CSIRO PUBLISHING Soil Research, 2013, 51, 645–656 http://dx.doi.org/10.1071/SR12358
Relationship between environmental and land-use variables on soil carbon levels at the regional scale in central New South Wales, Australia Warwick B. Badgery A,E, Aaron T. Simmons A, Brian M. Murphy B, Andrew Rawson C, Karl O. Andersson A,D, Vanessa E. Lonergan D, and Remy van de Ven A A
NSW Department of Primary Industries, Orange Agricultural Institute, 1447 Forest Road, Orange, NSW 2800, Australia. B NSW Office of Environment and Heritage, PO Box 445, Cowra, NSW 2794, Australia. C NSW Office of Environment and Heritage, c/o Charles Sturt University Orange, Leeds Parade, Orange, NSW 2800, Australia. D School of Environment and Rural Sciences, University of New England, Armidale, NSW 2351, Australia. E Corresponding author. Email:
[email protected]
Abstract. The potential to change agricultural land use to increase soil carbon stocks has been proposed as a mechanism to offset greenhouse gas emissions. To estimate the potential carbon storage in the soil from regional surveys it is important to understand the influence of environmental variables (climate, soil type, and landscape) before land management can be assessed. A survey was done of 354 sites to determine soil organic carbon stock (SOC stock; Mg C/ha) across the Lachlan and Macquarie catchments of New South Wales, Australia. The influences of climate, soil physical and chemical properties, landscape position, and 10 years of land management information were assessed. The environmental variables described most of the regional variation compared with management. The strongest influence on SOC stock at 0–10 cm was from climatic variables, particularly 30-year average annual rainfall. At a soil depth of 20–30 cm, the proportion of silica (SiO2) determined by mid-infrared spectra (SiMIR) had a negative relationship with SOC stock, and sand and clay measured by particle size analysis also showed strong relationships at sites where measured. Of the difference in SOC stock explained by land use, cropping had lower soil carbon than pasture in rotation or permanent pasture at 0–10 cm. This relationship was consistent across a rainfall gradient, but once soil carbon was standardised per mm of average annual rainfall, there was a greater difference between cropping and permanent pasture with increasing SiMIR in soils. Land use is also regulated by climate, topography, and soil type, and the effect on SOC stock is better assessed in smaller land-management units to remove some variability due to climate and soil. Additional keywords: land management, mid-infrared (MIR) spectroscopy, particle size analysis, silica, soil organic carbon. Received 6 December 2012, accepted 21 September 2013, published online 20 December 2013
Introduction Soil organic matter (SOM) is the living and dead organic material, other than living plant roots, in the soil (Baldock and Nelson 2000), and soil organic carbon (SOC) is a component of SOM. Changing land use to increase SOC stocks has been proposed as a mechanism to offset greenhouse gas emissions that are associated with climate change (Lal 2004; Sanderman and Baldock 2010). Moreover, higher SOM can increase the supply of nutrients to plants, soil aggregation and structure, infiltration of rainfall, and water-holding capacity (Whitbread et al. 1998), which in turn can improve agricultural productivity. Generally, there has been a decline in SOC in Australian soils since the clearing of native vegetation and cultivation for agriculture (Luo et al. 2010), but there is considerable variation in the Journal compilation CSIRO 2013
rate of decline of SOC stocks and the influence of different management practices. The stocks of SOC result from the balance between inputs and losses of carbon (Sanderman et al. 2010). Land management that is more productive in the same environment generally yields higher SOC (Sanderman et al. 2010). Meanwhile the loss of soil carbon is determined by mineralisation, which is regulated by climate (temperature and rainfall), soil properties, biological activity, the degree of physical soil disturbance, and landscape position (moisture conditions). Average annual rainfall is highly positively correlated with SOC (Post et al. 1982; Dalal and Mayer 1986; Grace et al. 2006) and the potential to store SOC (Luo et al. 2010). Average temperature is generally negatively correlated with rainfall in south-eastern Australia. Areas with cooler temperate and wetter www.publish.csiro.au/journals/sr
