Conceptual frameworks for estimating the water quality benefits of improved agricultural management practices in large catchments

Conceptual frameworks for estimating the water quality benefits of improved agricultural management practices in large catchments

G Model AGEE-4061; No. of Pages 18 ARTICLE IN PRESS Agriculture, Ecosystems and Environment xxx (2012) xxx–xxx Contents lists available at SciVerse ...

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G Model AGEE-4061; No. of Pages 18

ARTICLE IN PRESS Agriculture, Ecosystems and Environment xxx (2012) xxx–xxx

Contents lists available at SciVerse ScienceDirect

Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

Conceptual frameworks for estimating the water quality benefits of improved agricultural management practices in large catchments P.J. Thorburn a,∗ , S.N. Wilkinson b a b

CSIRO Ecosystem Sciences, GPO Box 2583, Brisbane, QLD 4001, Australia CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia

a r t i c l e

i n f o

Article history: Received 2 May 2011 Received in revised form 29 December 2011 Accepted 29 December 2011 Available online xxx Keywords: Erosion Grazing Great Barrier Reef Nitrogen Pollution Sediment Sugarcane

a b s t r a c t Chemical and sediment losses from agricultural lands are threatening coastal marine and aquatic ecosystems in many parts of the world. This is an acute problem in Australia, where the condition of Great Barrier Reef (GBR) ecosystems is threatened by increased pollutant loads from agricultural lands, and Governments have enacted policies to reduce pollutant exports. These policies raise the question of how to identify changes in land management that will effectively reduce exports. The scale of the GBR catchments (> 400,000 km2 ) precludes detailed modelling investigations, especially within the time scale of policy implementation. Therefore, we developed conceptual frameworks linking agricultural land management to river pollutant exports for two contrasting agricultural pollutants posing threats to the health of GBR ecosystems; dissolved inorganic nitrogen (DIN) and fine sediment (silt and clay), based on a synthesis of past studies. We argue that nitrogen (N) Surpluses (N inputs relative to crop N off-take), are the primary driver of DIN losses from agricultural land to rivers. Similarly, previous studies in GBR grazing lands and elsewhere have quantitatively defined how sediment losses from hill slopes, gullies and stream banks are related to grazing land condition, ground cover and riparian management, which are products of recent climate and grazing practices. From these frameworks we derive relationships between firstly, estimated N Surplus and DIN exports, and secondly ground cover and river fine sediment exports. Using these relationships we examine how DIN and fine sediment exports to the GBR may respond to a range of management scenarios for reducing N inputs, and increasing ground cover and improving riparian management. We predict that widespread adoption of the most extreme scenarios would approximately meet water quality improvement targets set/implied by governments for these two pollutants. However, it is unlikely that these extreme scenarios will be adopted to the extent needed and in the time frames set by current policy. In particular, the agri-environmental management practices defined in this study for N are generally unproven in GBR cropping systems, the required levels of pasture cover and riparian management are generally beyond current experience, and it can take decades to improve land condition, and so reduce erosion rates after cover increases. We also show that the approach taken is applicable to other pollutants, such as total N, that combine characteristics of the pollutants considered here. For the case of total N, the reductions in pollutant loads are not as great smaller relative to targets than for DIN or fine sediments. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.

1. Introduction The loss of chemicals and sediments from agricultural lands is threatening coastal marine and aquatic ecosystems in many parts of the world, notably areas of USA, Europe and China (Howarth et al., 2002; Howarth, 2008; Stoate et al., 2009; Ju et al., 2009). Recent deforestation and agricultural development is accelerating this process in tropical regions (Filoso et al., 2006). One example of this problem is north eastern Australia. There, pollutants

∗ Corresponding author. Tel.: +61 7 3833 5715; fax: +61 7 3833 5505. E-mail address: [email protected] (P.J. Thorburn).

transported from catchments draining into the GBR lagoon are impairing the condition of corals and associated ecosystems of the Great Barrier Reef (GBR) World Heritage Area (De’ath and Fabricius, 2010), and also having substantial economic consequences in the region (Access Economics, 2007). The pollutants include nitrogen (N) and phosphorus, pesticides and fine sediment (fine silt and clay) that are generated by agricultural activities undertaken since European settlement (Kroon et al., 2012). Much of the sediment and particulate nutrients are derived from erosion of grazing lands within 80 km of the coast (McKergow et al., 2005a), while the dominant source of dissolved nutrients and pesticides are areas of intensive cropping, commonly on coastal floodplains (Bramley and Roth, 2002; McKergow et al., 2005b; Mitchell et al., 2009;

0167-8809/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2011.12.021

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Davis et al., this issue). In response to these detrimental impacts, Australian governments have enacted policy to reduce these pollutant exports (Anon, 2003, 2009) and protect coastal ecosystems of GBR. There are two major policy approaches have been enacted to improve the quality of water leaving agricultural areas. First, substantial incentives (totalling AU$146M) have been made available to farmers through the Australian Government’s Reef Rescue programme (Commonwealth of Australia, 2008) to change management and so improve water quality in relation to defined targets. Second, there are regulations requiring farmers to undergo property management planning and adopt management practices aiming to reduce risk of sediment and chemical losses and meet water quality targets (State of Queensland, 2009). These incentives and regulations are driving rapid management practice changes in agricultural lands of the GBR. As with all government programmes seeking to improve water quality, the success of these two initiatives relies, in part, on the identification of management practices that will have a positive and measurable impact on water quality (Dowd et al., 2008). Unfortunately, in contrast to some regions (e.g. in Californian Central Valley, Dowd et al., 2008; The Netherlands, De Vries et al., 2003) there is limited empirical evidence in the GBR of the water quality improvements likely to result from changes in agricultural practices (Thorburn et al., 2011c). In the absence of empirical data, or where there is a need to explore responses beyond those defined in empirical studies, modelling of agricultural systems can provide valuable insights to the links between land management and water quality (e.g. Arnold and Fohrer, 2005; Vigiak et al., 2011). However, the large area of the GBR catchments (423,000 km2 or 5.6% of the Australian continent) has limited availability of input data and resources for modelling efforts. To date only decadal-scale catchment models, that lump the generation and delivery of pollutant constituents from land uses to streams, have been use to quantify land management effects on GBR pollutant loads (McKergow et al., 2005a,b; Armour et al., 2009; Hunter and Walton, 2008). These models have helped identify dominant pollutant sources, estimate pollutant loads in rivers, and provided preliminary estimates of achievable pollutant reduction. But, they have little or only simplistic representations of farm management practices and so provide inadequate information on the specific practice changes required to meet water quality improvement targets or the economic consequences of these changes. A different modelling approach is using outputs from detailed, one-dimensional models of agricultural systems as inputs to catchment models. Relevant models are available for GBR grazing (McKeon et al., 1990) and cropping (Thorburn et al., 2005, 2011a; Biggs et al., this issue) systems, and this approach has been used to evaluate the water quality and economic consequences of changing agricultural management in one GBR catchment (Roebeling et al., 2009; van Grieken et al., this issue). Unfortunately, such model integration is information intensive and so has been limited to sub-regional scales (∼2000 km2 ) in the GBR, as elsewhere (Vigiak et al., 2011). Thus, where neither comprehensive empirical information nor appropriate modelling analyses are available, an alternative approach is needed for evaluating the extent to which agricultural management practices need to change to improve water quality and reduce ecosystem damage. The existence of catchment and agricultural systems models indicates that the biophysical processes governing generation of agricultural pollutants and linkages between management practices and pollutant losses are understood, and may provide the basis for an alternate, but more approximate means of evaluating water quality benefits of agricultural practice changes. In this paper, we draw on this existing understanding of agricultural pollutant generation to develop conceptual frameworks

that describe the first-order linkages between management practices and pollutant losses, to enable evaluation of water quality benefits of agricultural practice changes. We then review the information available in the GBR region to define the parameters in the frameworks, and establish relationships (or delivery ratios) between agricultural pollutant losses and river pollutant exports under current management. We then use these relationships to estimate the effect of scenarios of improved management on river pollutant exports in the GBR region. The analysis focuses on two pollutants in the GBR, Dissolved Inorganic N (DIN) from cropping lands and fine sediment from grazing lands. Both pollutants have significant ecosystem impacts (De’ath and Fabricius, 2010). However, they also have contrasting processes of pollutant generation and transport, thus demonstrating the generality of the approach and its applicability to pollutants of intermediate properties. 2. Conceptual frameworks 2.1. DIN losses from cropping systems 2.1.1. Sources of DIN exported from GBR rivers DIN exports to rivers are highly variable, but are dependent on the intensity of human activity (Billen et al., 2010) and human-derived N inputs in the catchment (Howarth et al., 1996). Agriculture can be an important source of human-derived DIN (Howarth, 2008; Seitzinger et al., 2010), and in some GBR catchments agriculturally derived N has been shown to influence DIN concentrations in rivers (Bramley and Roth, 2002; Mitchell et al., 2009), and groundwaters (Thorburn et al., 2003a). Groundwater is relevant in this context because groundwater DIN can discharge into rivers in the GBR (Rasiah et al., this issue). Agriculturally derived DIN can originate from a range of sources, such as applied fertiliser, biological N fixation and/or mineralisation of soil organic matter. There are other potential anthropogenic sources of DIN in rivers, e.g. atmospheric deposition and sewage, and these have been found important in developed areas of the world (Howarth et al., 1996; Howarth, 2008). However, the contribution of atmospheric deposition to DIN export from GBR catchments is likely to be relatively small compared with more developed regions, because of the lower population density and associated industrial N emissions (Howarth, 2008). The lower population densities also mean that N from sewage treatment works are a minor input of N to the GBR (McKergow et al., 2005b). 2.1.2. N Surpluses as an indicator of DIN exports N inputs to cropping systems can have several fates; namely uptake by crops, storage in the soils and losses to the environment. At steady-state, soil storage is not significant (Jansen and De Willigen, 2006) so the difference between N inputs and crop N uptake, i.e. the N Surplus, is an indicator of environmental losses over the long-term (Sieling and Kage, 2006; Buczko et al., 2010; Perego et al., 2012). N Surpluses can be assessed at different scales (Oenema et al., 2003), commonly whole farm (i.e. farm gate), or single field (often termed ‘soil surface’ balances). These approaches will be similar unless there are substantial transfers of nutrients between fields within a farm, such as on farms with intensive livestock production (Oenema et al., 2003). Intensive livestock production systems are not common in GBR catchments, and so examination of N Surpluses can focus on the field scale. Surplus N can be lost to the environment through various pathways, namely leaching, runoff and atmospheric losses (denitrification and volatilisation). The partitioning will depend on the soil, climate and management. Thus linking N Surpluses to a particular loss pathway, e.g. runoff or deep drainage in the context of

