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Article

Effect of Biochar Application Rates on the Hydraulic Properties of an Agricultural-Use Boreal Podzol

by
Daniel Altdorff
1,*,
Lakshman Galagedara
1,
Joinal Abedin
2 and
Adrian Unc
1
1
School of Science and the Environment, Memorial University of Newfoundland, Corner Brook, NL A2H 5G4, Canada
2
Labrador Institute, Memorial University of Newfoundland, Happy Valley-Goose Bay, NL A0P 1E0, Canada
*
Author to whom correspondence should be addressed.
Soil Syst. 2019, 3(3), 53; https://doi.org/10.3390/soilsystems3030053
Submission received: 13 June 2019 / Revised: 2 August 2019 / Accepted: 12 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Biochar for Sustainable Agricultural Systems and other Environmental Applications)
Browse Figures
Versions Notes

Abstract

:
Boreal agriculture struggles with soils of lower agronomic value, most of which are sandy with a low water holding capacity (WHC) and prone to nutrient leaching. Biochar amendments are associated with positive effects on soil hydraulic properties and enhanced nutrient retention. However, these effects are strongly related to feedstock type and pyrolysis parameters and depend on biochar application rates and soil types. While biochar could increase the productivity of boreal agriculture by improving water and nutrient use efficiency, little is known about its effects on hydraulic processes in podzol. In this study, we investigated the effects of biochar rates (10, 20, 40, 80 Mg carbon ha−1) and maturity on soil hydrology for an agriculturally used Podzol in Labrador, Canada. The in-situ soil water content (SWC) and weather data over an entire growing season were analysed. Hydrus 1D simulations were used to estimate changes in water fluxes. SWC showed clear differentiation among storage parameters (i.e., initial, peak and final SWC) and kinetic parameters (i.e., rate of SWC change). Storage parameters and soil wetting and drying rates were significantly affected by biochar rates and its maturity. The magnitude of the changes in SWC after either wetting or drying events was statistically not affected by the biochar rate. This confirms that biochar mostly affected the WHC. Nevertheless, reductions in cumulative lower boundary fluxes were directly related to biochar incorporation rates. Overall, biochar had positive effects on hydrological properties. The biochar rate of 40 Mg C ha−1 was the most beneficial to agriculturally relevant hydraulic conditions for the tested Podzol.

1. Introduction

Ongoing and future climate changes are expected to affect agricultural productivity in large parts of the current farming regions [1,2]. On the other hand, global warming and corresponding northward shifts in growing degree-days might favor the expansion of agriculture into boreal areas [3]. However, the primary soil type in the boreal regions is Podzol, a soil type considered as unattractive for agronomic use [4]. The low productivity of agriculture in the boreal regions is thus mainly due to unsuitable soil conditions and agro-climatic limitations. A promising method to increase the quality of soils of lower agronomic value is the use of biochar as soil amendment. Biochar has gained much attention in recent years as a popular and widely proposed soil amendment due to the relative ease of availability, the positive effects on the physiochemical properties of soils and the corresponding possible increase of soil fertility. Biochar benefits relevant to agriculture are decreasing soil bulk density [5,6], increasing soil water content (SWC) and water holding capacity (WHC) [7,8,9,10], decreasing saturated hydraulic conductivity (KS) [11,12,13], mitigating nitrogen leaching [14,15], lowering evaporation [16] and thus increased water availability, and enhancing the bioavailability of key nutrients [17,18]. Biochar is further cited for its carbon sequestration potential and mitigation of greenhouse gas emissions [15,17,19].
Studies demonstrating the positive agronomical effects of biochar make it particularly attractive for nutritionally poor sandy soils [20,21]. However, the beneficial outcomes of biochar application are highly dependent on several other factors. Beside parameters linked to the biochar itself, such as biochar organic feedstock, pyrolysis conditions and aggregate size, the incorporation rates, maturity and soil types play crucial roles in the eventual improvement of soil quality [9,17,21]. The interactions between biochar type and soil type can limit the desired outcomes and even result in divergent effects that are not always predictable. Although the majority of biochar studies reported its ability to immobilize harmful substances, such as heavy metals [22,23], polycyclic aromatic hydrocarbon (PAH) [24] and polychlorinated biphenyls (PCB) [25], it must be emphasized that biochar could act as either source or sink for these pollutants [26], with biochar origin and pyrolysis temperature governing these properties [27]. For example, biochar produced from certain feedstock might readily contain contaminants, such as heavy metals, potentially affecting plant growth as well as the rhizosphere microbial and faunal communities and functions [28]. Biochar’s inherent PAH concentrations could contaminate soil; moreover, biochar’s high sorptive capacity may facilitate the persistence of PAHs [26].
The effect of biochar on soil hydrological processes is highly variable. For example, it is known that biochar alters water infiltration rates, but this can be highly dependent on the soil type. Various authors reported decreased infiltration, particularly in coarse soils [13,16,29]. Other studies have shown that application of biochar had either no significant effects on water infiltration [30] or increased infiltration in loamy sand and fine-loamy soils [5,31]. Tian et al. [32] demonstrated that the incorporation of biochar at lower rates increased the WHC of sandy and silt loam soils, while the impacts of higher biochar rates on soil hydraulic parameters in variable soil textural classes remained inconsistent. A review carried out by Blanco-Canqui [18] concluded that biochar reduced KS in coarse-textured soils, while it increased it in fine-textured soils. The application of biochar also influenced the water retention curve, as demonstrated by several authors [13,16,33]. According to the same review, out of 19 studies on the effects of biochar incorporation, 17 reported increases in water retention while two reported the opposite effect. The latter two studies were carried out on clayey soils, confirming a limited response to biochar in fine-textured soils [18].
This brief literature review demonstrates that the effects of biochar on soil hydraulic properties and functions are complex and demand site-specific investigation. With respect to Podzol, understanding the effects of biochar on soil water storage and kinetic parameters can help select the best possible biochar rates and suitable crop types in support of higher agronomic productivity in boreal agriculture. Moreover, exploring the effects of biochar on infiltration rates and cumulative bottom fluxes can help understand the potential for biochar for mitigating leaching and contamination processes, thus supporting environmentally appropriate decisions. In this article, we investigated the effects of several biochar incorporation rates and maturity on hydraulic properties of a boreal Podzol. We hypothesized that biochar would (i) impact the SWC storage parameters and their kinetics, (ii) influence the temporal wetting and drying characteristics, and (iii) significantly influence the cumulative flux at the lower boundary of the incorporation layer. In this study, we further defined an appropriate biochar rate to improve the hydraulic parameters on the tested boreal podzolic soil and thus its potential effect on the leaching of environmentally harmful substances.