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environments generally have higher annual biomass production than those that have higher temperatures and are more arid, and this contributes to higher SOC (Sanderman et al. 2010). Soil moisture and temperature also regulate the decomposition of organic matter (Davidson and Janssens 2006). Environments that are wetter but not saturated and hotter have higher rates of mineralisation of organic matter. Decomposition is primarily driven by higher temperatures, but lower soil water content also constrains the rate of decomposition (Davidson et al. 2000; Davidson and Janssens 2006; Craine et al. 2013). Soil texture strongly influences the concentration of carbon in soil. Soils with a high clay content have a greater capacity to protect carbon from decomposition, which causes slower mineralisation of organic matter (Oades 1988), and these soils tend to have higher soil carbon levels (Baldock and Nelson 2000). The stabilisation of organic materials in soils is a function of the chemical characteristics of the soil mineral fraction and the presence of multivalent cations, the presence of mineral surfaces capable of adsorbing organic materials, and the architecture of the soil matrix (Oades 1988; Baldock and Skjemstad 2000). While particle size clearly influences SOC concentration (Oades 1989), clay content is often used to describe the capacity of a soil to store SOC. Vertosols, which have a higher clay content than other soil types, have a slower rate of degradation of SOC as a result of land-management practices (Dalal and Mayer 1986; Luo et al. 2010; Sanderman et al. 2010). Extensive reviews have examined how land management influences soil carbon under Australian conditions (Luo et al. 2010; Sanderman et al. 2010). Often there has been considerable variability in the response to the same land-management practices, which can be attributed to the direction of the change in SOC stocks (i.e. whether there is an increase in stocks or just a reduction in losses) (Sanderman and Baldock 2010). When native vegetation is converted to cultivation (determined by paired adjacent sites), most of the reduction in SOC was calculated to have occurred in the first 10 years (Luo et al. 2010). However, soil carbon level may increase again when cropping land is converted back to pasture (Young et al. 2009). For crop production, land-management practices that have been demonstrated to increase SOC include stubble retention, conservation tillage, pasture phases, nitrogen (N) fertilisation (Luo et al. 2010; Sanderman et al. 2010), and soil amendments (Sanderman et al. 2010). For pasture management, practices that aimed to increase herbage mass production generally increased the amount of SOM (Conant et al. 2001). Fertilisation with superphosphate (Chan et al. 2010; Williams and Donald 1957) and establishment of more productive pasture species and improved grazing management (Conant et al. 2001) have all shown increases in SOC, but management practices other than fertilisation have not often shown differences in SOC stocks in Australia (Henderson et al. 2005; Chan et al. 2010). One of the key issues preventing the introduction of a trading scheme for soil carbon is the inability to determine accurately the sequestration potential for any given area of land, although the scale of the area also has an influence. Often the physical constraint of the climate and soil type cannot be defined at a fine enough scale, nor the role of previous land management on
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current soil carbon levels determined. There have been attempts to subdivide regions into zones for assessing soil carbon (e.g. Murphy et al. 2010) and to map soil carbon at a national scale (Henderson et al. 2005) or regional scale (Henderson et al. 2005; Minasny et al. 2006). Climatic and soil variables regulate the capacity to store SOC (Baldock and Nelson 2000), and the expected range of soil carbon at a given location must be understood before land management can be determined from survey results. The aim of this paper was to identify the most important environmental variables (climate, soil physical and inherent chemical properties, landscape and topography) that determine SOC stocks in the Lachlan and Macquarie catchments of New South Wales (NSW), Australia. The influence of agricultural land use (i.e. cropping, pasture in rotation, and permanent pasture) was then assessed across significant environmental gradients. It was hypothesised that SOC stocks would be influenced largely by climate (rainfall and temperature), then by soil and landscape properties, with land use being a thirdorder influence at the catchment scale of this survey. It was hypothesised that differences in SOC due to land use could be determined at the catchment scale, but land management (e.g. stubble retention, tillage type, and stocking rate) could not. A secondary aim of this paper was to determine how several soil properties predicted from mid-infrared (MIR) spectra using generic models (e.g. Silica (Si) and clay) compared with results for particle size analysis (PSA) method and their relationship with SOC stocks. Materials and methods This study formed part of the National Soil Carbon Research Program, which used a consistent approach to soil sampling and analysis to determine the changes in SOC over a range of environments and management regimes (Sanderman et al. 2011). There were 354 individual sites sampled across the Macquarie and Lachlan Catchments of NSW (Fig. 1) from February 2010 to October 2011. The sites were stratified into 15 region soil type land use combinations (Table 1). Site identification Sites were randomly selected from a list of potential landholders, developed from researching land tenure and landscape properties from various sources. The landholders were contacted by telephone and sites were chosen if the landholder was willing to participate and the management records or farming systems were appropriate. Each landholder identified several potential paddocks that represented the nominated systems, and the paddocks that were sampled were chosen at random. Soil sampling A quadrat 25 by 25 m was established in an area of the paddock that represented the average landscape, soil type, and vegetation conditions. The approximate location was generally determined on a map before entering the paddock, and the location was altered if there were abnormalities (e.g. soil type unrepresentative of the paddock average) or obstructions (e.g. timbered areas or rocky outcrops) in that area. Two soil cores, 50 mm in diameter, were taken from the south-western