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Fig. 1. Change in (a and b) N Surplus (circles) and (b only) cane yield (diamonds) and nitrogen use efficiency (NUE: crop N uptake as a % of N applied; squares) with increasing N fertiliser applications for crops in the GBR. Data from (a) 11 sugarcane N experiments averaged over approximately 3 crops (after Thorburn et al., 2011b; Prove et al., 1997) and single year results from a banana nitrogen experiment (after Prove et al., 1997) and (b) a single sugarcane N rate experiment averaged over four crops (after Thorburn et al., 2003b). The lines indicate the regressions through the N Surplus (solid lines) and NUE (dashed line) data. In (b), N fertiliser was applied either sub-surface (solid symbols) or on the soil surface (open symbol).

GBR water quality, may be problematic (Buczko and Kuchenbuch, 2010). However, as a first principle, we expect changes in N Surpluses to be reflected in relative changes in N losses in all pathways. There are a number of studies that to support this expectation in the GBR region: Firstly, river N concentrations are well correlated with the proportion of upstream land uses occupies by cropping systems with high N fertiliser inputs (Bramley and Roth, 2002; McKergow et al., 2005b). Secondly, simulation studies show clear relationships between N Surpluses and DIN losses (Thorburn et al., 2011a; Biggs et al., this issue). Finally, and more directly, reducing N fertiliser applications by 47% over three sugarcane crops in an experiment in the Wet Tropics reduced the N Surplus by ∼60%, and resulted in similar relative reductions in DIN in both runoff and deep drainage (Webster et al., 2010, in press). Thus for the GBR, N Surpluses may be a useful simple indicator of N exports in the long-term. N Surpluses of some major crops grown in GBR catchments are significantly (P < 0.01) correlated with N fertiliser applications (Fig. 1). This occurs because yield or N off-take in these crops is not responsive to N fertiliser application beyond a certain threshold (Thorburn et al., 2011b; Armour et al., this issue; Prove et al., 1997). 2.1.3. Evaluating nitrogen use efficiency as an indicator of DIN exports Much of the focus of managing N fertiliser in cropping systems aims to increase N use efficiency (NUE, the proportion of N applied taken up by the crop). NUE is related inversely to N Surplus (e.g. Fig. 1b) and has been used as an indicator of environmental loss (Buczko and Kuchenbuch, 2010). Management practices to improve NUE of crops have been promoted in the GBR in the context of reducing N losses (State of Queensland, 2009; Thorburn et al., 2011c). Thus, it is worth considering the links between NUEimproving practices and water quality in the GBR. One practice commonly promoted to enhance NUE is burying N fertiliser. Much of the N fertiliser applied to crops in GBR catchments is applied as urea. When applied on the soil surface, ammonium volatilisation can be a significant pathway for loss of N in the GBR (Freney et al., 1992). Burying urea-N has been demonstrated to reduce volatilisation losses by 85% and increase NUE by ∼15% in sugarcane crops (Prove et al., 1997). However, the reduced volatilisation of N means that burying urea-N also increases the input of N into the soil relative to surface-application. As a result, burying urea also increases N losses in runoff and deep drainage (Fig. 2) and, importantly, increases these to a greater extent than NUE. Webster et al. (in press) also found burying urea increased

both NUE and N lost in runoff. Thus from a water quality perspective, burying urea changed the partitioning between N loss pathways (volatilisation v. runoff and deep drainage) more than it reduced total losses; an example of ‘pollution swapping’. Another management practice promoted in the GBR for reducing N losses and (implicitly) increasing NUE is controlled traffic, the concept being that reduced runoff in controlled traffic management reduce N lost in runoff and increase N update by the crop. However, in a field scale comparison of controlled traffic and conventional tillage practice (Masters et al., 2008) controlled traffic reduced runoff by 32%, but only reduced N lost in runoff by 11%. We expect the outcome would be similar for other NUE-enhancing practices, such as splitting N applications or using different N chemical fertiliser forms. A more effective approach to increase NUE is to reduce N fertiliser inputs while maintaining similar crop yields: That is, maintaining similar crop N uptake at reduced N fertiliser inputs, analogous to reducing the N Surplus. This approach can give much greater increases in NUE (e.g. double; Fig. 1b) than the practices described above (e.g. 15%, Fig. 2). The reductions in DIN losses resulting from this strategy have already been described (above). In the GBR, the potential to substantially reduce N fertiliser

Fig. 2. The amount of N (% of N in fertiliser applied) in harvested cane and lost to the atmosphere (via volatilisation and denitrification), in deep drainage and runoff, relative to urea-N fertiliser applied (at 160 kg ha−1 ) to a sugarcane crop either on the soil surface or buried. Source: data from Prove et al. (1997).

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grazing practices influence each of these erosion processes, culminating in a conceptual framework linking grazing practices to field-scale sediment losses. Sediment loss from other land uses can be included within the framework, but it is excluded for this analysis since grazing land is the major fine sediment source; by compiling previous sediment budget modelling of individual river basins (Brodie et al., 2003) we estimate that grazing supplies 74% of fine sediment export to the GBR.

Fig. 3. Conceptual relationship between the factors involved in N fertiliser management and N lost to water courses in regions draining to the Great Barrier Reef.

inputs below current recommendations without reducing yields (so substantially increasing NUE) has been demonstrated in both sugarcane (Thorburn et al., 2011b) and banana (Armour et al., this issue) production. 2.1.4. Framework relating N management practices to DIN losses From the foregoing analysis, we conclude that the primary strategy to reduce N losses to the environment is to reduce the N Surplus, that is to reduce N fertiliser inputs while maintaining similar crop yields. Practices aiming specifically to increase NUE, such as manipulating the placement or timing of N fertiliser applications, give small increases in NUE (or decreases in N Surpluses), but, at best, give even smaller water quality benefits or even dis-benefits. Thus, such NUE-enhancing practices can be considered management tactics. Unless N fertiliser applications have been optimised and the N Surplus minimised, applying management tactics may result in ‘pollution swapping’ between loss pathways rather than an overall increase in the environmental performance of the crop. Based on these arguments, we develop a framework (Fig. 3) relating N management practices to DIN losses to water courses. Firstly, N Surplus is the primary indicator of N losses from fields. N Surplus is affected by N inputs to the crop and crop size (or crop N uptake), crop size being the result of climate, crop management, etc. Secondly, surplus N is partitioned across the different potential loss pathways. Partitioning is affected by soil type and climate, and modified by tactical management practices (often seeking to improve NUE). While Fig. 3 is developed primarily at the plot scale, we argue that the concepts will be valid at the catchment scale in the GBR. In-catchment N loss process (such as in-stream denitrification) are small because of modest distances between cropped fields and water courses and fast travel times of water during significant runoff events that transport the majority of water-borne chemicals, resulting from highly variable natural rainfall (Petheram et al., 2008) or excessive irrigation (Thorburn et al., 2011a). 2.2. Fine sediment losses from grazing lands 2.2.1. Processes generating fine sediment in grazing lands Livestock grazing exacerbates the erosion of fine sediment, and associated particulate nutrients, by the removal of in situ vegetation cover, and by the physical disturbance of soil such as compaction causing increased surface runoff. At the field-scale, the key grazing management controls influencing vegetation cover and the physical impacts on soils are the stocking durations and stocking rates (animals ha−1 ). The primary erosion processes in Australian environments are sheetwash or rill erosion on hillslope surfaces, and gully and streambank erosion within drainage networks (Prosser et al., 2001). The following sections examine how

2.2.1.1. Hillslope erosion. Ground vegetation cover, defined as herbaceous foliage and litter, is the primary indicator of the effect of grazing practices on erosion of hillslope surfaces. Vegetation cover has been widely used for assessing grazing effects on rangeland condition (Bork et al., 1998; West, 2003; Karfs et al., 2009). The effect of ground vegetation cover on sheetwash and rill erosion has been widely measured and codified in empirical models. The ‘cover factor’ in the Universal Soil Loss Equation (USLE; Wischmeier and Smith, 1978), and subsequent derivatives, is perhaps the best known empirical model and is used here to represent the link between vegetation cover and hillslope erosion. The USLE cover factor is non-linearly related to ground cover, and predicted hillslope sediment yields increase substantially at ground cover levels below 50% (Wischmeier and Smith, 1978; Rosewell, 1993). The effect of grazing pressure on vegetation cover is quantified by the pasture utilisation rate. In a seasonally wet-dry climate the pasture utilisation rate is defined as the proportional reduction in above-ground dry-weight pasture biomass during a dry season (McKeon and Rickert, 1984). In rangeland environments, the stocking rate (animals ha−1 ) that results in a given utilisation rate can vary greatly between years, due to the effect of variations in rainfall on biomass at the start of the ‘dry season’. This is particularly the case in GBR catchments, where the climate and rainfall is highly variable in world terms (Petheram et al., 2008). Even if management of stocking rate is responsive to seasonal conditions, there are physical and economic constraints on the land holder’s ability to adjust herd sizes to match biomass at the start of each dry season, and consequently pasture utilisation rates vary between years. The potential for hillslope erosion in the subsequent wet season is then strongly dependent on the vegetation cover at the end of each dry season. Management of grazing pressure must also consider the land condition, i.e. pasture species composition and pasture basal area, and soil properties. These properties may be degraded by historical grazing impacts, thus reducing the capacity to produce useful forage during the wet season (McIvor et al., 1995), which in turn reduces the rates of animal production that can be supported while avoiding further land degradation (Ash et al., 1995). In summary, the core grazing practice affecting hillslope erosion is stocking rate, which together with recent climate and land condition affects utilisation rate, and consequently cover. There is large spatial variation across landscapes in the impact of grazing on vegetation cover, resulting from variations in grazing pressure and in the vulnerability to erosion (West, 2003; Stokes et al., 2009). The result is small areas of low cover from which fine sediment loss can be significant. In the Burdekin River catchment for example, patches with <10% cover occupy less than 10% of total area, but can produce more than 90% of hillslope sediment (Bartley et al., 2010b). As well as bare patches on hillslopes, two further types of vulnerable areas endemic across rangelands are (i) established gully networks and (ii) riparian zones. The linkages between grazing practices and these erosion processes are described below. 2.2.1.2. Gully erosion. Gully erosion is the incision of a former surface drainage line by concentration of overland runoff, and development into a channel >0.5 m or more in depth, being too large to plough across (Poesen et al., 2003). Gully erosion can be widespread in grazing rangelands, because once vegetation cover

