceramic membrane (Soil Moisture Equipment Corp., Santa Barbara, CA, USA) was used to measure water content (ω, g g −1 ) of soil-biochar mixtures, according to the Richards' method [51]. Samples of an equal volume of dry soil-biochar mixtures were excerpted from the pots using PVC sampling rings (Ø = 52 mm; h = 1 cm; volume = 21 cm 3 ). On theses samples, the bulk density of the soil-biochar mixtures was further calculated in parallel with the procedure already reported in Section 2.3. Then, each sample was saturated with distilled water through partial soaking and by imbibition from below. As soon as saturation was reached, every sample was weighed and then placed on a porous ceramic plate before being placed into the pressure chamber. By using an air compressor, a constant pressure was established inside the chamber. The selected pressure corresponded to a specific value of the assigned matrix potential to be tested. Seven subsequent matric potentials (ψ, kPa), namely, saturation (ψ = 0 kPa) and ψ = −10, −30, −50, −100, −500 and −1500 kPa were selected.
The mixture ring samples were finally weighted and then dried at 105 • C for 24 h. At every pressure step, the mixture water content was determined gravimetrically with respect to the dried weight. Therefore, the water content at each matric potential was expressed as percentage by weight of the soil-biochar mixtures (ω, g g −1 ).
The previously determined bulk density of each soil-biochar mixture (ρ m ) allowed converting the water content from a gravimetric (ω, g g −1 ) to a volumetric basis (θ, cm 3 cm −3 ), according to the following equation: where ρ w is the water density currently assumed equal to 0.998 g cm −3 at 20 • C.

Soil Water Desorption Curve of Soil-Biochar Mixtures
Water content data on volumetric basis were used to describe the water desorption curve of the six soil-biochar mixtures. For this purpose, the van Genuchten model [54] was fitted to the experimental data by applying the following equation: where: θ (ψ) is the actual soil water content (cm 3 cm −3 ) at the matric potential ψ (kPa); θr is the residual soil water content (cm 3 cm −3 ) at limit values of matric potential (ψ → ∞); θs is the saturated water content (cm 3 cm −3 ), assuming equivalence with total porosity; α is a dimensionless parameter related to the inverse value of the air entry potential (kPa −1 ); n and m are dimensionless curve shape parameters. In particular, m characterizes the asymmetry of the curve and the Mualem constraint m = 1 − 1/n was applied [55]. Therefore, Equation (10) only contains the following four unknown parameters: θs, θr, α, and n to be determined by a statistical fitting procedure (Section 2.5). Once specific estimates are assigned to the model parameters, further indicators useful in the soil-biochar mixture comparisons are the following: • matric potential of the inflection point of the water desorption curve (ψi, kPa): • water content at the inflection point, (θi, cm 3 cm −3 ):

Soil Water Desorption Curve of Soil-Biochar Mixtures
Water content data on volumetric basis were used to describe the water desorption curve of the six soil-biochar mixtures. For this purpose, the van Genuchten model [54] was fitted to the experimental data by applying the following equation: where: θ (ψ) is the actual soil water content (cm 3 cm −3 ) at the matric potential ψ (kPa); θr is the residual soil water content (cm 3 cm −3 ) at limit values of matric potential (ψ → ∞); θs is the saturated water content (cm 3 cm −3 ), assuming equivalence with total porosity; α is a dimensionless parameter related to the inverse value of the air entry potential (kPa −1 ); n and m are dimensionless curve shape parameters. In particular, m characterizes the asymmetry of the curve and the Mualem constraint m = 1 − 1/n was applied [55]. Therefore, Equation (10) only contains the following four unknown parameters: θs, θr, α, and n to be determined by a statistical fitting procedure (Section 2.5). Once specific estimates are assigned to the model parameters, further indicators useful in the soil-biochar mixture comparisons are the following: • matric potential of the inflection point of the water desorption curve (ψ i , kPa): • water content at the inflection point, (θ i , cm 3 cm −3 ): • slope of the water retention curve at the inflection point (S i ): It should be specified that the soil water release or desorption curve is expressed as volumetric water content (θ, cm 3 cm −3 ) versus the absolute value of pore water tension (ψ, kPa) is expressed as natural logarithm: ln|ψ|.
The pore volume distribution function, P(ψ), may be defined as the slope (first derivative dθ/dψ) of the water release curve expressed as volumetric water content θ (cm 3 cm −3 ), versus ln(ψ), and plotted against the equivalent pore diameter, δe (µm), on a logarithmic scale [52]. The equivalent pore diameter (δe) may be determined according to the Young-Laplace equation, and assuming that the pores are perfectly cylindrical, uniform, and equally drained, according to the following formula [41,52]: with δe (µm) and ψ (kPa). Therefore, the van Genuchten curves were converted into pore volume distribution functions, and this can be considered the ultimate way to compare the six soil-biochar mixtures with respect to their hydraulic properties.