2. Materials and Methods

2.1. Test Site and SWC Measurement

A field study was carried out on an experimental farm in Happy Valley-Goose Bay, Canada (53.3017° N, 60.3261° W) (Figure 1A), managed by the Labrador Institute of the Memorial University of Newfoundland. The test site covered an area of approximately 600 m2 and was converted from boreal forest to agricultural use in 2012. This biochar trial was started in 2013. The surface was fairly level and web-based elevation data indicated an altitude of about 10 m above sea level (a.s.l.) (topographic-map.com). The A-horizon (upper 15 cm) has a loamy sand texture with an initial average organic matter (OM) content of 3.28%. Five different biochar incorporation rates, i.e., 0, 10, 20, 40 and 80 Mg carbon (C) ha−1 were tested. Biochar type was Cement kiln, a hard wood biochar with a particle size <2.5 cm, produced principally from sugar maple (Acer saccharum) and yellow birch (Betula alleghaniensis). Biochar was produced and supplied by Basques Hard Wood Charcoal (http://www.basquescharcoal.com). According to the manufacturer, this tested biochar was produced at a core temperature of 550 °C. However, the temperatures could have varied between 350 and 600 °C within the burn chamber depending on the position of the source material. Biochar’s pH and electric conductivity (EC) were measured following the protocol [34] suggested by the International Biochar Initiative (http://www.biochar-international.org) (Table 1). Samples of 1 g ground (<2.0 mm) biochar were placed in 50 mL plastic centrifuge tubes to which 20 mL of deionized water was added. Then, the tubes were shaken on a horizontal shaker for 90 min [35] before measurements were taken.
A completely randomized design with four replicates was used. Each replicate was an 8 × 4 m2 plot.
Biochar was thoroughly mixed within the upper 15 cm of the soil. The incorporation of biochar was completed in two stages. In 2013, the biochar rates of 10, 20 and 40 Mg C ha−1 were applied. In 2014, each biochar treated plot was divided into two equal plots (4 × 4 m2). One-half of each original plot received a second biochar dose equal to the original application, effectively doubling the amount of applied biochar (Figure 1B). This incorporation resulted in ‘old’ (O) and ‘new’ (N) biochar rates (Figure 1B), subsequently named by their rate [t ha−1] and an incorporation schedule of O10, O20 and O40 was incorporated in 2013, while N10, N20, N40 and N80 were fully incorporated in 2014. After the incorporation of biochar, the average bulk density of the A-horizon, based on the undisturbed core samples (n = 6), was found to be 1.21 ± 0.14 g cm−3. The relatively low bulk density was caused by ploughing and due to the incorporation of coarse biochar particles.
In the center of each replicated plot, a 5TM moisture-temperature probe was installed horizontally at a depth of 5 cm and connected to an Em50 series data logger (METER Group Inc., Pullman, WA, USA). Weather data were collected by an onsite Weather Station (METER Group Inc., Pullman, WA, USA). Both SWC and weather data were collected automatically at 5 min intervals from 20 July to 1 September 2016. Sugar beet (Beta vulgaris, Red Ace cultivar) was planted in all the plots. Due to technical problems, SWC sensors for one replicate of each control, N20 and O40 did not produce consistent results and thus, these treatments only have three statistical replicates.