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is reduced in drainage lines, soil there is especially vulnerable to erosion (Prosser and Slade, 1994). For example today there exist approximately 80,000 km of gullies within the GBR catchments (Hughes et al., 2001; Trevithick et al., 2008; Kuhnert et al., 2009). In gullied areas more than 75% of fine river sediment can be derived from sub-surface soils (e.g., Wilkinson et al., in press). There are no robust predictive models of gully erosion rates, let alone models that incorporate the effects of management practices (Poesen et al., 2003; Valentin et al., 2005). However, three processes can be identified as important determinants of the effectiveness of changing grazing management practices on erosion rates in existing gullies, and on the risk of new gullies forming: (1) Reducing runoff from upslope into the gully decreases the rate of headward extension of the gully and hence sediment yield, as headward extension is correlated with runoff (Wijdenes and Bryan, 2001). Reducing runoff also decreases the sediment transport capacity of the gully channel, so increasing retention of mobilised sediment within the gully. (2) Increasing cover on gully walls decreases sheetwash and rill erosion from the walls. The threshold for initiation of incision of new gullies is also sensitive to the erosion resistance of the hillslope surface (Prosser and Dietrich, 1995). (3) Reducing the sediment transport capacity of the gully channel, by reducing the slope gradient or increasing roughness, enhances sediment deposition on the gully floor reducing net export of sediment from the gully and promoting vegetation establishment. The key indicator of grazing impact on these three processes is the vegetation cover within the vulnerable gullied areas (Fig. 4). Engineering approaches to preventing erosion through these processes, such as runoff diversion banks and check-dams, can be highly effective, but are expensive per unit area of treatment (Valentin et al., 2005). However, changes in grazing practices to increase ground cover can also reduce runoff volumes (Process 1 above) through impeding the overland flow of water to allow more time for infiltration (McIvor et al., 1995), and in the longer term by changing the physical properties of the soil to increase infiltration capacity. Increasing vegetation cover on gully walls protects against splash, sheet and rill erosion (Process 2), but does not protect against sidewall retreat and mass movements (MartinezCasasnovas et al., 2009). Thus, Process 2 is an effective control measure only on older gullies where gully depth has naturally stabilised. Establishing or enhancing vegetation on gully floors (Process 3) can increase the sediment trapping by several times compared with gully floors without vegetation (Molina et al., 2009). Grazing infrastructure, such as paddock subdivision and watering points away from streams, is the means by which grazing pressure can be distributed across the landscape to increase cover and reduce erosion in vulnerable areas (Hunt et al., 2007), such as gullied areas. 2.2.1.3. Streambank erosion. Stock access to streambanks, and riparian tree cover, are the main factors that can be managed to reduce streambank erosion in grazing lands. Correlation between riparian vegetation and bank erosion has been widely observed (Trimble and Mendel, 1995). However, practice change needs to be at suitable scale to be effective; for example, small-scale revegetation of incised stream networks is not sufficient to reduce bank erosion rates (Harmel et al., 1999). As for gully erosion, the key grazing practice which affects streambank erosion is the distribution of grazing pressure away from the vulnerable area, being the riparian zone. The key indicator of grazing impact on streambank erosion is the vegetation cover within the riparian zone (Fig. 4).

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2.2.2. Framework relating grazing management to fine sediment loss Based on the above analysis, we conclude that the core grazing practices affecting fine sediment losses from paddocks are: (i) stocking rate corresponding to pasture utilisation rate, which influences hillslope runoff and erosion rates; and (ii) distributing grazing pressure across the landscape to avoid damaging vegetation cover in vulnerable areas including gullies and streambanks. The effects of current practices on erosion rates are also influenced by the starting pasture biomass, which is determined by recent climatic conditions and by land condition (through its effect on pasture species composition and soil surface condition). These linkages between site conditions, practices and fine sediment loss are assembled into a conceptual framework (Fig. 4). Two indicator metrics of sediment loss emerge from these linkages; paddock spatial-mean cover as an indicator of sheetwash erosion, and cover within vulnerable areas (particularly near drainage lines) as an indicator of erosion by rilling, gully and streambank erosion. 3. Quantifying the effect of GBR agricultural practices on catchment exports In this section we quantitatively evaluate the relationships outlined in the frameworks developed above (Figs. 3 and 4). To do this we gather data on, firstly N Surpluses and river DIN exports, and secondly ground cover, grazing management and fine sediment exports from GBR catchments under current management practices. Recently, exports of a range of pollutants, including DIN and fine sediments, to the GBR have been estimated for all the GBR catchments (Kroon et al., 2012). Further, exports have been partitioned into those occurring naturally and those caused by anthropogenic activities, a distinction made as policies for improving water quality stipulate a reduction in only the anthropogenic portion of the currently measured loads (Anon, 2009). 3.1. Nitrogen management To estimate N Surpluses for the major crops grown in GBR catchments we need to collate information on yields, N fertiliser inputs and crop N off-take. (NB: All data are quoted as kg ha−1 of elemental N.) Information on N fertiliser applications is publicly reported for some crops, but has to be estimated from N fertiliser management recommendations for others. We also consider other inputs of N, e.g. from biological N fixation and net mineralisation of soil organic matter (although we argue below that these inputs have been small in recent times). We derived crop N off-take from information on a combination of N concentrations and yields of harvested product and (where applicable) residue removed. The crops grown, their management and level of production vary within the GBR region. In an effort to account for this variability we collate information for each of the catchment-based Natural Resource Management Boards1 within GBR. In one case, information is available for two sub-regions, the Herbert River and the Wet Tropics, within the jurisdiction of one Board (Terrain NRM). Information on N fertiliser applications is publicly reported for some crops, but has to be estimated from N fertiliser management recommendations for others. 3.1.1. Major crops grown in the GBR Information on the extent of different crops grown in the GBR is available from the Queensland Land Use Mapping Program (QLUMP, 1999; 2004) and, from industry statistics for some crops

1 Organisations forms by the Australia Government to develop and deliver policies for improved natural resource and environmental management.

Please cite this article in press as: Thorburn, P.J., Wilkinson, S.N., Conceptual frameworks for estimating the water quality benefits of improved agricultural management practices in large catchments. Agric. Ecosyst. Environ. (2012), doi:10.1016/j.agee.2011.12.021

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Fig. 4. Conceptual relationships between the grazing practices and other factors affecting erosion and fine sediment loss water courses in regions draining to the Great Barrier Reef.

(e.g. sugarcane and grains). The latter data are useful as they more closely reflect the areas planted to those crops (and hence where fertiliser is applied), whereas QLUMP data tend to reflect the sum of planted area and fallow and headland areas. Grains are grown over the largest area, but dominantly in the Fitzroy region (Table 1). Sugarcane area is ∼60% that of grains, with substantial areas in each of the GBR regions except the Fitzroy region. The Cape York region is excluded from this analysis as cropping areas are very small relative to other regions. Areas of other crops (i.e. horticulture [small crops], tree fruits/bananas and cotton) are an order of magnitude smaller. In the Wet Tropics, bananas strongly dominate the tree fruits/bananas classification, with tree fruits (e.g. mangoes, macadamia, avocadoes) dominating in the other regions. The largest areas of cotton are grown in the irrigation areas of the Fitzroy region, with small areas (generally rainfed) in the Burdekin area.

