Spatial variability of mineral surface area and carbon sequestration potential at the farm scale – a case study

Introduction

Soil carbon (C) provides essential ecosystem services important for providing food and fibre and regulation of climate (Trivedi et al. 2018). However, agricultural land use has significantly reduced the amount of C stored in the soil (Sanderman et al. 2017) and contributes to the increase in atmospheric carbon dioxide (CO₂) concentrations. Reversing this flux of C, through the adoption of negative emission soil C sequestration practices, could contribute to the goal of reducing atmospheric CO₂ concentrations.

The past decade has seen advances in the understanding of soil C, through the development of conceptual frameworks and new soil biogeochemical models that incorporate measurable pools of soil organic C (SOC; Cotrufo and Lavallee 2022). While soil C is complex in its nature and function, two physically distinct fractions are commonly identified: particulate organic C (POC) and mineral associated organic C (MAOC). These two fractions differ in their relative size (i.e. proportional mass), biochemical composition, biodegradability, and persistence (Gregorich et al. 2006, 2015; Lützow et al. 2006; Poeplau and Don 2013; Curtin et al. 2022). The fraction associated with soil minerals, MAOC (<53 μm), is considered to represent the more stable and persistent fraction of soil C, defined as having longer residence times, greater resistance to decomposition, and more resistance to changes in land management (Lavallee et al. 2020). The POC fraction (>53 μm) is less stable and more vulnerable to changes in management and land use (Gregorich et al. 2006; Poeplau and Don 2013).

The benefits of soil C sequestration for climate change mitigation depend on ensuring that the new C that is stored is stabilised and persistent. While soils are generally thought able to accumulate SOC, they are limited in the quantity of C that can be stabilised through association with mineral surfaces and can thus become saturated (Six et al. 2002; Stewart et al. 2007, 2008; Cotrufo et al. 2019). However, there is debate in the scientific community on whether the total C stock of a soil becomes saturated (Kirschbaum et al. 2018; Begill et al. 2023).

There is increasing interest in the measurement and management of soil C at various scales. At the global scale, the 4p1000 initiative (4per1000.org) recognises the potential for soil C sequestration to contribute to reducing atmospheric CO₂ concentrations. At the national and farm scales, the development of protocols for monitoring soil C stocks (FAO 2019), the introduction of C accounting programmes [e.g. the Australian Carbon Credits (Carbon Farming Initiative) Act 2011], and voluntary C markets (e.g. Verra Verified Carbon Standard) have increased interest in soil C. While many of the protocols and programmes only relate to the total stocks of soil C, an improved understanding of the ability of soils to store C in a more persistent form would enhance the success of these programmes.

Many studies have attempted to estimate the additional amount of SOC that could be stored in soils at global (Georgiou et al. 2022), national (Angers et al. 2011; Beare et al. 2014; McNally et al. 2017), or regional scales (Wiesmeier et al. 2014, 2015; Emde et al. 2022). These estimates have been based on statistical relationships between SOC content and measured soil properties, such as silt and clay content (Hassink 1997; Angers et al. 2011; Wiesmeier et al. 2014; Georgiou et al. 2022) or mineral surface area (Beare et al. 2014; McNally et al. 2017; Emde et al. 2022). Furthermore, many of these studies base the estimates on additional storage of SOC within the mineral associated fraction of soil (e.g. silt and clay fraction). However, no previous studies have considered spatial variability in the stabilisation capacity (upper limit of MAOC) and sequestration potential of SOC at the farm scale, where differences in soil texture and properties may not be as variable as that observed at a regional or national scale, and the implications for SOC management to enhance SOC stocks at the farm scale.

An understanding of the current SOC stock and the potential for accumulation of SOC at the farm scale would allow land managers to make spatially explicit decisions and implement management change to increase their SOC stocks or reduce the losses of this SOC. In addition, more recent studies have called for more localised soil information to support and translate soil specific management options into SOC sequestration (Amelung et al. 2020). Management options that could be applied in a targeted manner, to soils with the potential to increase in SOC, include management of effluent or plant C inputs (Whitehead et al. 2018) or redistribution of biomass (Kirschbaum et al. 2020a). Alternatively, management practices known to decrease soil C stocks could be avoided from soils that have high SOC stocks or from soils that had high stocks relative to their mineral surface area.