2.2. SWC Data Processing and Analysis

For the preliminary observations of the temporal SWC variations, we plotted the measured SWC values, averaged by biochar rates and age, for the entire monitoring period (74 days) and obtained basic descriptive statistics: Minimum (MIN), Maximum (MAX), Mean, Median (MED), Standard Deviation (SD), and Coefficient of Variation (CV) (Appendix A, Table A1). For more detailed analyses, the individual responses to rain events of each sensor were discretized into (1) initial SWCi (start of the wetting curve), (2) peak SWCp (end of the wetting curve), and (3) final SWCf (end of the drying curve), assuming that each response describes the plot’s wetting and drying characteristics. By employing data from all sensors, the variability within the replicates was taken into consideration. In addition to SWCi, SWCp, and SWCf, the following SWC transformations were estimated, allowing for an assessment of the effects of biochar on soil hydraulic properties: (4) Differences in absolute wetting (SWCp − SWCi), (5) Differences in absolute drying (SWCp − SWCf), (6) Differences in relative wetting (SWCp − SWCi)/SWCi, (7) Differences in relative drying (SWCp − SWCf)/SWCf, (8) Initial differences in control vs. treatments (SWCi control − SWCi treatment), (9) Peak differences in control vs. treatments (SWCp control − SWCp treatment), (10) Final differences in control vs. treatments (SWCf control − SWCf treatment), (11) Difference in absolute change, wetting ((SWCp − SWCi) treatment − (SWCp − SWCi) control), (12) Difference in absolute change, drying ((SWCp − SWCf) treatment − (SWCp − SWCf ) control), (13) Average wetting and drying rates, calculated as change in SWC (%) with time.
The effect of biochar on parameters 1 to 12 was statistically analysed using a one-way ANOVA (H1: at least one variable is significantly different) if all plots were investigated, and F-tests (H1) if individual differences between biochar rates and the control were queried. To reveal the differences in temporal responses between treatments, the four most prominent rain responses were selected and analysed by their full wetting and drying curves (up to 600 observations in each event), combined and individually vs. the control. Moreover, F-tests (H1) were employed with parameters 1 through 7 to assess the maturation effect of biochar (new vs. old). Data from all replicates were used for the calculation of parameters 1 through 7. Averaged SWC values were employed for comparative parameters 8 through 12 and for the temporal analyses; averaging was necessary for simplification due to the unequal number of replicates and the potential arbitrary order of single sensor comparisons. To remove the effects of the individual of rain event and to obtain a normalized response data, we calculated the changes in terms of SD units from the wetting and drying rates (6 and 7) for each treatment. The standardization in units of SD deviation from the mean, also known as z-scores, allows for concomitant analyses of datasets measured on different scales and units by removing the impact of the variability in the absolute numerical magnitude due to variable units, while at the same time maintaining the treatment-driven variability structure inherent to each parameter [36]. Z-score standardization was carried out separately for each identified rain event (see sub-item 3.1). The within-treatment absolute SWC variability, as described by SD, was collectively analysed by F-tests (H1) and individually using t-tests (H1) to reveal the differences between biochar treatments and the control. For most statistical analyses, Microsoft Excel (Microsoft, Inc., Redmond, WA, USA) and Minitab16 (Minitab, Inc., State College, PA,USA) were used. The impact of treatment on the wetting and drying rates were analysed using the “agricolae” package [37] in Rstudio [38]. The wetting and drying rates were standardized as z-scores, separately for each of the 20 rain events. This, as hinted above, eliminated any putative statistical significance of the absolute changes in SWC values, which are related to the amplitude of the rain event (i.e., event as was excluded as a factor), and allowed for a proportional comparison among treatments for the wetting or drying slopes.

2.3. SWC Simulations

We used Hydrus 1D [39] to assess the effects of different biochar rates on the infiltration rate, the WHC and the cumulative lower boundary flux. Several studies demonstrated the capability of Hydrus 1D to simulate hydrological processes in the vadose-zone of a podzolic soil [40,41,42]. More recently, Hydrus 1D was also successfully used for simulating hydrological processes after biochar application [43,44], and it can be a valuable tool for predicting hydrological responses of different biochar amendment rates [10]. In this study, Hydrus 1D simulations were based on the van Genuchten-Mualem model [39,45] with a 1 cm discretization of a 1-m soil column, free drainage at the lower boundary, and a variable flux at the upper boundary. Potential evapotranspiration (PET) was derived by the Penman–Monteith method [46] using an on-site meteorological data set. Simulations were performed using a two-layer system (0–15 cm and 16–100 cm), as the top layer of loamy sand contained variable biochar incorporation rates. Since soil texture data were available for the upper 15 cm only, we assumed a subjacent sand terrace (underlain by clay on high terraces) for the bottom layer (16–100 cm), according to the available literature [47]. To obtain information about the fluxes, observation nodes were set at 6 cm (immediately below the SWC sensor depth of 5 cm) and at 16 cm (immediately below the top layer boundary). Biochar incorporation was calculated assuming that (i) the amount of carbon can be added directly to the initial OM content, and (ii) the incorporated biochar is homogeneously distributed within the top 15 cm of soil depth. Saturated SWC (SWCS) and KS were derived from the Soil-Plant-Air-Water (SPAW) model [48] (Table 2) while alpha and n parameters were derived by manual calibration using measured SWC for each treatment using the first wetting event that provided nearly 100 h of continuous drying data for calibration (Table 2). The model performances were validated by three independent similar wetting and drying events (100 h each) and displayed by its root mean square error (RMSE) and coefficient of determination (R2). The cumulative water flux values from the two observation nodes were assessed for each treatment and correlated to the biochar rate. We simulated the whole monitoring period of 1776 h (74 days) between 20 June and 1 September 2016.