Water Infiltration of Soil-Biochar Mixtures
Water infiltration in the pots filled with the six soil-biochar mixtures was measured using a mini-disk infiltrometer (Meter Group Inc., Pullman, WA, USA). A constant pressure head of −2 cm was applied [56]. The amount of water (cm) that infiltrated into the soil-biochar mixtures was recorded manually at intervals of 10 s until all water from the reservoir chamber was infiltrated. The infiltration test was applied to every available pot and; therefore, three infiltration measurements were performed for each soil-biochar mixture.
Cumulative infiltration (I C , cm) was measured over time and the infiltration rate (I R , cm s −1 ) was determined as the difference between two cumulative infiltration (I C ) readings over the corresponding time interval (t): In particular, three values of average infiltration rate were calculated. The initial infiltration rate (I Rstart ) was determined over the first 60 s of the infiltration run, the final infiltration rate (I Rend ) determined over the last 60 s of the infiltration run and, finally, the total infiltration rate (I Rtotal ), determined as the ratio of the total infiltrated water volume over the total time of the experimental infiltration process.
Cumulative infiltration (I C ) data were analyzed by fitting the Philip's infiltration model [56][57][58], as follows: where: I C (cm) is the cumulative infiltration; C 1 (cm/s 1/2 ) is the soil sorptivity; C 2 (cm/s) is the parameter related to soil hydraulic conductivity; t is the time (s). A mathematical fitting procedure was applied to estimate the two coefficients, C 1 and C 2 , and their assigned values were used as further indicators to compare the hydraulic properties of the soilbiochar mixtures. The instantaneous infiltration rate was determined by differentiating, with respect to time, the cumulative infiltration as reported in the following equation:

Statistical Analysis
The soil-biochar bulk-density data were processed according to an ANCOVA model considering the biochar addition rate (from zero to 10%) as the regressing variable. Both "expected" and "observed" data were the response variables. The former being the data theoretically compatible with a simple "dilution" effect of biochar in the mixtures, while Agronomy 2021, 11, 489 9 of 20 the latter the actually measured data. In other words, a "lighter" component (the lower density biochar) when added to a "heavier" medium (the higher density soil) produced a decrease in the mixture bulk density proportional to the amount (weight) of the added component, according to the following equation: where, as already stated, X is the percentage of the added biochar, while the subscript i refers to the soil-biochar specific treatment. According to Equation (18), the expected values of bulk density can be calculated. Therefore, the ANCOVA model allowed testing if the decreasing effect of biochar on bulk density (i.e., the slope of the regression line) was statistically different in the two compared data sets (expected vs. observed).
A one-way ANOVA test was applied to statistical process all the water-capacitive indicators as well as the average infiltration rates with respect to the experimental treatments (the six soil-biochar mixtures). A mean comparison was performed via a Tukey honestly significant difference (HSD) test, and a 0.05 probability level was applied. A multivariate analysis was also performed on the water-capacitive indicators, which were processed as a whole through a cluster analysis. The "average linkage method" was applied, and the resulting dendrogram was represented considering the detected clusters.
The parameter estimates of the two models, van Genuchten and Philip's, were obtained through an iterative, nonlinear, least-square fitting procedure. In this respect, a "stepwise" approach was applied according to the following procedure: 1.
The model was fitted independently to the data from every experimental Bi treatment (from B0 to B10) to obtain specific parameter estimates. The overall experimental error (SSe-Sum of Square error) was obtained as the sum of SSe related to each treatment curve (ΣSS i ). 2.
The model was then fitted to the complete dataset, regardless of experimental treatments, to obtain a single, general estimate of each model parameter.