3.1.2. N fertiliser applications and crop N off-take 3.1.2.1. Sugarcane. The only detailed, publicly available information on N fertiliser applications (courtesy of the Fertiliser Industry Federation of Australia) and crop production (Anon, 2010) is available for sugarcane. Since the late 1990s, N input rates averaged over all GBR regions have declined from ∼200 to ∼165 kg ha−1 over the period 2004–2008, in response to a combination of initially low sugar prices and then high N fertiliser prices. In the corresponding period, there has been little change in cane yields. N application rates vary between regions (Table 2), as do cane yields, averaging 79–85 t ha−1 (in 2004–2008) for all regions except the Burdekin (where crop are irrigated) which was 116 t ha−1 (Anon, 2010). N concentrations in cane are variable, but an average value is 0.51 kg N (t cane)−1 (Thorburn et al., 2011b). Using this value, and assuming 20% more N is lost in crop off-take where crop residues are burnt (Thorburn et al., 2011b; which mainly occurs in the Bur-

dekin region), annual crop N off-take for sugarcane in 2004–2008 ranged from 40 to 71 kg ha−1 (Table 2). 3.1.2.2. Bananas. A comprehensive survey of banana farmers in the Wet Tropics found a wide range of N application rates (100–1100 kg ha−1 yr−1 ), averaging 520 kg ha−1 yr−1 (Daniells, 1995). Subsequent studies indicate average rates of 300–350 kg ha−1 yr−1 (Moody and Aitken, 1996; Prove et al., 1997; Armour et al., 2009). Estimates of N in crop off-take have been as low as ∼30 kg ha−1 yr−1 (Weier, 1994; Prove et al., 1997), but higher values (e.g. ∼75 kg ha−1 ) seem more realistic (Moody and Aitken, 1996; Armour et al., this issue) and adopted in this study (Table 2). 3.1.2.3. Other tree crops. Macadamias are an important tree crop in GBR regions south of the Wet Tropics. Macadamia N application rates varied between 110 and 190 kg ha−1 yr−1 , with crop N off-take from 35 to 77 kg ha−1 yr−1 (Moody and Aitken, 1996; Stork et al., 2009). We adopt the average of these studies for our analyses (Table 2). There is no published information on other tree crops in GBR regions south of the wet tropics, principally mangoes and avocadoes. N applications and off-take may be lower but the N surpluses are generally similar (Huett and Dirou, 2000). Therefore, we apply the information for macadamias to those crops also. 3.1.2.4. Small crops. There is a wide array of small crops grown within GBR catchments. Published information on N applications to the different crops is sparse. However, the small amount of information available show considerable variability in application between different crops. For example, Moody and Aitken (1996) reported 216 and 65 kg ha−1 of N applied to capsicum and tomato crops, respectively. Conversely, there was a smaller difference in crop offtake between the crops: 120 kg ha−1 for capsicum and 84 kg ha−1 for tomato. The result for this tomato crop (off-take greater than N

Table 1 Areas (ha) of the different crops grown in the Great Barrier Reef regions that have significant cropping. Details of sources are given in the text.

Sugarcane harvestedb Horticulture Tree fruits/bananas Cotton Grains a b

Wet Tropicsa

Herberta

81,302 4877 22,057 0 0

56,772 290 122 0 0

Burdekin 71,061 11,073 4089 1167 74,794

Mackay Whitsunday

Fitzroy

115,207 1232 444 0 0

677.45 4804 968 24,954 472,255

Burnett Mary 53,371 17,437 1691 0 72,111

Total 378,391 39,713 29,372 26,121 619,160

For the N loss analysis, the Herbert River catchment is analysed separately from the remainder of the Wet Tropics region. For the period 2004–2008.

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Table 2 Rates of N fertiliser application, crop N off-take and N Surplus (all kg ha−1 yr−1 ) for different crops grown in the Great Barrier Reef regions (data sources are given in the text). Crop

N applied

N in crop-off take

N Surplus

N Surplus (%N applied)

Sugarcane (2004–2008) Wet Tropics Herbert Rivera Burdekin Mackay Whitsunday Burnett Mary

140 149 213 168 138

42 43 71 41 40

98 106 142 127 98

71 71 67 76 71

Tree crops Banana Other tree crops

325 150

75 56

250 94

77 63

Small crops Capsicum and tomatoes

216

120

96

44

Cotton Irrigated Rainfed

265 110

133 46

132 64

58 50

Grains Cereal farming systems

100b

90

10

10

a b

For the N loss analysis, the Herbert River catchment is analysed separately from the remainder of the Wet Tropics region. This figure accounts for multiple sources of N input to grains, i.e. fixed by legumes, mineralised, and fertiliser.

fertiliser application) is unlikely to represent long-term management. Another study found high N applications to, and substantial soil mineral N accumulations under both capsicum and tomatoes (Weier and Haines, 1998), suggesting more similar management of these two crops. The situation for small crops is even more complicated because of the short growing season of the crops and the potential for more than one crop per year. In the absence of better information, we adopt the results from capsicum (Moody and Aitken, 1996) as representative of all small crops in the GBR (Table 2). 3.1.2.5. Cotton. Cotton N fertiliser applications differ considerably between irrigated and rainfed production because of the different production potential (e.g. 10 bales ha−1 vs. 4 bales ha−1 ). In the Fitzroy region, cotton is predominantly irrigated and recommended N fertiliser applications are ∼265 kg ha−1 . For a lint yield of 2000 kg ha−1 in irrigated production (Rochester et al., 2009) the N off-take would be approximately 133 kg ha−1 (Rochester, 2007) (Table 2). Small areas of rainfed cotton are grown in the Burdekin area. In this area, we assume N fertiliser applications of 110 kg ha−1 and lint yields of 700 kg ha−1 , giving N off-take of approximately 46 kg ha−1 (Rochester, 2007). 3.1.2.6. Grains. In cereal crops, N exports in harvested grains vary widely, e.g. 25–150 kg ha−1 (Lester et al., 2010) driven by high yield variability caused by large rainfall variability. However, we assume average off-take is approximately 90 kg N ha−1 (from yields of ∼4 t grain ha−1 ) with N inputs of 100 kg ha−1 (Table 2). These inputs can come from fertiliser (Lester et al., 2010), residual biologically fixed N from grain legumes incorporated into cereal rotations (Huth et al., 2010), or mineralisation of soil organic matter (Dalal and Probert, 1997). The latter source becomes negligible in soils that have been cropped for several decades (Radford et al., 2007; Huth et al., 2010), as have most in GBR catchments (discussed below). For this study, we have assumed the water quality impacts of N from fertiliser and biological N fixation to be equivalent. Legume grain crops are often grown in rotation with cereals. N is seldom applied to legume grain crops as their N requirements are met through biological N fixation. So, off-take will be in reasonable equilibrium with N fixation. 3.1.3. Other N inputs There can be N inputs to cropping systems from sources other than fertiliser, the two main ones being net N mineralisation from soil organic matter and biological N fixation. There is also N input

from and sugar mill wastes, especially from applications of ‘mill mud’ (also called filter cake or press mud) to sugarcane fields. These inputs are considered in detail below. 3.1.3.1. N mineralised from soil organic matter. Soil organic matter dynamics can affect inputs of N to cropping systems, and so may contribute to N exports. There is net mineralisation of N as soil organic matter decomposes, so run down of soil organic matter over time causes a net input on N into cropping systems. Run down of soil organic matter during cropping following clearing of native vegetations is well documented in north eastern Australia (Wood, 1985; Dalal and Probert, 1997). Indeed, current nutrient recommendations for sugarcane explicitly consider inputs from mineralisation of organic matter (Schroeder et al., 2005), although these relationships have been determined in laboratory incubations and so do not account for organic matter inputs and associated N immobilisation from crop residues, root systems, etc., described below. However, over time organic matter concentrations reach (a dynamic) equilibrium after soils have been cropped for several decades, and so net mineralisation of N from year-to-year becomes negligible. For example, in a long-term experiment monitoring grain cropping following clearing of native vegetation in the Fitzroy catchment (Radford et al., 2007; Huth et al., 2010) there were substantial decreases in organic soil C and, more so, N concentrations approximately the first 10 years of cropping, then organic C and N concentrations remained constant (Huth et al., 2010). In this phase of soil constant C and N, crop yields and grain protein contents were relative low, and N in grain was 5–20 kg ha−1 where it had been 50–100 hg ha−1 during soil organic matter rundown and N mineralisation phase (Radford et al., 2007). In competition with soil organic matter rundown and net mineralisation of N, there are practices that increase organic matter and result in net immobilisation of N. One such common practices is retention of crop residues, that distinctly increases soil organic matter relative to residue burning (Thomas et al., 2007; Thorburn et al., 2012) and promotes N immobilisation (Robertson and Thorburn, 2007). In the GBR region, crop residues were traditionally burnt but are now dominantly retained in the two major GBR cropping systems, grains (Thomas et al., 2007) and sugarcane (Thorburn et al., 2012) production. Thus, given that (1) cropped lands in GBR catchments have generally been cultivated for many decades and so organic matter run down has greatly declined and, (2) crop residues retention has been widespread for some decades and will be increasing soil organic matter and immobilising N, we assume that net N inputs from net mineralisation are negligible.

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3.1.3.2. Biological N fixation. There is potential for substantial N inputs to cropping systems through biological N fixation, commonly in legumes growing within the system. Grain legumes are a common crop in grains production rotations, as described above. Legumes are also grown in rotation with sugarcane (Park et al., 2010). In addition to fixation in legumes, sugarcane itself has been associated with biological N fixation (Boddey et al., 2003), although these inputs have been found negligible in Australia (Biggs et al., 2002; Thorburn et al., 2003b) and so will not be considered further. We have described N inputs from legumes in grains production systems, above. Therefore, in this section we focus on biological N fixation in other cropping systems in GBR regions. In Australian sugarcane production systems, legumes are grown as a break crop in the fallow between sugarcane cropping cycles2 . Thus a legume crop will generally be grown once in every 4 or 6 years in these systems and can only occupy a small proportion (e.g., <20%) of the total area under sugarcane production. Given that planting legumes during fallows is not universal, the actual area of legumes may be substantially lower (∼2.5%; Bell et al., 2007), although the trend for planting fallow legumes is increasing. These legumes crops contain from 20 to 360 kg ha−1 of N (Schroeder et al., 2005; Park et al., 2010), depending on the species and whether they are grown as lay crops or harvested for grain, compared with recommended N fertiliser applications to sugarcane plant crops of 100–160 kg N ha−1 . While it is recommended that N applications to plant crops following fallow legumes be reduced by an amount equivalent to the legume N content (Schroeder et al., 2005), fertiliser reductions may only partially off-set legume N inputs (Park et al., 2010). However, the combination of the partial off-set and the relatively small area occupied by legumes means that the mean annual inputs of N from legumes across all sugarcane growing areas is likely to be small (e.g., <3 kg N ha−1 ), especially in relation to estimated of N fertiliser applications (Table 2). Actual N inputs may be higher than this estimation if N fertiliser reductions are not as great as those recommended, or the relative area of legumes is greater than assumed. However, not all N in legumes is necessarily sourced from biological N fixation (K. Macpherson, unpublished data), and so the actual net N inputs maybe lower than the above estimate. Thus, the errors in the estimation will tend to cancel out and the overall conclusion, that net inputs of N from legumes is small, is likely to be robust across the whole GBR region. We do acknowledge that fallow legumes may substantially increase N losses from specific fields (Park et al., 2010; Thorburn et al., 2011a), potentially causing N loss ‘hot spots’ although not contributing greatly to the GBR region-wide situation. It is possible that legumes may also be grown in rotation with cotton or horticultural crops (e.g. small crops). However, little information is available on these rotations so it s not possible to estimate net N inputs. However, we assume these inputs would be small given the relatively small area of these crops (Table 1). 3.1.3.3. Mill mud. Mill mud includes soil and sugarcane plant material filtered from sugar syrup during raw sugar production, and so contains N and other nutrients. It is primarily disposed of from Australia sugar mills through application to sugarcane fields. Some N in mill mud could be considered to be recycled when applied to fields. However, it is applied over a small proportion of the harvested area, so ‘returned’ to fields at much higher rates than ‘collected’. At these high rates mill mud contains considerable N.

