METHODS

Sampling Sites

Samples were gathered from Breton plots which serve as a significant research site for studying soil organic carbon (SOC) dynamics due to its long-term experimental design and diverse agricultural practices. These plots have been instrumental in understanding the effects of various management strategies on SOC, making them a valuable resource for researchers in soil science and agronomy. University of Alberta’s Breton Plots are located outside the town of Breton (53°09’N, 114°44’W) in west central Alberta, Canada. The first experimental plots at this location were established in 1930 (Dyck et al., 2012), after it was converted into agricultural production in 1919-1920. The Breton Plots belonging to the grey-wooded soil zone of Alberta (Achtymichuk, 2024), encompass soils that are classified as either Luvisols, Orthic Dark Gray Luvisols, or Typic Cryoboralfs (Izaurralde et al., 2001).

Samples were obtained from both Classical and Hendrigan Plots at Breton Plots. Various crop rotations, such as wheat-fallow and wheat-oat-barley-hay systems have been implemented in the Classical Plots specifically to evaluate the impacts on SOC levels (Izaurralde et al., 2001). The Classical Plots have also been used to understand the benefits of conservation practices, including reduced tillage and crop residue retention, associated with enhanced SOC levels compared to conventional practices (Izaurralde et al., 2001; Giweta et al., 2014). In contrast, the Hendrigen Plots were established later and focus specifically on the interactions between soil management practices and nutrient dynamics (Achtymichuk, 2024) with a more targeted approach towards examining the influence of specific nutrient management strategies on SOC levels and soil microbial activity. The research primarily done on Hendrigan Plots have provided insights into the critical role of balanced fertilization in increased SOC accumulation demonstrating the importance of nutrient management in promoting soil health (Grant et al., 2020; Giweta et al., 2017). To summarize, Classical Plots utilize a factorial experimental design incorporating multiple crop rotations and management practices to assess their cumulative effects on SOC while Hendrigan Plots employs a more focused approach to examine the effects of specific nutrient applications on SOC and microbial dynamics. As explained by Grant et al., (2020), these two different experimental designs provided an overview of insights into the long-term sustainability of various agricultural practices and their impacts on SOC through Classical Plots and targeted data on nutrient management strategies and their effectiveness in enhancing SOC levels, contributing to more refined agricultural practices through Hendrigan Plots. Since 1930, the original experimentation through these two plots at the Breton Plots has remained continuous to the present.

Identifying the sampling site according to Agricultural Management and Treatment practices:

Classical Plots (est. 1930),

Wheat-fallow (WF) rotation (non-amended/check/control, fertilized/NPKS, and manured sub-treatments)
5-year (5Y) rotation (non-amended/check/control, fertilized/NPKS, and manured sub-treatments)
- three years of annual cash crops
- two consecutive years of perennial grass-legume forage hay

Hendrigan Plots (est. 1980):

a continuous forage (CF) system
a continuous grain (CG) system
complex 8-year (8Y) agro-ecological rotation
- annual cash crops,
- leguminous green manure,
- three consecutive years of perennial grass-legume forage hay

Figure 2: Breton Plots (Achtymichuk, 2024)

Sample Collection

As explained by Achtymichuk, (2024) in his Master’s Thesis, soil samples were collected from two depths, 0–7.5 cm and 7.5–15 cm, during the sampling period of October 12th to 15th, 2021. A hydraulic Giddings soil coring unit mounted on a Dodge 3500 flat deck truck and affixed with a sampling tube 6 cm in diameter and 1.2 m long was used for sample extraction. The coring unit was inserted to a depth of at least 90 cm at three locations randomly identified within each plot. The extracted cores were carefully removed from the sampling tube, separating into distinct depth intervals: 0–7.5 cm, 7.5–15 cm, 15–30 cm, 30–60 cm, and 60–90 cm. Soils from two cores from the same plot were pooled within each depth interval and bagged for further evaluation. To ensure representative sampling and to account for the variation in surface soils, two additional cores were collected at a depth of 30 cm at random locations within each plot.

Sample Preparation

After collection, samples were air-dried before further chemical analysis. Care should be taken when drying the samples (air-dried or oven-dried at a low temperature, typically below 40°C) Once collected, the soil samples should be to prevent the loss of volatile organic compounds. Drying is as overheating could contribute towards the loss of volatile organic compounds and alter the chemical composition of the soil organic matter (SOM) (Nakano et al., 2021). Samples collected from the two uppermost layers were used for analysis in this study as SOM is most concentrated typically between 0-15 cm of the soil (Leitner et al. 2012). Once air dried, the soil samples were sieved with a mesh size of 2 mm to remove larger particles, such as stones and roots, which can interfere with the analysis. Samples were then ground to a fine powder using a mortar and pestle or a mechanical grinder, to obtain a more homogeneous sample and to increase the surface area of the soil particles which could promote more efficient pyrolysis during the PyGCMS analysis (Suherman et al., 2021). Homogenization facilitates consistent results across samples (Thomas et al., 2020).

Laboratory Analysis

Samples were analyzed using Pyrolysis-Gas Chromatography-Mass Spectrometry (PyGCMS), as it is considered as an effective analytical technique for analyzing SOC as it could provide detailed chemical signatures of organic compounds present in soil samples through qualitative analysis. A main advantage of PyGCMS is its high sensitivity in detecting a wide range of organic compounds, including those present in low concentrations, also allowing for the identification of both volatile and semi-volatile organic compounds (Mohamed et al., 2016). Soil samples go through pyrolysis thermally decomposing into smaller, volatile compounds, which get separated and analyzed by gas chromatography and mass spectrometry. Various classes of organic compounds, including aliphatic hydrocarbons, phenols, and fatty acids can be identified through this process which facilitates the understanding of chemical and biochemical processes occurring in soils (Palayukan et al., 2022).

Figure 3: PyGCMS (Frontier Labs, n.d.)

The particular technique used was single-shot PyGCMS analysis, a method that is advantageous for analyzing complex matrices such as soil, where a variety of organic compounds may be present. (Kim et al., 2018; Kawashima et al., 2022). Comparatively, single-shot analysis minimizes the need for extensive sample preparation. The prepared soil sample is placed in a pyrolysis chamber and rapidly heated to high temperatures (typically between 300°C and 600°C) in an inert atmosphere. During pyrolysis, the organic matter in the soil decomposes into smaller volatile compounds, which are then released into the gas phase, a process critical for breaking down complex organic materials into simpler compounds that can be analyzed by GCMS (Kawashima et al., 2019).

Statistical Analysis

Samples were statistically analyzed using Principal Component Analysis (PCA) ordination, a statistical tool that can be used to analyze complex data sets such as SOC data. This analysis helps simplify the data and provide insights into the underlying structure and identify key factors that contribute to variability. By focusing on the principal components which could explain the highest variability, PCA identifies patterns and relationships within the datasets enabling it to capture the most significant sources of variability while minimizing information loss. In addition bar graphs and scatter plot diagrams were used to explore data.

All statistical tests and analyses were completed using R and RStudio version 2024.12.1 Build 563

Data visualizations were completed using many packages available via R studio including ggplot2, vegan and readxl packages.

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