3. Results and Discussion

3.1. Measured SWC and Transformation

From the entire period, we identified 20 rain events (Figure 2A), resulting in 580 observations, if all available replicates were used (SWC parameters 1–7), and 160 observations for the averaged SWC values, including the control (SWC parameters 8–13). The ANOVA results for all the plots (Table 3) provide a clear pattern: while storage parameters SWCi, SWCp, and SWCf (SWC parameters 1–3) and their divergence from the control (SWC parameters 8–10) were highly significant (p < 0.05), no kinetic parameters were shown to be significantly affected (p > 0.05) by biochar. The control and O10 had the lowest SWC over the full period, while O40 and N80 had the highest measured SWC (Figure 2B, Figure 3). The basic descriptive statistics show correspondingly lower MED, as well as a lower SD for the control and O10, indicating generally low and temporally stable SWC values (Supplementary date, Table 2). Furthermore, the control had the lowest CV. O40 had the highest MED, followed by N80, as may also be inferred from Figure 2B and as summarized in Figure 3. The minimal SWC (MIN) and maximal SWC (MAX) did not exhibit clear trends, indicating rather a complex interaction between biochar rates, rain amplitude and SWC values. Nevertheless, a per-event proportional analysis of the wetting and drying rates showed them to be directly or inversely related to the biochar rate in the 0 to 40 Mg C ha−1 range, respectively. The 80 Mg C ha−1 plots, however, did exhibit wetting and drying kinetics statistically similar to the control treatments (Figure 3), albeit at different SWC ranges (Figure 2B).
These results indicate that varying biochar rates led to consistent and statistically significant differences among SWC storage parameters (initial, peak, end), and consistent (Figure 2B), but statistically not significant (Table 3 parameters 4–7 and 11–12) differences in the actual absolute changes due to wetting and drying. The effects of two different incorporation times (Old vs. New) were statically less uniform: the ANOVA showed no significant effect for any of the considered parameters if all plots were considered (p > 0.05, data not shown). However, a comparison of individual biochar rates (Table 3, columns b–d) uncovered a significant application timing (maturity) effects on most storage parameters, with the exception of N20, while the SWC kinetics remained again unaffected. For O40, even the drying rate (parameter 7) was significantly lower than for N40. It is known that biochar can undergo a maturation process after application as a product of various processes. Maturation involves surface oxidation [49,50], sorption of soil organic matter rich in oxygen-containing groups [51,52] and increase its surface area while decreasing the pore diameter, relative to fresh biochar [53]. Another possible cause for the different performances of the New vs. Old plots might be related to the initial water repellency characteristics of biochar [54,55], which change with aging [56,57]. For the tested soil, biochar contact to organic acids and other chemical soil components could have caused the aging and decreasing of the hydrophobicity, as reported by previous studies at similar soils [56]. However, given the short temporal differences between Old vs. New in our study, it is unclear how much of the differences can be truly explained as being due to maturation. It is further possible that the soil of Old biochar plots might have compacted (over the one-year period), reducing macro- porosity in comparison to the New biochar plots, and enhancing soil water retention, as described by Dokoohaki et al. [58]. Regarding the measured SWC range (MAX–MIN) for N10 vs. O10 and N40 vs. O40, the New plots provided a larger range which could also be related to changes in total porosity, confirming the assumed maturation/compaction processes. Since granular biochar also contains pore spaces capable of retaining water, at initial stages, water retention in biochar-amended soils can be higher. However, this can be reduced with time due to the temporal decomposition of biochar in soils [59,60]. An explanation for the similar responses from N20 and O20 cannot be assumed from the available data. Long-term effects need to be evaluated for clear understanding of the effect of biochar maturity at field scale on the soil hydrology of podzolic soils.
The ANOVA for boundary characteristics for the wetting and drying events (SWCi, SWCp, and SWCf) consistently exposed significant differences (p < 0.001) between the treatments for all parameters and rain events (data not shown). The statistically significant differences in SWCi, SWCp, and SWCf indicate the influence of biochar on temporal characteristics. However, analyses of individual rain responses (Appendix A, Table A2) show complex interactions in relation to biochar rates. Analyses based on absolute SWC data show that higher biochar rates affected wetting kinetics in a manner statistically different from the control (Appendix A, Table A2); this was less clear for the lower biochar rates, i.e., N10, O10 and N20. Nevertheless, the overall trend in wetting and drying, when analysed independently of the rain amount (Table 4 and Appendix A, Figure A1), showed that wetting rates had direct relationships, while drying rates showed inverse relationships to biochar rates for the 0 to 40 Mg C ha−1 treatments. The initial SWC at the start of each rain did not have a significant role in the wetting rate differences among treatments (see statistics in Appendix A, Figure A1). On the other hand, the SWCp, at the start of drying had a significant (p < 0.001), albeit minor (r = −0.55; R2 = 0.3), relationship with the proportional drying rates. The SWC at the end of the drying event was still significantly (p = 0.03), but even more marginally (r = −0.17; R2 = 0.03) related to the proportional drying rates (see statistics in Appendix A, Figure A1).
The non-uniform relation between biochar rate and application scheduling, and the soil water parameters indicates the complexity of interactions and their effects. This was most obvious for the maximum biochar rate N80, which showed an increase in storage capacity while it has wetting and drying kinetics more akin to the non-treated plot. The differences in drying parameters between biochar and control plots were generally large and statistically significant, suggesting the presence of biochar as a main factor (Figure 3; Appendix A, Table A2 and Figure A1). Control plots were the slowest to wet and the fastest to dry. Both wetting and drying characteristics were affected by biochar, however, in an inconsistent relationship to the cumulative incorporation rates. The differences in water storage capacity and kinetics after the addition of biochar were clearly described by the inverse relationship between wetting and drying (Figure 3, Appendix A Figure A1a). Wetting was always positively associated with the biochar application rate (Figure 3), albeit not significantly for N80 and O10 (Table 4), whereas drying was proportionally but inversely related to the biochar rate except for the N80 (Figure 3, Table 4). The differences in the wetting and drying rates indicate a hysteresis effect present for all treatments (slope different from 1:1; Appendix A Figure A1a). In respect to agricultural purposes, the application of 40 Mg C ha−1 provided the best results to increase and retain soil water.