2 Sugarcane is a semi-perennial crop. Commonly in Australia, the crop planted, harvested 12–18 months later, then allowed to re-grow (ratoon) and harvested approximately annually. The crop loses vigour after 3–5 harvests. At this time it is destroyed, and the field is fallowed for ∼6 month until the next sugarcane crop is planted. This sequence, planting to planting, is called a cropping cycle.

Fig. 5. Average annual anthropogenic dissolved inorganic N (DIN) load (relative to catchment area) exported from rivers in six Great Barrier Reef regions (WT: Wet Tropics, H: Herbert, B: Burdekin, MW: Mackay Whitsunday, F: Fitzroy, BM: Burnett Mary) as a function of N fertiliser surplus (relative to whole catchment area). The solid line is the regression (given by Eq. (1) in the text) and the dashed line the 95% confidence intervals. Data sources are given in the text.

Fertiliser management recommendations recognise this input and ‘discount’ recommended N rates to some extent following application of mill mud (Schroeder et al., 2005). However, the uncertainty about the availability of N from mill mud to crops means this ‘discount’ is small (e.g. <30%) relative to the total amount of N applied and so there is potential for applications of mill mud to exacerbate losses of N from fields (Thorburn et al., 2008). Still, the fate of N in mill mud applied to fields is uncertain, e.g. there seems to be some immobilisation of N in soil organic matter (Thorburn et al., 2008) and there is no information on the effect of mill mud on crop N uptake. Given this uncertainty the net impact of mill mud applications to N inputs and losses from sugarcane fields is not clear, so we do not include mill mud impacts in this analysis. 3.1.4. Estimated N Surpluses Following the above analyses, N Surpluses were derived from the difference between N applied and N in crop off-take (Table 2). For sugarcane, N Surpluses vary between regions, from 98 kg ha−1 yr−1 in the Wet Tropics, Herbert and Burnett Mary regions, to ∼135 kg ha−1 yr−1 in the Burdekin and Mackay Whitsunday regions (Table 2). N Surpluses vary between crops, from 10 kg ha−1 yr−1 in cereal farming systems to 250 kg ha−1 yr−1 in banana production. Surpluses for the other crops, excluding of rainfed cotton, are more consistent, ranging from 94 to 142 kg ha−1 yr−1 . The regional variation in sugarcane N Surpluses is similar to this between-crop variation. 3.1.5. Catchment DIN exports under current N management practices As hypothesised above, the amount of anthropogenically derived DIN (DINa ) exported from GBR rivers increases with increasing N Surplus (Ns ) expressed relative to catchment area (Fig. 5), as described by: ln(DINa ) = 1.08 ln(Ns )–2.38 (r 2 = 0.89, P < 0.01),

(1)

One test of the accuracy of Equation 1 is to predict the value of DINa predicted the from Ns data estimated for the six regions (Table 2) against the value of DINa derived by Kroon et al. (2012). The predicted value of DINa is 19% less than the derived value. We consider this difference reasonable given the underlying assumptions and uncertainties in estimates of both DINa (Kroon et al., 2012) and Ns .

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generating runoff, (2) the steepness of the terrain, and (3) the soil erodability (Wischmeier and Smith, 1978; Rosewell, 1993). The extent of grazing relative to other land uses is a further variable between regions (Table 3). The dependence of erosion on environmental factors is in contrast to DIN loss where cropping practice represented by fertiliser application or N Surplus is a strong driver of regional DIN export rates (Fig. 5). Therefore, we instead quantify the impact of grazing practices on fine sediment loss by considering how changes in vegetation cover affect each erosion process, rather than by assuming that erosion processes in one catchment can be made to replicate those in another.

Fig. 6. Anthropogenic sediment exported from rivers in six Great Barrier Reef regions (CY: Cape York, WT + H: Wet Tropics and Herbert, B: Burdekin, MW: Mackay Whitsunday, F: Fitzroy, BM: Burnett Mary) as a function of the proportional area under grazing with <60% cover (vegetation cover Classes III and IV as defined in Section 3.2.2).

The responses of DIN loss to changed management at the fieldscale are greater than that implied by Eq. (1) (Thorburn et al., 2011a; Webster et al., in press; Biggs et al., this issue). However, this equation also accounts for in-stream processes such as denitrification which will reduce regional N exports relative to paddock scale losses. 3.2. GBR grazing management 3.2.1. Current vegetation cover and catchment fine sediment export Despite the weight of evidence that reductions in cover caused by grazing increase erosion rates at a given location (Section 2.2), there is no significant correlation across the GBR regions (i.e. the catchment-based Natural Resource Management Boards outlined above) between poor cover levels and anthropogenic fine sediment export rates (Fig. 6). The variations between regions in erosion rates and sediment yields are instead caused by underlying environmental factors including: (1) the amount and intensity of natural rainfall

3.2.2. Defining classes of vegetation cover In order to quantify how different grazing practices affect fine sediment loss, we define four classes of vegetation cover; I (80–100% cover), II (60–79%), III (40–59%) and IV (0–39%). Because the spatial variation in cover has important bearing on total sediment export, given the interaction with other environmental drivers of erosion, we defined the extent of each cover class in each region (Table 3) using mean-annual dry-season ground cover (1986–2009), from ∼25 m resolution Landsat imagery (Karfs et al., 2009). Changes in the rates of hillslope, gully and streambank erosion associated with increasing cover from each class to the next higher class were then calculated based on differences between the midpoint values of each class (90%, 70%, 50% and 20%, respectively).

3.2.3. Hillslope erosion Studies in GBR grazing lands are consistent with RUSLE relationships regarding cover. For example, Silburn et al. (2011) measured hillslope soil loss rates of 2–6 times higher in areas of <20% cover relative to areas > 40% cover (RUSLE at 10% cover is ∼5 times that at 50% cover), and Bartley et al. (2010a) measured soil loss rates of more than 40 times higher from a rilled hillslope with <10% cover than from other slopes having 35–70% cover (RUSLE at zero cover is >10 times that at 60% cover). In GBR catchments, native tussock grass species tend to provide more direct coverage of the soil surface and resistance to surface runoff for given cover levels than some introduced pasture species (McIvor et al., 1995). However, the effect of pasture type on erosion rates in GBR catchments has not been consistently or widely verified (Scanlon et al., 1996; Hawdon et al., 2008).

Table 3 Fine sediment export, contributions of hillslope, gully and streambank erosion processes to that export, and the proportion of the region grazed and in different vegetation cover classes (defined in the text) for each GBR region. Region

Cape York

Wet Tropics (including Herbert region)

Burdekin

Mackay Whitsunday

Fitzroy

Burnett Mary

Anthropogenic export (kt yr−1 )a Anthropogenic export (t km−2 yr−1 )a Total export from grazing (%)b Hillslope supply (%) Gully supply (%) Streambank supply (%) Grazing area (%)h Cover Class I (%)i Cover Class II (%) Cover Class III (%) Cover Class IV (%)

1944 45 87 NA NA NA 72 51 42 5 2

1058 49 33 55c 11c 33c 44 86 11 2 0

4142 30 86 49d 34d 16d 93 49 39 10 3

1294 143 48 85e 3e 11e 55 72 25 3 0

2850 19 91 67f 24f 9f 82 57 30 8 5

2813 53 72 52g 20g 27g 71 37 39 23 2

a b c d e f g h i

Total 14,101 33 74 59 23 18 80 52 35 10 3

Kroon et al. (2012). Compiled from Brodie et al. (2003). Hateley et al. (2006). Kinsey-Henderson et al. (2007). Rohde et al. (2006). Dougall et al. (2009). Fentie et al. (2006). QLUMP (1999; 2004). Vegetation cover classes are expressed as a proportion of grazing area.

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Table 4 Relative changes (%) in hillslope and gully erosion rates associated with improvements in vegetation cover, and in bank erosion from riparian revegetation and destocking, applied to all Great Barrier Reef regions. Relative change in erosion rate for erosion process

Cover classa change

IV–III

III–II

II–I

Hillslope erosion Gully erosion Streambank erosion reduction

−65 −35 −10

−61 −10 −30

−71 −20 −20

a Cover classes (defined in Section 3.2.2) are: I (80–100% cover), II (60–79%), III (40–59%) and IV (0–39%).

