Plant and soil carbon, nitrogen and phosphorus on ... - Semantic Scholar

Symposium no. 02

Paper no. 1664

Presentation: oral

Plant and soil carbon, nitrogen and phosphorus on the Australian continent KIRBY Mac, RAUPACH Mike, BARRETT Damian and BRIGGS Peter CSIRO Land and Water, GPO Box 1666, Canberra ACT 2601, Australia Abstract We aimed to estimate for the Australian continent the overall biomass productivity, water balance, and nutrient exports from the soil. We used a steady-state model which describes the uptake of carbon by plants, limited by the available energy, water and nutrients, and the cycling of carbon and nutrients within the soil. The model solves steady-state equations of the balances of energy, water, carbon, and nutrients in various stores within plants and soil, and the fluxes between the stores. All fluxes are parameterised as long-term averages. Specifically, long term average evapotranspiration is constrained by soil water content in dry environments and the energy (radiation) in wet environments. Transpiration, light and air humidity saturation deficit (which influences water use efficiency) together determine the Net Primary Productivity (NPP, plant production of carbon). From NPP, the model tracks the flow of carbon through litter and soil stores (in upper and lower soil horizons) using rate constants and partitioning coefficients that depend on soil type and environment. Organic nutrient fluxes are linked to carbon fluxes by store-dependent stoichiometric ratios which are weakly sensitive to nutrient availability. The model requires detailed data describing climate, land use, vegetation cover, soil types and agricultural nutrient inputs. The soil types were used to estimate soil properties such as silt and clay fractions in an upper and lower soil horizon. The datasets were reconciled to a common spatial resolution of 0.05 degrees (about 5 km). The model output shows, as expected, that the principal determinant of the distribution of NPP on most of the Australian continent is water (transpiration). Saturation deficit is also important through its effect on water use efficiency, which implies less NPP per unit rainfall in the semi-arid tropics than the mid-latitude temperate regions. Data support this prediction. The stores of biomass C, litter C, soil C, total N, mineral N, total P and primary P are all strongly determined by NPP, showing significant responses to saturation deficit and temperature as well as the obvious rainfall response. The predicted distributions are supported by independent data. Agricultural nutrient inputs (fertiliser, legumes) have led to regional-scale increases (relative to preagricultural conditions) of up to a factor of 2 for NPP, and up to a factor of 5 for mineral N, primary P and N and P concentrations in soil water. This study of distributions of carbon, nitrogen and phosphorus in the Australian continent is based on a description of the processes of plant and soil cycling of material. This approach is complementary to that undertaken in the Australian Soil Resources Information System project, reported elsewhere in this conference. Keywords: material balance, spatial predictions, carbon, nutrients, plant, soil

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17th WCSS, 14-21 August 2002, Thailand