3.
The general model (point 2) was gradually relaxed, one parameter at a time, keeping the others fixed. The statistical significance of an independent estimates of each Bi treatment i was evaluated, one parameter at a time, by means of an F Fisher test against the error term (SSe).

4.
We proceeded with point 3 in order of relevance, from the most to the least influential parameter. The additional SS, accounted for by the model when fitting Bi-independent estimates of each parameter, represented its relevance.

5.
A significant F-test allowed the conversion of the model from one single estimate of the considered parameter to different estimates, according to a multiple range comparison among the Bi treatments. A non-significant F-test allowed the preservation of a unique estimate of the considered parameter. 6.
The same procedure was repeated each time considering the remaining model parameters; every time, a new fitting procedure assigned new estimates to the remaining parameters.
All data processing and statistical tests were performed using the JMP statistical software package, version 11 (SAS Institute Inc, Cary, NC, USA).

Bulk Density of Soil-Biochar Mixtures
Two independent and parallel bulk-density estimates of the six soil-biochar mixtures were obtained. The first estimates derived from weighing the entire pots (Section 2.3), while the second from weighing the smaller ring samples placed in the pressure-plate apparatus (Section 2.4). No statistical differences were detected between the two datasets (data not showed). Since the first estimates came from a greater volume of mixtures (the pots) than the second ones (ring samples), the former were considered more reliable and taken as a reference.
The bulk density of the soil-biochar mixtures decreased linearly with the biochar fraction added to the soil (Figure 3).
Two independent and parallel bulk-density estimates of the six soil-biochar mixtures were obtained. The first estimates derived from weighing the entire pots (Section 2.3), while the second from weighing the smaller ring samples placed in the pressure-plate apparatus (Section 2.4). No statistical differences were detected between the two datasets (data not showed). Since the first estimates came from a greater volume of mixtures (the pots) than the second ones (ring samples), the former were considered more reliable and taken as a reference.
The bulk density of the soil-biochar mixtures decreased linearly with the biochar fraction added to the soil (Figure 3). Our results were consistent with other experimental studies [1] as well as meta-analysis [59]. Most studies showed a reduction in bulk density as a consequence of biochar addition although few cases also reported increase in bulk density [17]. A biochar addition roughly higher than 2% by weight should be enough to create a significant decrease in bulk density [60]. The rate of biochar application as well as the density and porosity of the original soil are critical for predicting the effects of a biochar addition to soil. In our experiment, the biochar bulk density was 0.321 ± 0.074 g cm −3 , while the soil bulk density was 1.191 ± 0.079. Therefore, considering an addition of biochar (10% by weight), a bulk density decrease of 12.6% was actually detected compared to an expected decrease of 7.3% if only a "dilution" effect were supposed. This decrease, therefore, does not follow the proportions resulting from a simple "dilution" effect (red line in Figure 3) since the reduction observed (blue line in Figure 3) was significantly stronger than expected. This was proved by the result of the ANCOVA test reported in Table 3. Our results were consistent with other experimental studies [1] as well as metaanalysis [59]. Most studies showed a reduction in bulk density as a consequence of biochar addition although few cases also reported increase in bulk density [17]. A biochar addition roughly higher than 2% by weight should be enough to create a significant decrease in bulk density [60]. The rate of biochar application as well as the density and porosity of the original soil are critical for predicting the effects of a biochar addition to soil. In our experiment, the biochar bulk density was 0.321 ± 0.074 g cm −3 , while the soil bulk density was 1.191 ± 0.079. Therefore, considering an addition of biochar (10% by weight), a bulk density decrease of 12.6% was actually detected compared to an expected decrease of 7.3% if only a "dilution" effect were supposed. This decrease, therefore, does not follow the proportions resulting from a simple "dilution" effect (red line in Figure 3) since the reduction observed (blue line in Figure 3) was significantly stronger than expected. This was proved by the result of the ANCOVA test reported in Table 3. As far as we know, only one other paper [53] compared the expected "dilution" data with the actual observed data of bulk density, thereby showing that the effect of biochar in reducing the soil bulk density was more than proportional to the simple contribution of its weight. Consequently, it is appropriate to call into question the effects of the spatial arrangement of biochar particles in relation to soil particles or small aggregates. This implies an increase in total porosity not only due to the intrinsic biochar microporosity, but also due to a soil-biochar interporosity. Apart from reasons of simple particle spatial arrangement, the effect of soil-biochar interaction may also lead to the development of structural aggregates. Considering that biochar increases the mixture cation exchange ca-pacity (CEC), the bridging of clays and organic matter may be promoted; on the other hand, the increase in soil reaction (pH) may also increase surface nonacidic cations bridging soil colloids [53]. A better particle aggregation may promote soil structuring and a decrease in bulk density. Considering the limited time available for soil structuring in this experiment, we believe the former factor should be more effective than the latter.