3.2. Hydrus 1D Simulation

The Hydrus 1D models resulted in good to very good accuracies for SWC predictions, with an average RMSE (cm3 cm−3) of 0.015 (ranging from 0.008 to 0.025), and an average correlation (R2) of 0.836 (ranging from 0.961 to 0.691) (Table 5). Based on the archived accuracies, we assumed that the Hydrus 1D results provided valuable supporting information. Figure 4 displays the distribution of SWC over a period of 7.5 days (i.e., Event I) as a means to visualise one-dimensional water flow and storage within the soil profile for the different biochar treatments (the simulations for other rain events were very similar).
While the simulation revealed similar initial responses to rainfall, the drying characteristics over the soil column diverged more noticeably, analogous to the measured SWC at a 0–5 cm depth. Biochar increased the WHC, particularly for higher biochar rates (note the difference in drying slopes), an observation supported by previous studies [7,8] and confirmed by the measured SWC data. Where higher biochar rates were incorporated, large portions of the upper layer remained wet (SWC > 17%, Figure 4) over several days between the rain events, while O10, N10 and the control dried relatively quicker. Furthermore, the rain water accumulated at the upper layer boundary, several centimeters above the subjacent sand layer; for the higher biochar rates, the simulated SWC reached values > 30% for several hours after the rain event. A higher SWC availability at a depth of approximately 10–15 cm can facilitate the water uptake by roots and thus, plant growth. This information can be useful for the selection of appropriate crops based on their physiological needs. Table 5 displays the cumulative water flux values (cm h−1) calculated from the observation nodes located at 6 and 16 cm under the soil surface. As expected, the flux values show significant attenuations with increasing biochar rate, more distinct at the layer boundary. In comparison to the unamended plots (control), the biochar incorporation reduced leaching up to 22.5 cm h−1 at 6 cm (N40) and 22.3 cm h−1 at 16 cm (N80). With respect to WHC, the incorporation rate of 40 Mg C ha−1 provided the best results within the top layer. Given our data, it is unclear why the WHC of the N40 rate was larger than that for N80. A variability in electrostatically driven parameters, e.g., repellence or interactions from the high amount of new added biochar with charged molecules in soil, might partially explain this result, but the lack of an O80 treatment did not allow for a direct comparison. The lower performance of N80 in respect to WHC might also suggest non-linear changes in soil porosity parameters. However, the effect was limited to WHC and wetting and drying rates only; both leaching potential and median SWC were still highest for the 80 Mg C ha−1 treatment (Table A1). Thus, the simulations suggest that biochar could mitigate deep percolation and solute transport in boreal Podzols in a direct relationship to biochar rates. With respect to mitigating the leaching processes of potentially harmful substances from agricultural fields, such as nitrate and pesticides, our findings support previous studies based on other soil types [14,15] and could have relevance for other areas of boreal agriculture. However, we emphasize that long-term experiments and simulation exercises both at laboratory and field scales are needed to confirm and expand upon these results, especially as biochar functional parameters change with age [61,62].