Introduction The terrestrial cycles of energy, carbon, water and nutrients are linked to the global climate and the global carbon cycle. Human activities such as farming and forestry disturb these cycles and can create both shortages (for example, the depletion of soil carbon) and excesses (nutrient leakage to streams). If we are to manage landscapes and ecosystems sustainably, then we must remedy the shortages and deal with the excesses. It therefore behoves us to understand the storages and fluxes of water, carbon and nutrients within landscapes. In Australia, a major program (the National Land and Water Resources Audit) has focussed for the last four years on determining the current state and trend of the land and water resources on the Australian continent. Within that program, one activity (the subject of this paper) has been to characterise the coupled cycles of water, C, and key nutrients (N and P) on Australian landscapes. The aims were (1) to determine the spatial patterns of the major stores and fluxes in the cycles of water, C, N and P; (2) identify key climate processes controlling these spatial patterns; and (3) assess the ways that the water, C, N and P cycles have changed in response to large-scale changes in land use, especially the introduction of cropping and grazing (both dryland and irrigated) since the settlement of Australia by Europeans from 1788 onwards. Methods We developed a model (BIOS) to describe the exchange of energy, water and carbon with the atmosphere, the inputs (as fertilisers or by fixation) and losses (in harvest or run-off) of nutrients, and the cycling of water, carbon and nutrients within the soil. BIOS is described in detail by Raupach et al. (2001). Figure 1 shows the main flows and stores of water, carbon, nitrogen and phosphorus considered in the model. BIOS starts by estimating transpiration, from which the Net Primary Productivity (NPP, the amount of carbon assimilated by plants less that required for their own maintenance) is calculated. Nitrogen and phosphorus uptake from the mineral stores in the soil is calculated from the NPP according to stoichiometric ratios that are generally fixed, but vary a little according to the nutrient status of the soil. As the plants die and decay, their carbon is delivered to the soil. There it cycles through various fast and slow pools, and is lost to the atmosphere by respiration. In BIOS, the soil carbon cycling is based on the CENTURY model (Parton et al., 1987, 1988, 1993) and is calculated for both an upper and lower soil horizon. The distributions to the different pools and turnover times within each pool are governed by partition and rate constants are based on those in CENTURY, adjusted to better simulate Australian soils. Nitrogen and phosphorus follow the carbon in storedependent stoichiometric ratios which are weakly sensitive to nutrient availability. Occluded phosphorus, which is not involved in rapid plant and soil cycling, is not considered in BIOS. We developed two versions of BIOS: BiosEvolve solves the full time dependent equations, whereas BiosEquil is an equilibrium or statistical steady state model. The statistical steady state solution arises when the input forcing variables (climate and so on) vary in time but have no overall trends (that is, are statistically steady). BiosEquil uses a simple formulation of the water balance in which the total evapotranspiration is determined by rainfall in dry environments and by a radiation-dependent potential evaporation in wet environments (calculated by the Priestley-Taylor approach), with a

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simple interpolation between the two. The total evaporation is the sum of plant transpiration and soil evaporation, with the partition between the two determined by leaf area index. The time averaged relative water content of a single soil water store is approximated as the ratio of actual total evapotranspiration to potential (PriestleyTaylor) evapotranspiration. The drainage rate is the product of the average relative water content and a drainage rate constant which is assigned on the basis of soil texture, modified to account for cultivation. The results reported here are based on BiosEquil.

Figure 1 Major pools and fluxes in the linked water, C, N and P cycles through the atmosphere, plants and soil. NPP is the sum of photosynthesis and plant (not litter and soil) respiration. The input data for the model comprised gridded datasets of climate (Jeffrey et al., 2001), soils, vegetation, land cover (Lu et al., 2001) and land use (including irrigation) (NLWRA, 2000), and a data set describing the agricultural nutrient inputs and offtakes (harvest) of nitrogen and phosphorus. Some of these datasets, particularly the land use and agricultural nutrients datasets, were developed by other projects within the Australian National Land and Water Resources Audit. All the datasets were resolved to a 0.05 degree grid, or approximately 5 km. The Digital Atlas of Australian Soils was used to identify the principal soil type in each 5 km grid cell. The soil type, in conjunction with look-up tables of McKenzie et al. (2000), determined several soil properties including silt and clay fractions, hydraulic conductivity, and density. The silt and clay fractions determined the values of some of the rate constants for the soil carbon pools.

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We used BIOS to simulate the NPP and soil carbon and nutrient cycling for two scenarios. In the first scenario, corresponding to present land use, we used all the input data including those describing agricultural inputs. In the second, corresponding to Australian land use prior to the advent of European-style agriculture, all agricultural nutrient inputs (irrigation, fertiliser inputs and harvest offtakes) were set to zero. The aim was to investigate both the current state of stores and fluxes and also the major changes brought about by modern agriculture. Modelling at this scale necessarily involves many simplifying assumptions, and the input data contained many uncertainties. The results should therefore be treated with caution. This issue will be commented on later. Results and Discussion The Net Primary Productivity for Australian is shown in Figure 2, for the first scenario corresponding to current agricultural inputs. This map is similar to a rainfall map for Australia, but less NPP was predicted in the north than would be expected were there exact correspondence between rainfall and NPP. The air saturation deficit is greater in the north of Australia due to the higher temperatures there, which led to less NPP per unit rainfall. This finding is supported by independent data (Barrett, 2001). The NPP was also greater than expected from rainfall alone where it has been influenced by agriculture (particularly irrigation).