Water Capacitive Indicators of Soil-Biochar Mixtures
The one way ANOVA test performed on each capacitive indicator (Table 4) gave quite clear results concerning the different hydraulic properties of the six soil-biochar mixtures. TAW, PAW, RAW, and LAW increased progressively with increasing biochar application fractions; conversely, DRW decreased. In general, biochar effectively contributed to improved soil quality with respect to water plant-soil relationships. When comparing the maximum range between treatment pairs, water holding capacity (TAW) and water availability to plants (PAW, RAW and LAW) increased significantly. Mostly importantly, the highest increase was observed in RAW (+73%), followed by PAW (+61%) and LAW (+54%), while DRW was reduced by approximately 46%. Not only the amount of available water to plants was significantly expanded, but also the amount of drainage water (lost water or only temporary available to plants) was significantly reduced. Concerning the relative indicators, PAW/TAW grew significantly larger with the biochar addition (+32%, the maximum range between treatment pairs), while RAW/PAW did not significantly change. Several experimental evaluations [17], both in the field and in the lab, demonstrated that soil amendment with biochar is associated with a significant increase in PAW. The magnitude of this increase is related to the soil's being higher in coarse-textured and lower in fine-textured soils, as well as roughly proportional to the biochar application rate. Similar to what was previously observed with bulk density, this effect is mostly interpreted in reference to a remarkable increase in the total soil porosity and specific surface area [59]. According to the same meta-analysis [59] the effect of biochar on PAW increases as its porosity increases. Biochar has been seen to increase not only total soil porosity but also pore connectivity and the number of pores [61,62], thus increasing soil water retention. As remarked already, biochar intrapores (pores inside the biochar particles) together with soil-biochar interpores jointly contribute to increasing water retention [63]. According to our results, the increase in PAW due to the biochar addition was mostly determined by a significant increase in water content at FC (+27.9%, the maximum range between treatment pairs).
The treatment with the highest biochar fraction did not always show the most noticeable results; indeed, with respect to PAW and RAW, it was B8, not B10, that offered the best soil conditions although it was not statistically different. A sort of "threshold effect" in biochar application was detected in this lab experiment. Interestingly, the same effect was also observed in our previous field-conducted experiment [13]. Indeed, B8 and B10 showed a general trend (not statistically confirmed) to swap places. This was particularly clear when the result of the cluster analysis jointly applied to all the data reported in Table 4 are shown (Figure 4). A clear split into two clusters (B0-B2-B4 vs. B6-B8-B10) was shown. Moreover, a closer "friendship" between B6 and B10 rather than B8 and B10 was observed. It is difficult to provide an explanation of the observed swapping between B10 and B8, but it seemed that no further porosity was added to the mixture; that is to say, the "useful" porosity to the plant water supply was partially lost in B10 with respect to B8.
The treatment with the highest biochar fraction did not always show the most noticeable results; indeed, with respect to PAW and RAW, it was B8, not B10, that offered the best soil conditions although it was not statistically different. A sort of "threshold effect" in biochar application was detected in this lab experiment. Interestingly, the same effect was also observed in our previous field-conducted experiment [13]. Indeed, B8 and B10 showed a general trend (not statistically confirmed) to swap places. This was particularly clear when the result of the cluster analysis jointly applied to all the data reported in Table  4 are shown (Figure 4). A clear split into two clusters (B0-B2-B4 vs. B6-B8-B10) was shown. Moreover, a closer "friendship" between B6 and B10 rather than B8 and B10 was observed. It is difficult to provide an explanation of the observed swapping between B10 and B8, but it seemed that no further porosity was added to the mixture; that is to say, the "useful" porosity to the plant water supply was partially lost in B10 with respect to B8.  Table 4. A clear split into two clusters (B0-B2-B4 vs. B6-B8-B10) is shown. Moreover, a closer "friendship" between B6 and B10 rather than B8 and B10 can be observed. This particular behavior suggests a possible threshold effect of biochar application to soil. (Read the text for an in-depth explanation).

