Allaire, S. E., Baril, B., Vanasse, A., Lange, S. F., MacKay, J. and Smith, D. L. 2015. Carbon dynamics in a biochar-amended loamy soil under switchgrass. Can. J. Soil Sci. 95: 1–13. The environmental impacts of switchgrass production for bioenergy could be reduced through the use of biofertilizers rather than mineral fertilizers and through soil amendment with biochar. The objectives of this study were: (1) to assess the impact of biochar and biofertilizer on switchgrass (Panicum virgatum L.) yield and parameters related to carbon dynamics, (2) to correlate carbon parameters with soil physico-chemical properties over the first two growing seasons, and (3) to develop a C budget. A complete randomized block design was installed in a sandy loam with split plot treatment design, the main plots receiving 0 or 10 t ha^-1 of biochar and the sub - plots receiving no fertilization, mineral N fertilization, or biofertilizers. Biofertilizers had no significant impact on plant and soil. Biochar increased yield relative to the control treatment by about 10% during the first year and root biomass by up to 50% after 2 yr (P>0.1). Mineral N fertilization also increased yield resulting in higher plant C sequestration after 2 yr. Biochar increased CO₂ soil concentration (CO_2-soil) by up to 50% but its impact on CO₂ emission flux (CO_2-flux) changed over time. The impact of mineral fertilization on CO_2-flux also varied with time. Soil CO₂ dynamics was mostly influenced by temperature, N and water content. Biochar and fertilization treatments showed interactions on some plant and soil parameters. The highest C sequestration budget was obtained with a combination of biochar and mineral N fertilization. The equivalent of about one-third of the increase in soil C content associated with biochar treatments was respired away by soil microorganisms. Nearly one-fourth of C sequestered by plants remained in or at the soil surface (root and crop residues).

Huang, M., Rodger, H. and Barbour, S. L. 2015. An evaluation of air permeability measurements to characterize the saturated hydraulic conductivity of soil reclamation covers. Can. J. Soil Sci. 95: 15–26. The saturated hydraulic conductivity (K_s) of soil covers used in land reclamation is known to change over time as the result of weathering processes. Guelph permeameter (GP) measurements have been used to track the evolution of K_s for soil covers at an oil sands mine near Ft. McMurray, Alberta. Although successful, the method was time consuming and consequently a rapid method of estimating K_s based on in situ air permeability measurements was developed. The objectives of this study were: (1) to use air permeability measurements to characterize the spatial variations of K_s for typical reclamation soils and (2) to compare air permeability measurements to direct measurements obtained through laboratory and GP measurements. The results highlight that the values of K_s estimated from measured air permeability values were higher than the values of K_s measured directly using the GP. This is likely due to swelling of clay soils or air-entrapment during GP measurements. Although the magnitude was over-estimated, the variability of K_s was captured by the air permeability measurements. Consequently, a limited program of comparative GP and air permeameter measurements could be used to more rapidly characterize the K_s of reclamation covers over time.

Miller, J. J. and Chanasyk, A. S. 2015. Unsaturated water flux at mid and lower slope positions within an inclined landscape of the Dark Brown soil zone in southern Alberta. Can. J. Soil Sci. 95: 27–36. Little research has quantified vertical-unsaturated water flux below the root zone for mid and lower slope positions within inclined, low-relief, and longer-slope landscapes of the Dark Brown soil zone of the Canadian prairies. We measured soil moisture (0.23–1.22 m) in the field at mid and lower slope positions in southern Alberta from May to October in 1985 and 1986. Undisturbed soil cores were taken from soil horizons and saturated hydraulic conductivity and soil moisture retention were determined in the laboratory. Vertical-unsaturated water flux below the root zone was calculated between 1.07 and 1.22 m depths below ground surface using the hydraulic gradient method. Water fluxes for the 2 yr ranged from <10^-11 to 10^-10 m s^-1 at the mid slope position, and from <10^-11 m s^-1 to 10^-9 m s^-1 at the lower slope position, and were consistent with some other studies. Cumulative water flux was dominantly downward (-2.2 to -3.4 mm) at the mid slope position and this flow direction was consistent with this Orthic Dark Brown Chernozemic soil that was located in a “recharge area”. Cumulative water flux was dominantly upward at the lower slope position in 1985 (1.4 mm) and dominantly downward but of very low magnitude in 1986 (-0.1 mm), and this flow direction was consistent with this saline Gleyed Regosol and “saline seep”. Cumulative water fluxes as a percentage of annual precipitation were 0.8 to 1.8% at the mid slope position and 0.3 to 0.5% at the lower slope position.

Rabileh, M. A., Shamshuddin, J., Panhwar, Q. A., Rosenani, A. B. and Anuar, A. R. 2015. Effects of biochar and/or dolomitic limestone application on the properties of Ultisol cropped to maize under glasshouse conditions. Can. J. Soil Sci. 95: 37–47. Ultisols in the tropics are characterized by low pH and high exchangeable Al. Maize grown on them produces low yield. A study was conducted to determine changes in soil properties and their subsequent effects on maize growth, resulting from oil palm empty fruit bunch (EFB) biochar and/or dolomitic limestone application. The results show that the application of the EFB biochar improved soil fertility by increasing soil pH. The Al³ activities in the soil solution decreased exponentially with increasing rate of the biochar application. The decrease in Al in the biochar-treated soil occurred because: (1) at the rate of>5 t ha^-1, soil solution pH increased significantly, precipitating Al as gibbsite; and (2) the biochar was able to fix some of the Al by chelation. Application of the biochar alone or in combination with lime significantly improved maize growth. The critical Al³ activity for maize grown on Ultisol was 10 µM, while critical pH was 4.7–4.8. Maize grown on the EFB biochar-amended soils produced greater root length compared with that of the control. The optimal rate of EFB biochar application to improve the productivity of the Ultisol for maize production under glasshouse condition was 5–10 t ha^-1.

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha^-1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.

Dessureault-Rompré, J., Zebarth, B. J., Burton, D. L. and Georgallas, A. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63–75. Prediction functions based on simple kinetic models can be used to estimate soil N mineralization as an aid to improved fertilizer N management, but require long-term incubations to obtain the necessary parameters. Therefore, the objective of this study was to examine the feasibility of predicting the mineralizable N parameters necessary to implement prediction functions and in addition to verify their efficiency in modeling soil N supply (SNS) over a growing season. To implement a prediction function based on a first-order (F) kinetic model, a regression equation was developed using a data base of 92 soils, which accounted for 65% of the variance in potentially mineralizable N (N₀) using soil total N (STN) and Pool I, a labile mineralizable N pool. However, the F prediction function did not provide satisfactory prediction (R²=0.17-0.18) of SNS when compared with a field-based measure of SNS (PASNS) if values of N₀ were predicted from the regression equation. We also examined a two-pool zero- plus first-order (ZF) prediction function. A regression model was developed including soil organic C and Pool I and explained 66% of the variance in k_S, the rate constant of the zero-order pool. In addition, a regression equation was developed which explained 86% of the variance in the size of the first-order pool, N_L, from Pool I. The ZF prediction function provided satisfactory prediction of SNS (R²=0.41-0.49) using both measured and predicted values of k_S and N_L. This study demonstrated a simple prediction function can be used to estimate SNS over a growing season where the mineralizable N parameters are predicted from simple soil properties using regression equations.