Figure 2 Mean annual Net Primary Productivity with current climate and current agricultural inputs.

Figure 3 Sum of all litter and soil carbon pools, with current climate and current agricultural inputs.

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The total soil carbon (both upper and lower soil horizons) is shown in Figure 3. The soil carbon is strongly influenced by NPP, and hence by rainfall and saturation deficit. However, soil carbon turns over more rapidly in hot climates, and so the predicted soil carbon was smaller in the north than expected solely on the basis of the carbon input. Figure 4 shows the total and mineral nitrogen. They strongly resemble the soil carbon map, because the soil nitrogen is strongly coupled to the soil carbon through well-defined (though not constant) stoichiometric ratios.

Figure 4 Left: mean total plant-available N (including organic N in litter and soil pools, and mineral N). Right: mineral N. Both with current climate and agricultural inputs. The total (excluding occluded and some less available secondary P) and dissolved mineral phosphorus (Figure 5) shows the same spatial distribution as the nitrogen but with smaller quantities, since C:P ratios in plants and soil are greater than C:N ratios. The total amount of phosphorus in the soil is greater than indicated in Figure 5, because much soil phosphorus is in occluded and secondary forms not considered in BIOS. The phosphorus shown in Figure 5 is not necessarily comparable to soil test data.

Figure 5 Left: mean total P (organic in P litter and soil pools and dissolved P, but excluding secondary and occluded P). Right: dissolved P store. Both with current climate and current agricultural inputs.

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The impact of modern agriculture can be seen in Figure 6, which shows the ratio of NPP with agricultural inputs to NPP without agricultural inputs. Over much of the continent there was no change because either there is no agriculture, or there is extensive grazing that does not use irrigation or fertiliser. In the agricultural areas, however, NPP was predicted to increase by factors of up to about two in response to fertiliser and irrigation inputs. The factor of two increase is at the scale of the 5 km grid cell: locally, particularly in irrigation areas, larger increases might obtain.

Figure 6 Ratio of mean NPP with current agricultural inputs (irrigation, N and P inputs and offtakes) to mean NPP without agricultural inputs. Whereas the increase predicted in total nitrogen was similar to that of NPP, the mineral nitrogen was predicted to increase by factors of up to 5 at the 5 km scale (Figure 7). Prior to modern agriculture the main input of nitrogen was fixation by the native vegetation, and was closely tied to NPP. With modern agriculture, the nitrogen budget has changed substantially. The largest input remains fixation, but this has been greatly increased by sown legumes, both crop and pasture. Fertiliser nitrogen contributes less over the whole continent (by a factor of about 7), but locally may be the dominant nitrogen input term. Inputs of nitrogen from a modern agriculture appear to have had a greater impact on the soil mineral nitrogen than on NPP (in terms of the relative increase of those quantities).

Figure 7 Ratios of mineral N store with current agricultural inputs (irrigation, N and P inputs and offtakes) to mineral N store without agricultural inputs.

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Figure 8 shows predicted total run-off (overland flow plus base flow) which was greatest in the wet margins of the continent. The spatially distributed runoff aggregated to 245 drainage basins in Australia compared reasonably well to measured drainage division runoff data. Figure 9 shows the predicted leaching of nitrogen, which is one of several nutrient flux terms predicted by BIOS. The nitrogen leaching term is of particular interest because of potential environmental impact. As expected, the greatest nitrogen leaching was predicted in the South eastern and South western parts of the continent, which are wetter and more intensively used for agriculture.

Figure 8 Mean annual total runoff (surface plus subsurface).