Water Retention Curves of Soil-Biochar Mixtures
The fitting procedure of the van Genuchten equation, performed according to a "stepwise" approach, provided a model consisting of only two curves significantly different from each other. The first curve included the following treatments: B0, B2 and B4, while the second one the other three treatments: B6, B8 and B10. The pair of curves differed significantly in the value of θs and n (Table 5), while neither α nor θr had statistically different results among the Bi treatments (Table 5). In particular, θr never differed significantly from zero in any Bi treatment, and for this reason its value was zeroed (Table 5).  Table 4. A clear split into two clusters (B0-B2-B4 vs. B6-B8-B10) is shown. Moreover, a closer "friendship" between B6 and B10 rather than B8 and B10 can be observed. This particular behavior suggests a possible threshold effect of biochar application to soil. (Read the text for an in-depth explanation).

Water Retention Curves of Soil-Biochar Mixtures
The fitting procedure of the van Genuchten equation, performed according to a "stepwise" approach, provided a model consisting of only two curves significantly different from each other. The first curve included the following treatments: B0, B2 and B4, while the second one the other three treatments: B6, B8 and B10. The pair of curves differed significantly in the value of θs and n (Table 5), while neither α nor θr had statistically different results among the Bi treatments (Table 5). In particular, θr never differed significantly from zero in any Bi treatment, and for this reason its value was zeroed (Table 5). Table 5. Results of the "stepwise" procedure applied in performing the parameter fitting of the van Genuchten model to the six soil-biochar mixtures (Bi treatments). Standard error of the estimates, together with their confidence intervals at 95% of probability are also reported. The most influential effects of biochar application was a significant increase in the total volume of water at saturation (θs). As a consequence, the soil water content also increased at lower water potentials, thus improving plant water availability. The increase in the value of the n parameter further operated in this direction. Indeed, the increase in the n "shape" parameter accentuated the steepness of the curve, simultaneously increasing the water volume at FC and, by contrast, decreasing it at WP. Consequently, a remarkable increase in PAW was obtained and this effect represented another relevant result of biochar application: a significant enhanced capacity of the amended soil to hold water not only at soil saturation but also at unsaturated soil conditions.