The objective of this study was to apply previous methodology to evaluate the spatial variability in the mineral surface area and sequestration potential of SOC at a farm scale and assess how this variability might affect management decisions to increase farm-scale SOC stocks.

Methods

Site location

The study site was located at the Lincoln University Dairy Farm (LUDF) near Lincoln, Canterbury, New Zealand (43°38′41.41″S, 172°26′28.74″E). The farm is managed as a 160-ha milking platform utilising a high-quality irrigated pasture that is permanently grazed by dairy cows (3.4 cows ha⁻¹). The farm is split into two distinct blocks (‘North’ and ‘South’), which are both approximately 80 ha in size (Fig. 1). The annual dry matter (DM) production of the farm is approximately 19 t DM ha⁻¹ and total annual milk production is approximately 500 kg MS cow⁻¹ (SIDDC 2023). The pasture composition across the farm is predominantly a grazed perennial ryegrass (Lolium perenne L.) and white clover (Trifolium repens L.) sward, although some paddocks contain other species such as timothy (Phleum pratense L.), plantain (Plantago lanceolata L.), and chicory (Cichorium intybus L.). The mean annual rainfall (30 years) of the site is 660 mm and mean monthly minimum and maximum temperatures are 4°C and 32°C respectively. The annual average irrigation input for the site is approximately 450 mm of water applied predominately over the summer period. The site had a range of soil types distributed across the farm (Fig. 1, Table 1), which are representative of some of the most common soil types found across the Canterbury Plains, New Zealand.

Fig. 1.

Aerial view of the case study dairy farm comprising two blocks (North and South) with the respective soil map describing the various soil types as described in Table 1.

Table 1.The soil type, soil classification, abbreviation, and sand, silt, and clay contents of the soils across the case study dairy farm.

Soil type	NZSC	USDA equivalent	Abbreviation	Sand (%)	Silt (%)	Clay (%)
Eyre – shallow, stony soil	Recent	Entisols, Inceptisols	Ey	60	30	10
Eyre/Templeton – shallow	Recent	Entisols, Inceptisols	EyTe	55	35	10
Paparua – deep	Pallic	Inceptisols, Alfisols	Pp	50	35	15
Templeton – deep	Pallic	Inceptisols, Alfisols	Te	30	55	15
Templeton – deep, slow drainage	Pallic	Inceptisols, Alfisols	Tes	25	55	20
Temuka – very heavy, slow drainage	Gley	Aquic groups	Tms	5	45	45
Wakanui – deep	Pallic	Inceptisols, Alfisols	Wk	20	60	20
Wakanui – deep, slow drainage	Pallic	Inceptisols, Alfisols	Wks	15	60	25

Sand, silt, and clay percentages obtained from Smap-online (Manaaki Whenua 2023);

NZSC, New Zealand Soil Classification; USDA, U.S. Department of Agriculture.

Results

The apparent surface area of the soils in this study ranged within 28.6–117.30 m² g⁻¹ with a mean value of 57.86 m² g⁻¹ (Figs 2 and 3). The heavier textured Temuka soil type (Tms) had significantly higher surface areas than all the other soils (P < 0.001) with these remaining soil types not significantly different from each other. Adjusting for the effect of SOC content on the ADWC, as described by Kirschbaum et al. (2020b), resulted in a 25% reduction in the mean mineral surface area (57.86 m² g⁻¹) with values of 13.64–91.93 m² g⁻¹ (Figs 4 and 5). However, the distribution of mineral surface area across the farm was the same as that described for the apparent surface area.

Fig. 2.

Spatial distribution of apparent mineral surface area (m² g⁻¹) of soils across the farm.

Fig. 3.

Boxplots displaying the variability in apparent mineral surface area (m² g⁻¹) of individual soil types distributed across the farm. Refer to Table 1 for details of the individual soil types.

Fig. 4.

Contour plot of the mineral surface area (m² g⁻¹) distributed across the case study farm. The data presented are the result of the mineral surface area after removing the confounding effect of soil C as per Kirschbaum et al. (2020b).

Fig. 5.

Boxplots displaying the variability in mineral surface area (m² g⁻¹) of individual soil types across the farm after adjustment for the influence of soil organic C content. Refer to Table 1 for details of the individual soil types.