4. Conclusions

We conducted a field study in Happy Valley-Goose Bay, Labrador, Canada to investigate the effects of different biochar incorporation rates on the hydraulic properties of a boreal Podzol converted from forest to agricultural use. The analysis of measured SWC showed that biochar had significant impacts on the water storage parameters, while the effects on the absolute changes in SWC remained insignificant, contrary to our initial hypothesis. Biochar was shown to accelerate wetting and slow down drying after rain events. These observations were in direct relationship with the rate of applied biochar, independent if the biochar was added to soil in one application or split over two years. Moreover, we found that biochar addition significantly reduced the cumulative flux at the bottom of the incorporation layer, indicating restraining potential for applied agronomical chemicals. The 40 Mg C ha−1 treatment produced the best results for the soil hydraulic properties (e.g., WHC) relevant for practical applications. Over-application, i.e., 80 Mg C ha−1, resulted in wetting and drying kinetics closer to the non-amended treatment, although the cumulative fluxes were still favorably reduced and the absolute storage capacity increased. The split application of biochar over two years allowed a comparison of the putative age of biochar. The results suggested further biochar-rate-related aging effects, this may be non-linearly associated with changes in total and macro porosity of the bulk soil. However, longer-term monitoring is required to allow a full understanding of the maturation processes of biochar and their effects on the hydrological parameters of a converted Podzol. Experiments and simulations beyond the plough layer are required to fully assess the impact of biochar on physical, chemical and biological properties of amended soils and resultant environmental quality parameters.

Author Contributions

Conceptualization, D.A. and A.U.; methodology, A.U., D.A., J.A. and L.G.; software, D.A. and A.U.; validation D.A., A.U. and L.G.; formal analysis, D.A. and A.U.; field investigation, J.A.; resources, J.A.; data curation D.A. and A.U.; writing—original draft preparation, D.A.; writing—review and editing, A.U. and L.G.; visualization, D.A. (and A.U. for Appendix A); supervision, L.G. and A.U.

Funding

This research was funded by Agriculture Research Initiative (File no. ARI-1314-004) and Growing Forward 2 (File no. GF21415-308) of the government of Newfoundland and Labrador; Harris Centre (Applied Research Fund, 2014–2015 cycle); Atlantic Canada Opportunities Agency (ACOA); Department of Tourism, Culture, Industry, and Innovation (TCII); and Memorial University of Newfoundland. The PDF was funded by Research and Development Corporation, Government of Newfoundland and Labrador, grant number 5404-1962-101 (Ignite R&D).

Acknowledgments

The authors would like to thank Desmond Sellars for enabling the field investigation on the test site.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A

Table
Table A1. Description of soil water content (SWC) [cm3cm−3] as measured at a 5 cm depth: Minimum (MIN), Maximum (MAX), Median (MED), Standard Deviation (SD), Coefficient of Variation (CV) over the whole monitoring period (averaged by replicates).
Table A1. Description of soil water content (SWC) [cm3cm−3] as measured at a 5 cm depth: Minimum (MIN), Maximum (MAX), Median (MED), Standard Deviation (SD), Coefficient of Variation (CV) over the whole monitoring period (averaged by replicates).
TreatmentCONTN10N20N40N80O10O20O40
MIN0.1230.1040.1060.1250.1150.0860.1170.135
MAX0.3060.3530.3060.3490.3370.2800.3050.329
MED0.1510.1630.1560.1610.1670.1330.1610.180
SD0.0310.0410.0420.0390.0390.0320.0360.038
CV0.1910.2430.2480.2230.2190.2240.2090.200
Table
Table A2. Statistical comparison of wetting and drying characteristics for selected events (See Figure 2B). F-test, hypothesizing differences between treatments vs. control (p < 0.05 marked in grey).
Table A2. Statistical comparison of wetting and drying characteristics for selected events (See Figure 2B). F-test, hypothesizing differences between treatments vs. control (p < 0.05 marked in grey).
F-Test (Treatment vs. Control)
nN10N20N40N80O10O20O40
WettingEvent I1680.3690.1220.0080.0040.0040.0400.032
Event II70.0300.1110.1870.0490.1210.2560.307
Event III640.2910.0550.0710.0240.4710.0320.002
Event IV1490.3160.2130.0050.1280.1950.3970.218
DryingEvent I1730.4390.0680.0000.0220.0020.0310.114
Event II4710.0000.0130.0680.0000.0000.3650.022
Event III1960.2890.0090.0600.0000.0050.0000.000
Event IV6000.0000.0000.0000.0000.0000.0000.000
Soilsystems 03 00053 g0a1 550
Figure A1. Relationship between wetting and drying rates of soil water content (SWC): (a) wetting vs. drying, (b) wetting vs. initial SWC, (c) drying vs. initial SWC. The treatments were indicated by different symbols: the stars = control, hollow markers describe “New” treatments, the filled markers describe “Old” treatments; light blue, 10 Mg ha−1 treatments, dark blue, 20 Mg C ha−1 treatments, green, 40 Mg C ha−1 treatments, red, 80 Mg C ha−1 treatment. Analyses were carried out on data that were standardized in terms z-scores separately for each rain event; this normalization removed the effect of the absolute SWC for each event (i.e., no statistical difference due to event).
Figure A1. Relationship between wetting and drying rates of soil water content (SWC): (a) wetting vs. drying, (b) wetting vs. initial SWC, (c) drying vs. initial SWC. The treatments were indicated by different symbols: the stars = control, hollow markers describe “New” treatments, the filled markers describe “Old” treatments; light blue, 10 Mg ha−1 treatments, dark blue, 20 Mg C ha−1 treatments, green, 40 Mg C ha−1 treatments, red, 80 Mg C ha−1 treatment. Analyses were carried out on data that were standardized in terms z-scores separately for each rain event; this normalization removed the effect of the absolute SWC for each event (i.e., no statistical difference due to event).
Soilsystems 03 00053 g0a1
Table
Table A3. Correlations between parameters. (input data in z-score units); r/p (uncorrelated).
Table A3. Correlations between parameters. (input data in z-score units); r/p (uncorrelated).
Initial SWC [%]Peak SWC [%]End SWC [%]Wetting Slope [% min−1]Drying Slope [% min−1]
Initial SWC [%]<0.001<0.0010.338960.034
SWC peak [%]0.81 <0.001<0.001<0.001
End SWC [%]0.900.89 0.0020.031
Wetting slope [% min−1]0.0760.510.24 <0.001
Drying slope [% min−1]−0.17−0.55−0.17−0.72