Parameter
Beyond the rigorous statistical separation performed with the "stepwise" approach, both θs and n showed a gradual, progressive increase in their values from B0 to B10 (Table 6). In contrast, no kind of trend was detected with respect to the α parameter (Table 6). Table 6. Estimates of the van Genuchten model parameters to the six soil-biochar mixtures (treatments) without the clustering operations applied in the stepwise approach. (Compare Table 6 with Table 5). The effect of the biochar application on both θs and n allowed us to deduce that its main consequence is a substantial, additional contribution to soil porosity that can increase water sorption capacity. While the increasing trend on θs seemed quite regular, the values related to the n parameter reached a maximum at B8 and did not increase at B10, thus limiting any further increase in soil water-holding capacity (Table 6). Again, as previously noticed, a peculiar effect of biochar at a higher application rate (although not statistically confirmed) was observed.
According to the statistical results reported in Table 5, Figure 5A shows the two desorption curves with respect to the first (B0, B2 and B4) and the second (B6, B8, B10) group of treatments. The two curves differed significantly, visually confirming what was previously observed with regard to the capacitive indicators and the van Genuchten parameters. They showed similar values around and beyond water potential (WP) (N.B.: ψ in absolute terms); conversely, they showed a divergent trend at increasingly lower water potentials (N.B.: ψ in absolute terms), in the range of PAW as well of DRW. pore size distribution functions of the same former curves, obtained by calculating their slope (first derivative dθ/dψ) and plotting the data on an x-axis where soil water potential values were converted into pore equivalent diameter. Figure 5B represents, even more clearly, the conditions occurring in the soil as a consequence of biochar addition from lower (B0, B2 and B4) to higher quantities (B6, B8, B10). What could be observed in the pore-size distribution of the soil-biochar mixtures was a significant increase in that fraction of porosity straddling the size of the pores that marks the transition from "matrix" porosity (intra-aggregate voids) to "structural" porosity (interaggregate voids). Indeed, among aggregates, pores are formed that hold water primarily by capillary forces associated with the clumping of aggregates; these can be termed "structural" pores. In contrast, "matrix" pores are intra-aggregate, formed among elementary textural particles where interparticle adhesive forces principally hold water. In this respect, it is usually assumed that the former fraction of soil water is more easily available for the plant than the latter.  Figure 5B represents, even more clearly, the conditions occurring in the soil as a consequence of biochar addition from lower (B0, B2 and B4) to higher quantities (B6, B8, B10). What could be observed in the pore-size distribution of the soil-biochar mixtures was a significant increase in that fraction of porosity straddling the size of the pores that marks the transition from "matrix" porosity (intra-aggregate voids) to "structural" porosity (interaggregate voids). Indeed, among aggregates, pores are formed that hold water primarily by capillary forces associated with the clumping of aggregates; these can be termed "structural" pores. In contrast, "matrix" pores are intra-aggregate, formed among elementary textural particles where interparticle adhesive forces principally hold water. In this respect, it is usually assumed that the former fraction of soil water is more easily available for the plant than the latter.
The inflection points of the two curves in Figure 5A corresponded to the maximum value of the two curves in Figure 5B. They represented the slope of the water-retention curves at their respective inflection points (Si). The Si value was significantly higher in the blue line (B6, B8, B10) than in the red one (B0, B2 and B4), although they were both achieved at exactly the same water potential (170 kPa), corresponding to an equivalent pore size equal to 1.72 µm. As expected, the soil water content at the inflection point (θi) was higher in the blue line (B6, B8, B10) than in the red one (B0, B2 and B4), further stressing the improved soil quality offered by biochar application with respect to soil-water relationships.
The symmetry observed in the two pore size-distribution curves suggests that biochar addition at higher rates did not produce a curve shifting and significantly increased the amount of the existing pore classes, both intra-and inter-aggregates (i.e., at both sides of MRX, see Figure 5B). This general increase in porosity can explain both the reduction in bulk density of the mixtures, which was more than proportional to the bulk density of the added biochar, and the increase in the mixture water-retention capacity. Indeed, the first effect can be predominantly accounted for by the increase in the structural (interaggregate) porosity, while the second effect can be predominantly associated with interporosity with respect to RAW and intraporosity with respect to LAW. Adding RAW and LAW gives PAW.
If we consider that the peak of the two curves in Figure 5B roughly corresponds to the edge that separates the micro-from the meso-porosity, it is possible to understand the beneficial effect exerted by the addition of biochar in favouring water retention, precisely at the most convenient potentials for plant-water uptake (i.e., those in the range WP-FC).