We estimated the relative changes in USLE cover factors (and hence hill slope erosion) associated with moving between adjacent cover classes using the table D3 for permanent pasture of Rosewell (1993), and neglecting canopy effects (Table 4). 3.2.4. Gully erosion Studies of gully remediation and management outside GBR catchments indicates that complete stock exclusion from gullied areas increased ground vegetation cover upslope of gullies, and on gully walls and floors, and substantially reduced annual runoff and soil loss. For example, on the Loess plateau in China (Morgan and Davidson, 1986), runoff was reduced by 50–60% and soil loss by 60–80%. Similarly, gully sediment yields were reduced by 78% in semi-arid shrub-land in Colorado, USA (Heede, 1979). Changing from grass to forest cover can also lead to large reductions in gully erosion rate. In New Zealand, establishing forests on formerly grazed areas reduced sediment from gullies by 62% (Gomez et al., 2003). Even larger reductions (75–90%) were measured when moving from cultivation to forest cover on loess soils (Morgan and Davidson, 1986; Chen and Cai, 2006). No data are available within GBR catchments on the effectiveness of increased vegetation cover on gully sediment yield. However, several studies have quantified the effect of cover on hillslope runoff in the Burdekin and Fitzroy regions. Runoff was 50–90% less from sites with 40–50% ground cover, compared with sites with 10% ground cover (Owens et al., 2003; Bartley et al., 2006; Hawdon et al., 2008; Silburn et al., 2011). Based on these studies, we have derived reductions in gully sediment yield resulting from increasing cover from one class to the next in GBR catchments (Table 4). The magnitude of these reductions is smaller than the mid-range of previous studies. However, we think this is reasonable considering that it is not feasible to fence out or reforest all gullies while maintaining beef production in extensive grazing lands of the GBR. 3.2.5. Streambank erosion There have been no studies of the effectiveness of riparian management within GBR grazing lands. In temperate USA, bank retreat rates reduced by 36–87% following exclusion of livestock from grassed riparian zones, and by 59–91% following establishment of riparian forest buffers (Zaimes et al., 2008). There may have been a number of processes giving reduced bank retreat rates after livestock exclusion in this study. However, we assume that better livestock management in riparian areas is associated with higher landscape cover levels. In the Burdekin, Fitzroy and Burnett Mary regions which dominate the overall fine sediment export to the GBR (Table 3), the extent of property fencing influences both hillslope cover, by allowing better distribution of grazing pressure, and riparian management; by enabling stock exclusion or rotational grazing. Livestock management in riparian areas is less-well predicted by landscape cover levels in the wetter coastal regions (Wet Tropics

and Mackay Whitsunday), where the natural baseline for riparian management is complete tree cover. We used the reductions in bank retreat rates found by Zaimes et al. (2008) to inform our estimates of the change in streambank erosion rates associated with improving from each cover class to the next-higher cover class (Table 4). As for gully erosion, the magnitude of reductions expected in GBR catchments were smaller than results elsewhere. This accounts for conservatively managed rotational grazing of riparian zones continuing even in Class I areas of GBR catchments. Rotational grazing of riparian zones results in smaller reductions in erosion rates than total grazing exclusion, depending on rotation intensity (Lyons et al., 2000; Zaimes et al., 2008). Rotation timing is not explicitly represented in the estimates, but we assume that rotational grazing of riparian zones would generally include wet-season spelling, since impacts of grazing on streambank erosion are more pronounced when soil moisture is elevated (Trimble and Mendel, 1995). 4. Estimating the effect of improved management scenarios In this section we use the concepts and relationships developed above to assess the possible water quality improvements that may results from adoption of improved management practices. We first derive scenarios of improved management, then estimate the outcomes of these scenarios with the relationships developed above. 4.1. Derivation of improved management practice scenarios 4.1.1. Reduced N fertiliser inputs to cropping land 4.1.1.1. Approach. To examine the potential for reducing DIN exports to the GBR we first define scenarios of improved management practices that will reduce N Surpluses. Below we review current ‘Best Management Practices’ (BMP) for major crops in the GBR. In an agricultural management sense, these BMP are analogous to Class II vegetation cover defined for grazing above (Section 3.1.2.1). For crops with no clear BMP, we propose possible BMP based on studies of N management for that crop. Then we compare the resultant reductions in DIN predicted from Eq. (1) for complete adoption of BMP against policy targets for GRB water quality. There are studies both in the GBR (Armour et al., 2009) and more generally (Johnson et al., 2002; Beaudoin et al., 2005; Derby et al., 2009) that indicate that BMP will not meet water quality objectives. It is common for BMP for N (and other nutrients) to be aligned to potential or target crop yields, to ensure maximum production. However, when farmers’ actual yields are less than potential, as is common, there is a relative over-application of N (and other nutrients). This over-application of N is one of the reasons why BMP may not achieve desired environmental outcomes (Johnson et al., 2002; Beaudoin et al., 2005; Derby et al., 2009). This situation is clearly exemplified in the BMP for sugarcane in the GBR. Potential yield targets in the BMP are considerably higher than the actual yields achieved by farmers (Thorburn et al., 2011b). Thus there is a need to consider different management practices, which we will call Agri-Environmental Practices (AEP; after Beaudoin et al., 2005), more likely to meet environmental outcomes, while maintaining productivity–management systems. AEP would be analogous to Class I vegetation cover defined for grazing. Below, we propose AEP for major crops in the GBR and compare the resultant reductions in DIN predicted from Equation 1 for complete adoption of AEP. 4.1.1.2. Best Management Practices. There exist a number of established or potential BMP for N management in GBR cropping systems that would reduce N applications, N Surpluses, and so should improve water quality without reducing crop yields. These improved management systems are most developed for sugarcane,

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Table 5 Rates of N fertiliser application and N Surplus (both kg ha−1 yr−1 ) and the reduction in N application (%) compared with current practice (Table 2 for two scenarios of reduced N fertiliser application; Best Management Practice and Agri-Environmental Practices, defined in the text. Crop

Best Management Practices

Agri-Environmental Practices

N fertiliser applied

Reduction (%)

N Surplus

N fertiliser applied

Reduction (%)

N Surplus

Sugarcane (2004-2008) Wet Tropics Herbert River Burdekin Mackay Whitsunday Burnett Mary

140 140 180 150 138

0 6 15 11 0

98 97 109 109 98

82 85 139 81 79

42 43 35 52 43

40 42 68 40 39

Tree crops Banana Other tree crops

260 120

10 10

185 64

125 106

62 29

50 50

Small crops Capsicum and tomatoes

173

10

53

170

21

50

Cotton Irrigated Rainfed

212 88

10 10

79 42

183 88

10 31

50 42

Grains Cereal farming systems

100

0

10

100

0

10

for which the currently promoted BMP is based on regional potential yields and potential soil N mineralisation (Schroeder et al., 2005). It has replaced simpler, industry-wide more uniform Nfertiliser recommendations. The BMP results in a reduction of N applications up to 15% compared with recent practice (Table 5), although it should be noted that N applications in the Wet Tropics and Burnett Mary regions are already similar to the BMP (i.e. below the previous recommendations). While N fertiliser recommendation systems are not as well developed for the other major crops growing in the GBR, the substantial N Surpluses (Table 2) suggest that there is potential to reduce N fertiliser inputs below those currently practiced for these crops, except grains. There is practical experience supporting this conclusion in bananas and cotton. For bananas, ‘best bet’ N management in Wet Tropics has been considered application of ∼250 kg N ha−1 (Prove et al., 1997), compared with common applications >300 kg ha−1 (Table 2). Further, banana yields may not be significantly reduced with N applications as low as 100 kg ha−1 (Armour et al., this issue). For cotton, fertiliser N applied in the Fitzroy region has been found up to 130 kg ha−1 in excess of requirements (Rochester et al., 2009). One of the first steps in developing improved fertiliser management practices is to consider N needs of crops at some target yield and N from sources other than fertiliser, e.g. soil N, decomposing organic matter and/or crop residues. The BMP developments in sugarcane reflect early steps along this path. A similar approach has recently been advocated for cotton (Rochester, 2010) that would result in up to 20% reductions in recommended N applications. Thus, we assume that following new BMPs for other crops (excluding grains) in the GBR is likely to result in 10% lower N fertiliser rates (Table 5). Current N Surpluses in grains are estimated to be small (Table 2), consistent with the conclusion that lack of N is one of the major production constraints (apart from limited water) in rainfed grains in Australia (Hochman et al., 2009). Thus, we have assumed there is no potential for reducing N fertiliser applications in grains without affecting production. 4.1.1.3. Agri-Environmental Practices. Explicit AEP have not previously been derived for crops grown in the GBR. However, an N fertiliser management system, called the N Replacement system (Thorburn et al., 2011b), developed for sugarcane could serve as an AEP for that crop. This system is based on an input-output balance

approach, where N fertiliser inputs equal estimated removals of N in crop off-take and an allowance for environmental losses. Thus N inputs are aligned with actual yields rather than potential. In fieldscale experiments, the N Replacement system resulted in 15% lower N inputs compared with the sugarcane BMP and, counter intuitively, a small increase in long-term production (Thorburn et al., 2011b). Possible AEP for other crops are less clear. However, the general concept underpinning N Replacement – aligning N inputs with crop off-take plus some necessary surplus – provides a basis for AEP. We have assumed that an N Surplus of 50 kg ha−1 will be adequate to maintain crop yields, and so the N fertiliser applications under the AEP would be equivalent to average crop N off-take (Table 2) plus 50 kg N ha−1 (Table 5). For rainfed cotton, N Surpluses for the BMP were <50 kg ha−1 , so we have assumed N applications in the AEP the same as the BMP. As for BMP, we have assumed there is no potential for reducing N fertiliser applications in grains without affecting production.