The spatial distribution of the C concentration across the farm also corresponded to soil type and mineral surface area, with the highest concentrations in soil types characterised as having heavier texture (Table 2) and greater mineral surface area (e.g. Temuka, Tms soil type). The total C concentration across the study site ranged within 2.0–5.9%, with an average concentration of 3.3% in the top 0.15 m of soil. The range in bulk density across the site was 1.20–1.42 g_soil cm⁻³, which is typical of these soil types under permanent pasture in the Canterbury region of New Zealand (Manaaki Whenua 2023).

The range in total stock of C in the 0.15 m depth layer was 39–106 tC ha⁻¹ with a mean of 63 tC ha⁻¹ (Figs 6 and 7). The highest C stocks were associated with the heavier soil types (e.g. Temuka) found in the south-western corner of the farm and the lower stocks were associated with the lighter soil types (e.g. Eyre and Templeton soils). Therefore we demonstrated that, at the farm scale, C stocks display large variation driven by changes in soil type despite being under similar management.

Fig. 6.

Spatial distribution of total organic carbon stocks (TOC, tC ha⁻¹) at the Lincoln University Dairy Farm (0.15 m depth). Please note the difference in the scale between the contour plot and boxplots. The boxplots reflect the entire range as opposed to the contour plot, which has smoothed the data in the interpolation process.

Fig. 7.

Total organic carbon stocks (tC ha⁻¹) for the various soil types at the Lincoln University Dairy Farm (0.15 m depth). Refer to Table 1 for details of the individual soil types.

The maximum amount of MAOC that could be held, the MAOC_UL, was calculated using a 90th quantile regression with mineral surface area. This statistical relationship was determined in previous studies (Beare et al. 2014; McNally et al. 2017). The MAOC_UL in the soils of the current study ranged within 17.3–77.0 mgC g_soil⁻¹ with a mean of 41.4 mgC g_soil⁻¹ (Figs. 8 and 9). When these upper limit C concentrations were expressed as C stocks, the values were 31.8–131.5 tC ha⁻¹ (Fig. 8), depending on mineral surface area. The distribution of this upper limit, for each soil, across the farm was used to explore the areas with greater potential to stabilise additional MAOC.

Fig. 8.

Spatial representation of the maximum amount of C (tC ha⁻¹) that can be stored in the MAOC fraction.

Fig. 9.

Maximum amount of carbon (mgC g⁻¹) that can be stored in the MAOC fraction by individual soil type across the farm. Refer to Table 1 for details of the individual soil types.

The total stock of SOC in the top 15 cm of soil across the whole farm, calculated by multiplying the mean stock of each soil type by its corresponding area, was approximately 10,267 tC. This stock equates to an average of 64 tC ha⁻¹ over the farm. Within this total SOC stock, the average C loading, or MAOC/A_m, of each soil type had values all below 1, with a range of 0.58–0.71 (mgC m⁻² mineral surface area, Fig. 10). These ratios, all lower than 1, indicate that each soil type would have available mineral surface area to further stabilise additional SOC.

Fig. 10.

The carbon loading ratio of the MAOC fraction relative to the mineral surface area (MAOC/A_m). Values equal to or greater than 1 are assumed to have full loading of carbon, or saturation, of the mineral surfaces.

Discussion

Mineral surface area is a functionally significant measure of a soil’s ability to stabilise SOC (Beare et al. 2014; McNally et al. 2017; Kirschbaum et al. 2020a). This functional relationship was demonstrated, using soils across New Zealand, where mineral surface area explained 84% of MAOC variability (McNally et al. 2017). In a subsequent study, using samples from within a farm in New Zealand, 75% of the variation of total SOC was explained by mineral surface area (Kirschbaum et al. 2020a). In the current study, apparent mineral surface area explained 51% of the variability in the total SOC. After adjusting the apparent mineral surface area values, for the confounding effect of soil C (Kirschbaum et al. 2020b), the mineral surface area explained 35% of the variation in total SOC. The current study had a much narrower range in mineral surface area values (14–92 m² g⁻¹) compared to previous studies (~20–240 m² g⁻¹; McNally et al. 2017) which could be one reason for the lower proportion of the variance explained by mineral surface area. Further investigation would be required to understand the reason for the lower proportion of the variance in SOC explained by mineral surface area at this site.