Figure 9 Mean annual deep drainage. BiosEquil uses several crude assumptions (particularly in respect of soil water and drainage) and ignores several processes (such as the impact of fire and grazing). There is considerable uncertainty about many of the input parameters, but the overall uncertainty is probably not as great as it might appear from apparently large uncertainties in individual fluxes because they are constrained by overall mass balance requirements and by the coupling of balances between different entities. Uncertainties over smaller areas increase. The predictions are designed to determine large-scale patterns, not the behaviour of individual farms or paddocks, and should never be interpreted at single grid cell (5 km) scale. Some of the limitations (particularly those pertaining to the soil water) of BiosEquil will be overcome by using the dynamic model BiosEvolve. These are all issues that we will consider in future work.

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Conclusions BIOS represents an attempt to simulate coupled biophysical and biogeochemical cycles at the landscape scale in the Australian continent. This approach is complementary to that undertaken in the Australian Soil Resources Information System project, reported elsewhere in this conference (Bui et al., 2002). Simplifications in the process description and uncertainties in input data are unavoidable. Notwithstanding the uncertainties, the results suggest that agriculture has increased nutrient stores, particularly mineral nitrogen and phosphorus, by more than the increase in landscape production (NPP). This is especially evident in the 400 to 700 mm rainfall zones in the south eastern and south western agricultural regions. This in turn suggests that agricultural inputs are at or approaching a point of diminishing returns in which substantial extra inputs yield modest increases in NPP. At the same time, the increased mineral nutrient stores present an increased risk of nutrient leakage below the root zone or to streams. Acknowledgements This work was supported by the National Land and Water Resources Audit. We have received much benefit from interactions with colleagues (some working on related NLWRA projects), including Elisabeth Bui, Helen Cleugh, Dean Graetz, Stefan Hajkowicz, Ray Leuning, Hua Lu, Chris Moran, Ian Prosser, Graeme Priestley, Doug Reuter, David Simon, Chris Smith, Murray Unkovich, Bill Young and Mike Young. References Barrett, D.J. 2001. NPP multi-biome: VAST calibration data. 1965-1998. http://www.daac.ornl.gov/NPP/html_docs/vast_des.html, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. Bui, E.N., B. Henderson, C.J. Moran and R. Johnston 2002. Continental-scale spatial modelling of soil properties. 17th WCSS (these proceedings). Jeffrey, S.J., J.O. Carter, K.B. Moodie and A.R. Beswick. 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software 16(4):309-330. Lu, H., M.R. Raupach and T.R. McVicar. 2001. Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. CSIRO Land and Water Technical Report 35/01, CSIRO Land and Water, Canberra, Australia. McKenzie, N.J., D.W. Jacquier, L.J. Ashton and H.P. Cresswell. 2000. Estimation of soil properties using the Atlas of Australian Soils. Technical Report 11/00, CSIRO Land and Water, Canberra, Australia. NLWRA. 2000. 1996/97 Land Use of Australia. http://www.nlwra.gov.au/full/20_products/05_by_subject/15_land_resources_and_ mgt/05_Land_Use_Mapping/land_use_of_Aus_v1.html, National Land and Water Resources Audit, Commonwealth of Australia, Canberra, Australia. Parton, W.J., D.S. Schimel, C.V. Cole and D.S. Ojima. 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal 51:1173-1179.

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Parton, W.J., J.M.O. Scurlock, D.S. Ojima, T.G. Gilmanov, R.J. Scholes, D.S. Schimel, T. Kirchner, J.C. Menaut, T. Seastedt, E.G. Moya, A. Kamnalrut and J.I. Kinyamario. 1993. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem. Cycles 7 (4):785-809. Parton, W.J., J.W.B. Stewart and C.V. Cole. 1988. Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry 5:109-131. Raupach, M.R., J.M. Kirby, D.J. Barrett and P.R. Briggs. 2001. Balances of water, carbon, nitrogen and phosphorus in Australian landscapes: I. project description and results. CSIRO Land and Water. Technical Report 40/01.

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