4.1.2. Increased vegetation cover in grazing lands 4.1.2.1. Approach. To examine the potential for reducing fine sediment exports to the GBR we first define scenarios of improved management practices that will reduce increase ground cover. The approach is, based on increasing the proportion of each region in cover classes with higher levels of ground cover (Table 6). For each scenario (e.g. changing an area having Class III cover to Class II) the change in fine sediment export yield (YIII→II , t yr−1 ) was estimated by summing across hillslope, gully and bank erosion (EH , EG , EB , respectively) the product of: (1) the proportional change in the erosion process at paddock-scale (i.e. E/E) as given by Table 4, (2) the area of the grazing cover class being changed (e.g. AIII ) as a proportion of region area (A), and (3) the contribution of each erosion rate to current catchment fine sediment export, Ci (Table 3), as described by: YIII→II =



 i=HGB

Ei(III→II) AIII C Ei(III) A i

 (2)

The reduction in fine sediment export was computed for each region using Eq. (2), and then summed across the six GBR regions. The estimated changes in export were then expressed as a percentage of the baseline anthropogenic export. All erosion processes

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Table 6 Definition of the grazing management practice scenarios: changes in cover classes, regions included, areas of GBR catchments affected and the Source of information about erosion processes. Scenario

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Increase cover to Class II Increase cover to Class III Five (not CY) 12% Table 3

Increase cover to Class I Increase cover to Class II Increase cover to Class III Five (not CY) 43% Table 3

Increase cover to Class I Increase cover to Class II Increase cover to Class III Six 48% Table 3

Increase cover to Class I Increase cover to Class II Increase cover to Class III Six 48% Sediment tracing

Cover Class I Cover Class II Cover Class III

Increase cover to Class II

Cover Class IV GBR regions included Coverage of GBR grazing area Source of information about erosion processesb a b

Five (not CYa ) 9% Table 3

Cape York. Erosion process contributions (i.e. the value of Ci ) in Eq. (2).

were included in each scenario, since that is the most efficient strategy for reducing sediment exports (Lu et al., 2004). The above approach assumes that proportional changes in paddock sediment loss in the different practice change scenarios translate directly to proportional changes in catchment export. In other words, the efficiency of pollutant delivery from paddockscale to the catchment outlet is unaffected by practice change. This assumption is supported by the tight relationship between current regional mean-annual fine sediment exports (Kroon et al., 2012) and the amount of fine sediment delivered to streams in each region per unit area (Table 3; Fig. 7), which indicates that the delivery ratio is similar between regions under current conditions. The assumption is reasonable unless practice change is not uniform, e.g. being focused on areas which are much more (or less) connected to the catchment outlet than the catchment-average delivery ratio. We think the assumption of uniform practice change is reasonable in GBR catchments (as discussed below). 4.1.2.2. Improved management scenarios. The scenarios involved increasing vegetation cover in the regions (Table 6) through progressively decreasing the proportions of the regions with vegetation cover in classes with low cover (i.e. Class IV and/or III) and correspondingly increasing the proportion with higher cover (Class II and/or Class I). The cover class changes were applied to five of the six regions (excluding Cape York) in Scenarios 1–3, and all six regions in Scenario 4. Scenario 5 had the same management changes as Scenario 4, but took the contributions of erosion processes to fine sediment export from tracing studies (Hughes et al.,

Fig. 7. Sediment exported from rivers in six Great Barrier Reef regions (point labels as per Fig. 6) as a function of total erosion rate to streams in six Great Barrier Reef regions. The solid line is the regression.

2009; Wilkinson et al., in press) rather than Table 3. Scenarios 4 and 5 involved improving cover levels over all areas with mean-annual dry-season cover currently less than 80%, and so involved changing cover over 48% of the grazing area in GBR regions. The changes in cover affected hillslope and gully erosion throughout each cover class. Changes to streambank management were applied in these areas that currently did not have tree cover, with these areas defined as areas with foliage project cover <20% (measured by the Landsat imagery; Karfs et al., 2009). Under Scenarios 4 and 5, more than 80% of grazing land would have cover levels >80%, levels that have been shown to improve land condition, in terms of pasture production, species composition and soil hydrological function (McIvor, 2001; Roth, 2004; Ash et al., 2011). Establishing the pasture utilisation rates required to achieve these cover levels will require testing under local conditions. Available data from GBR catchments indicate that utilisation rates up to 25–30% will maintain or improve pasture composition and land condition (Orr et al., 2010; Ash et al., 2011), benefiting both water quality and pasture productivity (McIvor et al., 1995). In contrast, historical rates of utilisation have exceeded 50% in some areas during dry periods, causing replacement of native pasture species by exotic annuals (McKeon et al., 1990, 2004). Such land degradation reduces economic profitability over multi-year time-scales (McKeon et al., 1990, 2004). All scenarios implied relatively small extents of grazing practice change in Wet Tropics and Mackay Whitsunday regions due to the relatively higher cover levels and smaller proportions of area under grazing land use in these regions (Table 3). Additional improvements in riparian management in both Class I grazing areas and non-grazing areas of these regions may be worthwhile considering the high specific erosion rates in these regions (Table 3). However, improved riparian management is not likely to deliver large additional fine sediment export reductions, given the small riparian areas in these regions relative to all GBR catchments, and so these management improvements are not considered here. Cape York was excluded from Scenarios 1–3, since implementation of practice changes in that region has not progressed so far to date. A 10% reduction in erosion was applied in Cape York (Scenarios 4 and 5), since data on the contribution of erosion processes were unavailable there; this is a smaller reduction than the other regions except for Wet Tropics. Scenario 5 tested the sensitivity of the estimated export reductions to the assumed proportional contributions of each erosion process. Recent sediment tracing studies show that erosion of subsoil (gully erosion is the likely largest source) is more important in grazing areas than previously thought, contributing >75% of sediment to the river network in intensively gullied areas (Hughes et al., 2009; Wilkinson et al., in press). For Scenario 5, the contribution of

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Fig. 8. Estimated export of anthropogenic (a) dissolved inorganic N (DIN) and (b) total N (a proportion (%) of that estimated for current management) resulting from universal adoption in cropping across GBR catchments of different management practices scenarios: for (a) Best Management Practices (BMP) and Agri-Environmental Practices (AEP), described in Section 4.1.1; and for (b) Scenario B, a combination of BMP for cropping and Scenario 5 for grazing, and Scenario A, a combination of AMP for cropping and Scenario 4 for grazing, described in Section 5.2 (horizontal lines indicate targets implied by government water quality policy).

erosion processes in the Burdekin and Fitzroy were estimated as 30% hillslope, 40% gully and 30% streambank erosion, and in the Mackay Whitsunday region the Wet Tropics contributions were applied (from Table 3), since the Wet Tropics proportions are supported by sediment tracing results (Hateley, 2007), and these regions have most similar climates.

4.2. Outcomes of improved management scenarios on pollutant exports 4.2.1. N management practices and DIN exports Current policies for improving water quality in the GBR have no explicit target for DIN reduction (Anon, 2009). However, they do have a target of halving anthropogenic ‘total nitrogen’ discharged from GBR catchments, which we take to imply a need to halve the export of anthropogenic DIN from rivers in the regions. Applying the N fertiliser applications and N Surpluses derived for BMP into Equation 1 provides an indication of the likelihood of success in improved practices meeting these policy goals. The universal adoption of the BMP describe above will not meet the policy target. Under these assumptions, DIN will only be reduced by 12% (Fig. 8a) compared with current exports (estimated by Equation 1 for current N inputs). However, the universal adoption of AEP is predicted to exceed the policy water quality targets for DIN, with a reduction of almost 60% resulting from these practices (Fig. 8a).

4.2.2. Grazing practices and fine sediment exports The scenarios resulted in estimated reduction in total sediment exports to the GBR of between 4 and 19% of current exports (Fig. 9). Scenarios 3, 4 and 5 had larger export reductions than 1 or 2 because the area of cover Class II was much larger than that of Class III or IV in most regions (Table 3). Scenarios 3 and 4 came close to the water quality targets stipulated in Government policy (20% reduction in fine sediment exports; Anon, 2009). Scenario 5 resulted in an export reduction 4% smaller than Scenario 4. This difference is an estimate of the level of uncertainty in export responses given current knowledge, and it suggests that achieving effective reductions in gully and bank erosion rates may be more important than previously anticipated.

5. Discussion 5.1. Strengths and limitations of the approach This study demonstrates that conceptual frameworks based on empirical pollutant delivery functions can be used to evaluate water quality benefits of agricultural practice changes. The principles of the approach we employed to represent pollutant delivery were to: (i) estimate relative, rather than absolute magnitudes of change in pollutant export; (ii) base scenario estimates for reducing DIN exports on comparison of current exports between catchments or regions having different N Surpluses (Eq. (1)); (iii) base scenario estimates for reducing fine sediment exports on plot-scale cover-erosion relationships, scaled by the area involved in practice changes and the contributions of individual sources (Eq. (2)), since natural spatial variations in erosion rates made comparison of current rates between regions inappropriate for estimating future changes. The core elements of the conceptual frameworks linking agricultural land management practices to pollutant losses may be considered generic to agricultural systems and pollutants, although the frameworks themselves are specific to agricultural systems in the tropical and near-tropical GBR catchments. These core elements are, firstly, a basis on the primary biophysical processes governing

Fig. 9. Estimated export of anthropogenic fine sediment (as a % of that estimated under current management) from five different management practices scenarios described in Section 4.1.2. (horizontal line indicates the target set in government water quality policy).