The MAOC fraction represents the largest proportion of SOC in New Zealand grassland soils, with approximately 83% of the total SOC being in MAOC (McNally et al. 2017) or 84–86% of total SOC (e.g. Pallic, Recent, and Gley soils) for the same soils investigated in the current study (McNally et al. 2018). For the purposes of this study, we assumed that MAOC comprised approximately 85% of the total SOC, in grassland soils, based on the evidence from these previous studies (e.g. Beare et al. 2014; McNally et al. 2017). Although there was variability in both the mineral surface area and SOC, across the farm in the current study, the MAOC_UL followed the same general patterns observed in the distribution of mineral surface area (Fig. 4) and total SOC (Fig. 6). The soils with the highest mineral surface area also had the highest MAOC_UL values (i.e. south-western area of the farm). These results are consistent with other studies internationally. For example, in a study based in Canada, the MAOC upper limit was demonstrated to increase with increasing clay content (Emde et al. 2022). Furthermore, a large meta-analysis highlighted that soil properties such as silt and clay were the most important factor defining the capacity of SOC (Matus 2021). Numerous other studies have applied this relationship between SOC and silt and clay content to define the maximum amount of C that can be stored in the MAOC fraction (Hassink 1997; Angers et al. 2011; Wiesmeier et al. 2014). Therefore, at the farm scale, the distribution of soil type is likely to be the key driver of any potential to increase SOC, based on inherent soil properties that are important for SOC protection (e.g. silt and clay content or mineral surface area).

As an alternative to calculating MAOC_UL using the quantile regression equation, we suggest that the loading of MAOC relative to the mineral surface area (i.e. MAOC/A_m) could be a useful proxy of a soil’s ability to further increase the MAOC. The maximum C loading of mineral soils has been proposed as 1.0 mgC m⁻² of mineral surface area (Feng et al. 2013) with mineral surface area being an important component of SOC storage. Therefore, MAOC/A_m could be viewed as a simplified way of determining whether the mineral surfaces are at their maximum C loading and whether a soil may respond to additional C input. When MAOC/A_m was explored spatially across the farm (Fig. 10), the C loading ranged within 0.38–1.32 mgC m⁻² with a mean of 0.67 mgC m⁻² for the entire farm (Table 2). The areas where the C loading was the greatest (i.e. Paparoa and Templeton soils, Figs 1 and 10) also corresponded to the areas which had the lowest MAOC_UL (Fig. 8; e.g. centre of Northern block). While previous studies have predicted the capacity of a soil to store additional SOC in the stable fraction (Beare et al. 2014; McNally et al. 2017; Emde et al. 2022) these have all relied on a theoretical estimate based on quantile regression. However, the C loading using MAOC and mineral surface area (both measurable properties of a soil) means that MAOC/A_m could be more meaningful and relevant to landowners, although further research is needed to investigate the efficacy of this approach.

The area with the lowest C loading was in the south-western corner of the farm, e.g. the Temuka soils, the same region that displayed the greatest potential to increase MAOC. Interestingly, the MAOC loading in the North block could in part be explained through historical effluent application. Parts of this block (within the pivot irrigation area) received effluent at an annual rate of 200 kg N ha⁻¹. Assuming an effluent C:N ratio of 3.4 kg kg⁻¹ as per effluent from a nearby dairy farm (Graham et al. 2022), the C input would have been increased by approximately 680 kg C ha⁻¹ year⁻¹ above that provided through plant inputs. This continued additional C input over time may have acted to load these mineral surfaces. The soil types within the North block had lower mineral surface areas compared to soils in the South block (e.g. Tms soil type, Fig. 4). Therefore, these soils should approach their theoretical maximum C loading sooner than the soils with higher mineral surface area, assuming similar rates of C input. It could be argued that applying effluent, or additional C, to soils that have higher mineral surface area and a lower MAOC loading would be more beneficial as these soils would have greater capacity to protect and retain these additional nutrients from losses. Applying effluent on the south-western area of the farm (e.g. Temuka soils) in the current study could lead to additional protection of C added due to the greater mineral surface area and lower C loading. However, these heavier soils are characterised as poorly drained compared to the soils in the North block, which are more free draining (Manaaki Whenua 2023). Therefore, while adding effluent to these heavier soils may help to increase soil C, the increased leaching risk and water logging of these soils would need to be carefully considered.