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Scale: 1:2 800 000 Projection: Lambert Conformal Conic 0
25 50 75 100 Kilometres
City Town Highways Soil sampling sites
Fig. 1. Map of the Lachlan and Central West Catchments of New South Wales, Australia, with the location and system of the sites sampled. Table 1. Region, land use, soil type, and number of sites for the systems sampled Soil type: Australian Soil Classification (Isbell 2002) Region
Land-use
Soil type
Plains Plains Plains Plains Slopes Slopes Slopes Slopes Slopes Slopes Tablelands Tablelands Tablelands Tablelands Tablelands
Cropping Cropping Naturalised pasture Naturalised pasture Carbon farming Pasture cropping (PC) Permanent pasture (PC comparison) Crop–pasture rotation (pasture) Crop–pasture rotation (crop) Perennial pasture High input perennial pasture Low input perennial pasture Low input perennial pasture Pasture, continuous grazing Pasture, rotational grazing
Chromosols/Dermosols Vertosols Chromosols/Dermosols Vertosols Chromosols/Dermosols Chromosols/Dermosols Chromosols/Dermosols Chromosols/Dermosols Chromosols/Dermosols Chromosols/Dermosols Dermosols (volcanics) Dermosols (volcanics) Chromosols (sediments) Chromosols (granites) Chromosols (granites)
corner to a depth of 1 m or as deep as was possible and were used to confirm the soil type and describe the soil profile. The soil was classified using the Australian Soil Classification, to at least soil order level according to Isbell (2002). Ten coring points were located using random coordinates within the quadrat (Sanderman et al. 2011). Soil cores were collected using a hydraulic percussion corer, with a tube 75 mm in diameter. Any above-ground plant material and litter was removed from the surface to expose the mineral soil before sampling. Intact cores were extracted to a depth of ~40 cm and cut to depths of 0–10, 10–20, and 20–30 cm using a guillotine, with the
Sites 24 26 31 19 20 24 24 25 25 25 25 25 25 17 19
remaining soil discarded. The depth of the hole from which each core was extracted was measured to ensure cores had not been compacted during sample extraction. If cores did not remain intact or there was a discrepancy with the depth (>2 cm), then the core was resampled as close to the first soil core as possible. A minimum of 10, 7, and 5 intact cores were extracted for 0–10, 10–20, and 20–30 cm depths, respectively, because at times it was impossible to extract 10 intact cores from the latter two depths, due to gravel or extremely wet or dry soils. Average numbers of 10, 9.6, and 8.4 intact cores were extracted for 0–10, 10–20, and 20–30 cm depths, respectively. It was
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important that cores extracted by the hydraulic corer were intact because they were used to calculate bulk density as well as for chemical analysis. Management data Land-management data were collected in a survey with the landholder. The survey included: crop type, grain yield, tillage practice and number, fallowing periods, stubble management and burning, pasture type (introduced v. native), stocking rate (dry stock equivalents (DSE)/ha), grazing management (set stocking v. rotational), amounts of fertiliser (N, phosphorus (P), potassium (K)), other soil amendments, and hay production for each of the previous 10 years (Sanderman et al. 2011). Average time grazed was also calculated as a measure of the intensity of rotational grazing (i.e. 100% = continuous grazing, with decreasing values representing increasing intensity of rotational grazing). A tillage index (TI) was calculated to give a scaled weighting to more frequently cultivated paddocks. For each crop, tillage was rated as 0 = no till, 1 = minimum till and 2 = multiple cultivation, divided by the potential maximum value (20). Weather data Thirty years of monthly climate data were determined for each site using their latitude and longitude from SILO (Jeffrey et al. 2001). Data for average monthly temperature (8C), total rainfall (mm), total evaporation (mm), average radiation (MJ/m2), and average daily vapour pressure (hPa) were obtained (Sanderman et al. 2011). Topography Elevation (m; vertical distance above sea level), aspect (8; direction in which a land surface slope faces from north), slope (%; the inclination of the land surface from the horizontal), topographic wetness index (TWI; index based on the ratio of local catchment area to slope) and focal median of slope (%; FM300Slope, the median of slope over a 300-m radius circle) for each site