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Soilsystems 03 00053 g001 550
Figure 1. (A) Location of the test site, (B) Field set up using 4 × 4 m2 plots of four different biochar rates and control plot (CON).
Figure 1. (A) Location of the test site, (B) Field set up using 4 × 4 m2 plots of four different biochar rates and control plot (CON).
Soilsystems 03 00053 g001
Soilsystems 03 00053 g002 550
Figure 2. Temporal variability of rainfall, air temperature and soil water content, (A) Rainfall rate [mm h−1] and temperature over the whole monitoring period (74 days), (B) measured (averaged) soil water content (SWC) from all biochar rates with selected prominent rain responses. O10, O20 and O40 were incorporated in 2013, while N10, N20, N40 and N80 were fully incorporated in 2014; see details under 2.1. Test field and SWC field measurement.
Figure 2. Temporal variability of rainfall, air temperature and soil water content, (A) Rainfall rate [mm h−1] and temperature over the whole monitoring period (74 days), (B) measured (averaged) soil water content (SWC) from all biochar rates with selected prominent rain responses. O10, O20 and O40 were incorporated in 2013, while N10, N20, N40 and N80 were fully incorporated in 2014; see details under 2.1. Test field and SWC field measurement.
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Soilsystems 03 00053 g003 550
Figure 3. Impact of biochar application on wetting (A) and drying (B) rates described as changes in z-scores of soil water content (SWC) [vol%], calculated separately for each rain event. Analysis was carried out on data from 20 rain events (n = 160) (Figure 2A).
Figure 3. Impact of biochar application on wetting (A) and drying (B) rates described as changes in z-scores of soil water content (SWC) [vol%], calculated separately for each rain event. Analysis was carried out on data from 20 rain events (n = 160) (Figure 2A).
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Soilsystems 03 00053 g004 550
Figure 4. Rainfall rate of Event I (top) and hydrological response of soils with different biochar rates. Each horizontal colour bar consists of 180 single columns (7.5 days), representing the current soil water content (0–30 cm) for a one-hour time step of the simulation.
Figure 4. Rainfall rate of Event I (top) and hydrological response of soils with different biochar rates. Each horizontal colour bar consists of 180 single columns (7.5 days), representing the current soil water content (0–30 cm) for a one-hour time step of the simulation.
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Table
Table 1. Biochar pH, electric conductivity (EC), and total elemental concentrations.
Table 1. Biochar pH, electric conductivity (EC), and total elemental concentrations.
pH8.1
EC [dS m−1]0.69
P [%]0.07
K [%]0.91
Ca [%]2.71
Mg [%]0.21
S [%]0.06
Fe [%]0.19
Cu [mg kg−1]10
Mn [mg kg−1]1290
Zn [mg kg−1]125
Table
Table 2. Hydrus 1D model parameters: saturated soil water content (SWCS) and residual soil water content (SWCR) [cm3 cm−3], van Genuchten Parameters alpha [cm−1] n [–], and saturated hydraulic conductivity KS [cm s−].
Table 2. Hydrus 1D model parameters: saturated soil water content (SWCS) and residual soil water content (SWCR) [cm3 cm−3], van Genuchten Parameters alpha [cm−1] n [–], and saturated hydraulic conductivity KS [cm s−].
TreatmentSWCRSWCSAlphanKS
CON0.100.5270.0853.1513.4
N100.090.5360.0803.1013.6
O100.060.5360.0852.9013.6
N200.060.5510.0752.9014.0
O200.090.5510.1102.6014.0
N400.090.5970.1202.6015.3
O400.100.5970.0902.5515.3
N800.050.6400.0852.9016.5
Layer 20.0450.4300.1452.6829.7
Table