This C loading ratio may also act as an indicator for soils that are susceptible to loss of C. Previous studies have highlighted that as the C loading of mineral surfaces increases, the bond strength decreases, making the organic molecules more susceptible to microbial decomposition (Kögel-Knabner et al. 2008). Therefore, if a soil contains a C loading close to its protective capacity (i.e. defined by ratio close to 1.0 mgC m⁻²), the potential to further increase this stable fraction of C would be limited. However, these soils may be more susceptible to loss of SOC if changes of management occurred that either reduced C inputs or enhanced respiratory losses. Recent studies in New Zealand have demonstrated large losses of SOC and MAOC in cropping soils compared to grassland soils (McNally et al. 2017; Curtin et al. 2022), with a significant proportion of MAOC lost from these cropping soils (McNally et al. 2017). Therefore, the spatial distribution of this C loading, or the relative amount of MAOC in relation to the surfaces that act to protect it, would enable decision making that helps to protect these high SOC stocks. For example, avoiding management practices that enhance the decomposition of these SOC stocks (e.g. disturbances through forage cropping and extended fallow periods) on the soils that have high MAOC stocks relative to the mineral surface area would be critical.

While the areas that displayed the largest MAOC_UL also had the highest SOC stocks, it could be assumed that the soils are at a steady state based on New Zealand’s soil C inventory model (McNeill et al. 2014) assumptions and that the current farm has been under long-term pasture management. This farm has well documented and relatively high levels of pasture production for many years (SIDDC 2023). Hence, a limitation to further increasing the MAOC in the soils of the current study could be driven by limitations in C input. The farm is irrigated and receives frequent nutrient additions resulting in a highly productive pasture with an average annual pasture production since 2016 of around 14.8 t DM ha⁻¹ year⁻¹ (SIDDC 2023). Increasing pasture production and C inputs beyond this current level of production would be challenging. However, using the distribution of MAOC_UL, or the mineral surface area, would identify the areas on-farm that have greater potential to increase SOC where any practice that could lead to increases in SOC could be implemented.

This study applied a sampling approach based on the assumption of no prior knowledge of the site. This decision was made to capture data across the entire farm and understand the variability in soil properties such as mineral surface area and C concentration. However, one limitation to this approach is that it is expensive from both a labour and analysis standpoint, which would make this approach impractical and unviable in a commercial setting. Therefore, applying this approach using a stratified sampling such as that recommended for farm-scale monitoring (MPI 2020) would reduce cost and make it more viable for many farmers.

The information gained from this study would be useful to identify areas where soil C has the potential to be increased. However, while options to further increase SOC in New Zealand grassland soils are limited (Whitehead et al. 2018) they could be achieved by increasing C inputs through management practices such as effluent application, incorporation of supplementary feed, grazing management, and increasing root biomass. A modelling study demonstrated that feeding animals supplementary feed, in addition to pasture, could result in higher SOC through increasing the net C inputs of the system (Kirschbaum et al. 2017). However, no effect of importing supplementary feed on SOC was observed using eddy covariance measurements (Wall et al. 2019) and the production of the supplementary feed elsewhere, on-farm, results in large losses of SOC (Wall et al. 2020). While the results from these studies are contrasting, no baseline SOC was obtained that demonstrated that these soils had the capacity to increase in SOC. Therefore, future research investigating these management effects could benefit by establishing trials on soils that have capacity to increase in SOC. Consequently, understanding the distribution of SOC, mineral surface area, and areas of greater potential to protect additional C would greatly benefit the science community in addition to farmers. Furthermore, future research that applies this approach using a more stratified sampling would be beneficial to reduce sample size and cost and make the approach more viable and commercially practical.

Conclusion

The spatial distribution of SOC, mineral surface area, and potential to increase MAOC was well represented by the spatial distribution of soil type within a farm. This case study demonstrated that despite the farm operating at a very high level of pasture production and under the same management history, there are areas within the farm that had potential to increase the MAOC fraction. In addition, this case study highlights the potential of using the C loading ratio as a measure to identify soils with the potential to further increase SOC and soils that are more vulnerable to losing SOC. This information would allow more targeted and cost-effective approaches to increasing SOC rather than using a one-size-fits-all strategy across the entire farm. This case study also highlighted the ability of a model developed at the national scale (Beare et al. 2014; McNally et al. 2017) to be implemented at a smaller scale, which would be useful to farmers and land managers. The use of this approach would complement and enhance any farm-scale soil C monitoring (MPI 2020) or accounting programmes by highlighting soils with an additional ability to increase soil C or soils more vulnerable to losing soil C.