were determined from the Smoothed Digital Elevation Model of Australia (DEM-S; ANZLIC unique identifier ANZCW0703014016), in February 2000 (Gallant and Wilson 2000; Sanderman et al. 2011). Soil analyses All depth segments were processed for individual cores or pooled for the 10 cores at each site. A detailed description of soil processing and analysis for soil carbon and fractions is given by Sanderman et al. (2011). Processing To determine field moisture, a subsample (or multiple subsamples) with a cumulative weight of at least 50 g was taken from each site. Subsamples were weighed, dried at 1058C for 24 h or until dry, and re-weighed to determine the moisture percentage of the subsample. The moisture percentages were then applied to the total wet soil mass to estimate the total oven-dry weight of each sample. After the moisture subsamples were removed, the remaining sample was air-dried at 408C for 48 h or until a constant weight was reached. For 0–10 cm samples, the complete sample was
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sieved to 0.78). In addition, the environmental variables may have removed variation due to the management and this was examined further. The interaction between environmental and management variables was examined using CCA. The eigenvalues for the
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Table 2. Summary statistics for soil organic carbon (SOC stock; Mg C/ha) at different soil depths Summary statistics
SOC0–10
SOC10–20
SOC20–30
SOC0–30
Mean Median Minimum Maximum Standard deviation Standard error Skewness Kurtosis
21.1 20.1 4.8 50.2 8.47 0.45 0.82 0.72
10.2 9.6 2.9 27.9 4.18 0.22 1.22 2.13
6.9 6.2 1.4 22.1 3.11 0.17 1.31 3.26
38.1 35.6 14.9 99.1 13.88 0.74 1.20 2.18
first and second axes were 0.143 and 0.024, respectively (Fig. 2). The cumulative variance explained by management was 11.6% and 13.5% for these first two axes. The cumulative variance for the management and environment interactions was 79.1% and 92.5% for the first two axes. The first axis mainly separated cropping (years of crop, total N applied as fertiliser, and years of fallow) from pasture (years since crop and DSE/ha) systems for management. The environmental variable that was positively associated with pasture systems was higher average annual rainfall, which was also highly correlated with elevation, average temperature, and easting (Table 5). Higher soil P, clay, and pH were associated with cropping systems. This axis largely reflects the change from a Tablelands environment that is dominated by pastures to the Slopes where cropping becomes much more common. The second axis mainly separated years of hay from TI for management. The environmental variables clay,
pH, and Al (MIR) were all positively associated with greater tillage. The implication is that there is an interaction between environmental effects and the land-use effects. Two variables, 30AR, which was selected to represent the climate variables, and SiMIR, were investigated for their relationship with SOC stock at different soil depths. There was a linear relationship between 30AR and SOC0–10 (adjusted R2 = 0.51, P < 0.001; Fig. 3). The relationship between SOC and 30AR decreased deeper in the soil, with a poor relationship for SOC10–20 (adjusted R2 = 0.10, P < 0.001) and no relationship for SOC20–30. The SiMIR (mg/g) was predicted for each soil depth. At the soil surface there was no relationship between SOC0–10 and SiMIR 0–10 cm (Fig. 3), for SOC10–20 and SiMIR 10–20 cm there was a poor linear relationship (adjusted R2 = 0.07, P < 0.001), and there was a stronger linear relationship for SOC20–30 and SiMIR 20–30 cm (adjusted R2 = 0.25, P < 0.001). The relationship between CLAYMIR and SOC stock was weaker than that of SiMIR for each soil depth (data not presented). The effect of the strong climatic gradient on SOC stock also influenced land management, particularly the division between cropping and pasture systems. Land use was divided into cropping, pasture in rotation, and permanent pasture based on the land use at the time of sampling, to determine whether the difference due to these land uses were being removed by the lasso environmental analysis and to determine whether there were differences across the environmental gradient. The strong relationship between SOC0–10 and 30AR (Fig. 3) showed significant difference due to land use (Fig. 4). There was no significant difference in the slope for the linear relationships,
Table 3. Environmental variables that influenced soil carbon stock (SOC stock; Mg C/ha) at 0–10, 10–20, 20–30, and 0–30 cm depths The residuals from the environmental analysis were regressed against the management variables, but none was selected. The dependent variables are listed in the order they were selected in the model, with the final model significance presented. TPI, Topographic position index Independent
Model
Dependent 2
Coefficient
Std error
P-value
SOC0–10
Adj. R = 0.66 (P < 0.001)
Intercept Av. annual rainfall (mm) Elevation (m) Av. annual temp. (8C) SiMIR (mg/g soil) Slope relief class TPI mask pH(water) Tablelands region Slope (%)
22.2 0.023 0.006 –0.348 –0.016 0.013 0.486 –0.190 0.109 0.007
7.23 0.004 0.002 0.252 0.004 0.029 0.638 0.356 0.706 0.061
0.002