Table 3. Significance of treatment on the changes in soil water content (SWC) parameters in response to rain events; p-values (p < 0.05, marked grey): column a—ANOVA for all plots); column b to d—F-test for application schedule (New vs. Old), see details under Section 2.2.
Table 3. Significance of treatment on the changes in soil water content (SWC) parameters in response to rain events; p-values (p < 0.05, marked grey): column a—ANOVA for all plots); column b to d—F-test for application schedule (New vs. Old), see details under Section 2.2.
SWC Parameterp-Values
(a) all plots(b) N10 vs. O10(c) N20 vs. O20(d) N40 vs. O40
1SWCi0.0010.0030.7790.004
2SWCp0.010.0140.9920.189
3SWCf0.000.0010.9980.005
4SWCp − SWCi0.8310.5810.850.493
5SWCp − SWCf0.7550.7530.9890.328
6(SWCp − SWCi)/SWCi0.4040.5390.7360.059
7(SWCp − SWCf)/SWCf0.2790.5880.8750.043
8SWCicontrol − SWCi treatment<0.001n.a.n.a.n.a.
9SWCp control − SWCp treatment<0.001n.a.n.a.n.a.
10SWCf control − SWCf treatment<0.001n.a.n.a.n.a.
11(SWCp − SWCi) treatment − (SWCp − SWCi) control0.988n.a.n.a.n.a.
12(SWCp − SWCf) treatment − (SWCp − SWCf) control0.604n.a.n.a.n.a.
Table
Table 4. Linear regression analyses of wetting and drying rates. Data were standardized as z-scores, thereby eliminating the effect of the variable absolute soil water content (SWC) values among 20 rain events (n = 160).
Table 4. Linear regression analyses of wetting and drying rates. Data were standardized as z-scores, thereby eliminating the effect of the variable absolute soil water content (SWC) values among 20 rain events (n = 160).
WettingDrying
CoefficientPr (>[t])CoefficientPr (>[t])
(Intercept)−0.5950.0020.4150.034
N100.4680.088−0.1670.545
N200.8340.003−0.6450.020
N401.247<0.001−1.020<0.001
N800.0400.8830.2410.382
O100.4000.144−0.3010.275
O200.8410.002−0.7390.008
O400.935<0.001−0.6870.013
Model significance (p)<0.001<0.001
Table
Table 5. Hydrus 1D model accuracies are shown by root means square error (RMSE) and coefficient of determination (R2) between the predicted and measured soil moisture content for the calibration event I and three validations events (II to IV). Analyses were carried out on actual measurements for selected events.
Table 5. Hydrus 1D model accuracies are shown by root means square error (RMSE) and coefficient of determination (R2) between the predicted and measured soil moisture content for the calibration event I and three validations events (II to IV). Analyses were carried out on actual measurements for selected events.
EventVariableC N10O10N20O20N40O40N80
IR20.9260.9260.9540.9610.9260.9580.9240.937
RMSE0.0090.010.0090.0090.0110.0080.0130.009
IIR20.9160.9280.9520.7260.6910.7570.7530.882
RMSE0.0080.0240.0090.0240.0210.0140.0150.012
IIIR20.8350.8050.9350.8860.8430.8060.8320.833
RMSE0.010.0130.0080.0140.0150.0120.0170.012
VIR20.8470.8850.8780.7760.7840.8820.7640.919
RMSE0.020.0160.0170.0240.0230.0190.0250.016

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Altdorff, D.; Galagedara, L.; Abedin, J.; Unc, A. Effect of Biochar Application Rates on the Hydraulic Properties of an Agricultural-Use Boreal Podzol. Soil Syst. 2019, 3, 53. https://doi.org/10.3390/soilsystems3030053

AMA Style

Altdorff D, Galagedara L, Abedin J, Unc A. Effect of Biochar Application Rates on the Hydraulic Properties of an Agricultural-Use Boreal Podzol. Soil Systems. 2019; 3(3):53. https://doi.org/10.3390/soilsystems3030053

Chicago/Turabian Style

Altdorff, Daniel, Lakshman Galagedara, Joinal Abedin, and Adrian Unc. 2019. "Effect of Biochar Application Rates on the Hydraulic Properties of an Agricultural-Use Boreal Podzol" Soil Systems 3, no. 3: 53. https://doi.org/10.3390/soilsystems3030053

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