Water Infiltration of Soil-Biochar Mixtures
The average infiltration rate of water into the six different soil-biochar mixtures showed decreasing values as the biochar application rates from B0 to B10 (Table 7) increased. This trend was observed with respect to the initial (first 60 s of the process), the final (last 60 s) and the total average value of infiltration rates (the entire process duration). Considering the B8 treatment, a lower value for the final and total rate was detected compared to that of B10. From a statistical perspective, the ANOVA results determined only two distinct treatment groups, namely, the first, represented by only the B0 treatment, and the second, which comprises all other treatments (Table 7). Table 7. Average infiltration rates (I R cm sec −1 ) in the soil-biochar mixtures (treatments) at the beginning (first 60 s), at the end (last 60 s) and for the entire duration of the process. The ANCOVA model, applied to the infiltration data in order to estimate the coefficients of the Philip's model, confirmed the same statistical results shown in Table 7; the two model coefficients, C 1 and C 2 , were significantly affected by the biochar presence or absence but not significantly by the biochar amount (Table 8). Table 8. Results of the stepwise procedure in application of the Philip's cumulative infiltration model to the six soil-biochar mixtures. Biochar treatments (B2, B4, B6, B8 and B10) were grouped because they were not statistically different from each other. B0 remained statistically separated from the other treatments. The standard error of the estimates, together with their confidence intervals at 95% of probability are also reported. Both the "sorptivity" coefficient (C 1 ) and the "conductivity" coefficient (C 2 ), showed higher values in soils without biochar application and lower values when biochar was added, regardless of the biochar amount applied. These values fully confirmed the results of the ANOVA test performed on the initial, final and total average infiltration rates, as reported in Table 6.

Sourse of Variation
The application of the Philip's model considering both the cumulative infiltration and the instantaneous infiltration rate is represented in Figure 6. added, regardless of the biochar amount applied. These values fully confirmed the results of the ANOVA test performed on the initial, final and total average infiltration rates, as reported in Table 6. The application of the Philip's model considering both the cumulative infiltration and the instantaneous infiltration rate is represented in Figure 6. Considering Equation (16), the first-term of the model (controlled by the "sorptivity" coefficient C1) strongly dominated the process at the initial stage of infiltration. At this stage the soils were almost dried and the capillary and matrix forces at the soil pore surfaces largely prevailed over the gravitational forces because the larger pores were still empty. Therefore, vertical water movement proceeded at almost the same rate as horizontal water movement. The "gravity" component, controlled by the second term of the model (the "conductivity" coefficient C2), exerted a negligible effect at the very beginning of the infiltration process, but became progressively more important as infiltration proceeded and the soils became wetter. Finally, the second term of the Philip's equation strongly dominated the process when the soils were almost under saturated condition and the gravitational forces exerted a prevailing effect, while capillarity and matrix forces Considering Equation (16), the first-term of the model (controlled by the "sorptivity" coefficient C 1 ) strongly dominated the process at the initial stage of infiltration. At this stage the soils were almost dried and the capillary and matrix forces at the soil pore surfaces largely prevailed over the gravitational forces because the larger pores were still empty. Therefore, vertical water movement proceeded at almost the same rate as horizontal water movement. The "gravity" component, controlled by the second term of the model (the "conductivity" coefficient C 2 ), exerted a negligible effect at the very beginning of the infiltration process, but became progressively more important as infiltration proceeded and the soils became wetter. Finally, the second term of the Philip's equation strongly dominated the process when the soils were almost under saturated condition and the gravitational forces exerted a prevailing effect, while capillarity and matrix forces were, this time, negligible.
Considering the overall infiltration results (Tables 7 and 8 and Figure 6), it appeared that the predominant effect was the presence or absence of biochar, rather than the quantity added to soil. Biochar slowed infiltration into the soil compared to soil without biochar applied. This effect was significantly greater than the effect exerted by the different biochar concentrations in the soil. These results are not in agreement with those generally observed in fine-and fine-loam soils. According to a review paper [1], biochar application to a fine-textured soils at 5 and 10% by weight increased unsaturated hydraulic conductivity at all matric potentials and generally increased water infiltration. According to the same review [1], the application of biochar to fine-textured soils can improve water flow (both infiltration and hydraulic conductivity) because of an increase in pore volume and an improved soil aggregation. It is generally recognized that the addition of biochar significantly enhanced the formation of macro-aggregates that slightly increased the saturated hydraulic conductivity of the amended soils [64]. However, it should be remembered that in our experiment a sieved and repacked soil was used, and these particular conditions may have profoundly affected the results, which would be very different if the soil had been in situ. Indeed, it was also observed [59] that, as a general trend, biochar addition reduced soil-saturated hydraulic conductivity in laboratory experiments compared to those in field studies but without considering specific soil textures. It should be expected that under our lab experiment there was neither the time nor the proper conditions to promote the development of a good soil structure with macro-aggregates because of the contribution of biochar as a soil amendment.
Concerning the effects displayed by the biochar application to soil, on the one hand, the augmented intraporosity conferred by biochar (with small pore size) significantly reduced soil water conductivity when soil was wet and almost saturated [27]. On the other hand, when the soil was still almost dry, we can suppose that biochar showed its hydrophobic property, thus exerting a water-repellent action that significantly reduced the matrix and capillary effect of water attraction by pores and surfaces. This could be the reason that the "sorptivity" coefficient was lower in biochar rather than in the non-amended soil. Biochar hydrophobicity has been largely discussed in the literature [1,33,35]. It was observed, indeed, that biochar's microstructure is often highly hydrophobic, suggesting that when added to soil it can induce water repellency. This higher water repellency, in turn, can be the possible explanation for reduced infiltration and hydraulic conductivity. It was also observed that, over time, the biochar in the soil in situ undergoes oxidation involving organic compounds on the surface of biochar particles. These oxidations can progressively mitigate biochar's hydrophobic property [1], thus favoring the restoration of higher water conductivity. Results from the literature indicate that "aged" biochar is more hydrophilic then "fresh" biochar and that after additional wetting/drying treatments biochar become less hydrophobic [65][66][67].

Conclusions
The results obtained from a lab experiment with soil in pots confirmed that biochar has the potential to improve the hydrological properties of clay loam soil. We observed, on the one hand, a substantial increase in water retention capacity; on the other, a reduced water infiltration rate. The first effect agrees with the majority of experimental evidence retrieved from the literature, while the second one is somehow not aligned with the prevailing literature. Our interpretation is based on the relevant increase in porosity (intrapore and interpore) that biochar addition brings to soil, permitting higher water adsorption. The altered soil structural condition, the limited time elapsed between biochar application and the experimental measurements, together with the storage of the soil in pots under a sealed covering, prevented the development of macrostructural soil aggregates able to generate larger pores and facilitate internal water fluxes. Furthermore, water infiltration and its conductivity were hampered by the hydrophobic action exerted by "fresh" biochar (i.e., obtained only recently by pyrolysis and applied to the soil).
The positive effect on water-holding capacity showed that biochar has the potential to effectively improve the water available for plants and contribute to reducing the frequency and volume of irrigation in fine-textured soils. These aspects are of great agronomic relevance and can be confirmed in "open field" conditions, too.
The observed effects of biochar with respect to slower water infiltration and limited water conductivity, if confirmed under "open filed" conditions, may have a dual consequence. When water infiltration is hindered, the risk of water runoff and soil erosion strongly increases, as does possible waterlogging; this is a negative factor. Conversely, a reduced water movement due to limited hydraulic conductivity significantly slows deep water drainage and consequent nutrient loss through leaching, the latter being a positive factor. Which of the two should be considered as the prevailing factor and which kind of tradeoff should be established largely depends on the environmental farming conditions and operations. Therefore, further experiments under field conditions are needed to clarify these aspects.
Particularly in agricultural regions characterized by climatic water shortage and frequently droughts, such as the Mediterranean, greater water availability and reduced water loss through drainage or runoff due to biochar application could have an agronomic benefit that is perfectly in line with the sustainable management of water resources for irrigation.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.