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generation of agricultural pollutants and their loss to rivers, and secondly, the use of indicator metrics to quantitatively describe the first-order linkages between management practices and pollutant exports, so that scenarios of modified practices may be explored. While our approach has benefits, e.g. applicability to data-sparse regions, it also has limitations that need to be recognised. Our approach assumes spatially uniform practice change across areas that are the source of pollutants. Clearly practice changes in areas disconnected from the river mouth (e.g., upstream of a large reservoir) are relatively ineffective at reducing loads, but this is offset by practice changes downstream of a reservoir being relatively more efficient at reducing exports. The assumption of spatially uniform practice change is reasonable in this study, because recent practice changes in GBR catchments have been implemented broadly across land use areas and not biased towards areas more-connected to the river outlet. Further, landscape-scale spatial controls on pollutant delivery (reservoirs, in-stream processes) are less important in hydrologically variable environments such as the GBR where runoff is highly event-driven, as evidenced by the good fit between paddock pollutant losses and catchment exports (Figs. 5 and 7), and by the high proportion of sediment loss that is exported (McKergow et al., 2005a). Before applying the approach elsewhere, an assumption of spatially uniform practice change should be verified, particularly where large gradients in connectivity exist (e.g., associated with a reservoir), and practice changes are spatially targeted. If practice changes are strongly spatially targeted, the consequent changes in catchment exports could be represented by estimating changes to the delivery ratios based on known differences in connectivity between targeted areas and the catchment overall; based on estimates of dam pollutant trapping for example. Where practice changes are not spatially uniform and so conceptual frameworks may not be applicable, it may be necessary to explicitly represent the spatial context in catchment models (Arnold and Fohrer, 2005; Vigiak et al., 2011), such as those applied in the Tully-Murray catchment within the GBR (Roebeling et al., 2009; van Grieken et al., this issue) and currently being developed for all GBR catchments (Carroll et al., 2012). However, catchment models require substantial data and time for development, as already noted, especially at the spatial scales of GBR catchments (i.e. 105 –106 km2 ). It should be noted that the conceptual framework approach to estimating linkages between agricultural practices and paddock pollutant loss is equally applicable in a spatial modelling framework as it is with a delivery ratio approach, and such an approach may useful where spatial variability need to be considered but catchment models are not available. Another limitation of our approach is the implicit assumption of steady-state conditions. The timescales of response to practice changes are dependent on the physical processes governing pollutant generation and transport. For N, exports from fields vary from year-to-year (Thorburn et al., 2011a; Webster et al., in press), but losses in runoff from some GBR cropping areas can respond quickly (e.g. within one crop) to changed N inputs (Masters et al., 2008; Webster et al., in press). So our steady-state assumption may not be a significant limitation for these processes. However, where processes such as the rundown of soil organic matter overtime are substantially influencing N inputs, transient approaches may provide benefits. Sediment exports respond to increased cover slowly (Meals et al., 2010), since they depend on vegetation growth, and improvements in soil infiltration capacity and land condition (Roth, 2004; Colloff et al., 2010). So, transient approaches will be required to provide information on the time scales over which water quality improvements will result after practice changes are implemented. While the above issues make the application of more quantitative modelling approaches desirably, even where the information

is available to parameterise such approaches, knowledge limitations often prevent robust representation of transient responses in these models. For example, the timelines of recovery of soil hydrological function following implementation of improved grazing management are not well understood, and so will not be fully captured in catchment models (Bartley et al., 2010a; Colloff et al., 2010). As well, the processes governing N transport in groundwater through floodplains in humid tropical areas are not completely understood (Connor et al., this issue; Rasiah et al., this issue). Thus, simpler approaches like those developed in this study provide useful insights into agricultural management-catchment water quality relationships as more complex approaches are being developed. Perhaps a more fundamental limit is our knowledge of the social or economic responses to investing in practice change (Roebeling et al., 2009; van Grieken et al., this issue; Star et al., this issue). Spatially explicit data on agricultural practices is scarce and the likelihood of uniform adoption is small. Public investments must also consider social equity and strongly targeting some areas at the expense of others may lead to perverse behaviours in non-funded areas. 5.2. Application to other pollutants The contrasting properties of the two pollutants we have considered in this study, DIN and fine sediments, are underpinned by contrasting generation and transport behaviours and required different approaches to linking their discharge from catchments to agricultural management. For DIN, management of N inputs was the primary control measure (Fig. 3), whereas for fine sediments site conditions and past site history needed to be considered (Fig. 4) as well as current management. Thus, for pollutants with intermediate attributes, i.e. which move partly in soluble and particulate forms, a combination of the approaches we applied will be applicable. We can illustrate this combined approach by considering the management impact on another pollutant, total N (TN). Government policies that underpin investment in practice change within GBR catchments have a stated target of reducing the anthropogenic component of TN by 50% (Anon, 2009). TN is composed of DIN, N in sediments (i.e. particulate N; PN) and dissolved organic N (which we will not consider). The relative contributions of DIN and fine sediments to TN are 17% and 79%, respectively (Kroon et al., 2012), so our scenarios for DIN and fine sediment reduction can be combined to estimate the resultant reduction in TN from improved agricultural management. For simplicity, we assume that proportional reductions in PN will mirror those in sediments, which implies: (1) that similar reductions will be effected in erosion of topsoil and subsoil, given that topsoil has generally higher PN concentrations; and (2) that practice changes reducing DIN do not affect sediment N concentration. We combine the cropping BMP scenario with grazing Scenario 5 as a suite of practices that may reasonably be promoted in GBR catchments at the current time. The estimated reduction in TN from universal adoption of the practices in these scenarios is 14% (Fig. 8b). Combining more environmentally oriented practices, i.e. the AEP scenario for cropping with grazing Scenario 4, results in a 24% estimated reduction in TN. A similar approach may also be taken for pesticides, which also have part soluble phase and part particulate phase. However, a marked difference with pesticides is their degradation through time, which introduces a spatial and temporal complexity beyond the approaches as developed in this study. Approximate means of accounting for this complexity are emerging (Cook et al., this issue) that could also be incorporated into our conceptual framework approach.

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5.3. Implications for practice change priorities The results of this study show that changes in agricultural practices that reduce N inputs in cropping lands (Fig. 5) and increase cover in grazing lands (Table 4) should, over time, effectively reduce sediment and DIN exports to the GBR. For cropping, we argue that the greatest reductions in DIN exports will be achieved through activities that reduce N inputs to crops, rather than changing ‘management tactics’ to increase NUE without altering N inputs (Fig. 3). This conclusion is in line with European regulations that limit N applications through fertiliser or manure on farms (Anon, 2002). Our analysis suggests the change in N management required to meet the implied ‘Reef Plan’ policy target of 50% reduction in DIN export (Anon, 2009) is complete adoption of AEP (Fig. 8a), involving reductions in N fertiliser applications of 10–60%, depending on the crop. Total adoption of BMP is predicted to produce benefits and so should be encouraged, but is unlikely to meet the implied targets for DIN. Yet a large proportion of the government incentive payments directed towards N management has historically been directed to adoption of ‘management tactics’ (Fig. 3) and promotion of BMP (Thorburn et al., 2011c). This study suggests that testing the biophysical feasibility and economic viability benefit of AEP on relevant crops in GBR environments is a priority to provide the confidence on which to base widespread on-ground action. There is some experience that AEP may be sustainable in sugarcane and, with additional refinement, they may become the basis for a practical N management recommendation system (Thorburn et al., 2011b). However, the AEP defined here for other crops are quite conceptual and would need considerable testing and development before they could be recommended. Unlike the situation for DIN or fine sediments, for TN we estimate that improvements in water quality targeted in government policies will not be met (Fig. 8b). The result comes about because PN in fine sediment is a substantial proportion of TN. However, sediment exports will only be reduced by ∼20% in the scenarios examined (Fig. 9), but policy targets for TN are a 50% reduction. There may be some merit in reconsidering the policy targets for N reduction given the different management (Fig. 8) and ecological (Schaffelke, 1999) impacts of DIN and TN. The changes in grazing practices required to achieve ‘Reef Plan’ policy targets for fine sediment exports (20% reduction; Anon, 2009) in the long term would be most closely met by Scenario 4 (Fig. 9). Achieving this scenario would require an average 20% increase in mean-annual dry-season cover over areas currently in Class II, III or IV cover (i.e. where mean-annual dry-season cover is < 80%); this represents approximately half of the total grazed area in GBR catchments. However, further testing of erosion responses to practice change is also a high priority. The magnitudes of cover change represented in the grazing scenarios are larger than those previously produced in experiments on improved pasture management within GBR catchments. For example, we estimated that shifting from Class III to IV land condition would result in a 61% reduction in hillslope erosion rate (Table 4). In contrast, 5 years of reduced pasture utilisation increased cover from 37% to 45% (Bartley et al., 2010b), equating to 30% reduction in hillslope erosion rate estimated from the USLE model. Thus, it is not known what pasture utilisation rates would be required to achieve the predicted cover levels, or the length of time required to realise the full benefits of better management (Meals et al., 2010). These are substantial knowledge gaps. A large proportion of government incentive payments have been directed to paddock subdivision (Thorburn et al., 2011c), through activities such as increased fencing and improved location of stock watering and feeding points. In a similar way to fertiliser management tactics having little impact on DIN loss without being accompanied by reductions in N inputs, paddock

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subdivision will not substantially increase biomass production relative to continuous grazing unless it is accompanied by reductions in long-term pasture utilisation (Briske et al., 2008), even though wet-season pasture spelling can assist recovery of pasture composition (Ash and Corfield, 1998; Ash et al., 2011). Further research is also required to verify the effect of paddock subdivision and improved location of stock watering and feeding points on erosion in erosion-vulnerable and livestock-preferred areas (e.g. Hunt et al., 2007). 6. Conclusions We show that use of conceptual frameworks based on simple delivery functions can be useful for relating improvements in agricultural management practices to reductions in catchment exports of a range of water-borne pollutants. Our approach provides a methodology for use where resources are not available to undertake more intensive quantitative and spatially- and temporally detailed analyses. Application of the frameworks provides a clear focus on the field-scale processes linking agricultural practices to off-site pollutant losses, resulting in rapid and defensible estimates of the effectiveness of practice changes to inform land holder efforts and government investments to improve water quality. Applying this approach, we show that agricultural practices primarily required to effectively reduce exports of the pollutants DIN and fine sediment to the GBR are a reduction in N Surplus in cropping lands and increase in vegetation cover in grazing lands, respectively. Meeting government targets (stated or implied) for DIN, fine sediment and TN exports to the GBR will require substantial adoption of new management practices, some of which are outside current experience in GBR catchments. Thus, additional development and testing of agricultural mitigation practices will be required if these policy targets are to be achieved. Acknowledgements We would like to thank two anonymous reviewers for their constructive comments on this paper and Dr Ian Rochester (CSIRO) for his advice about nitrogen agronomy of cotton. Brett Abbott (CSIRO) calculated the land use and land cover statistics by region. Financial support was provided by the Reef Rescue component of the Australian Government Caring for Our Country program and the CSIRO Water for a Healthy Country National Research Flagship. References Access Economics, 2007. Economic and financial value of the Great Barrier Reef Marine Park, 2005–06. Report for Great Barrier Reef Marine Park Authority, Townsville. Anon, 2002. Implementation of Council Directive 91/676/EEC concerning the protection of waters against pollution caused by nitrates from agricultural sources: synthesis from year 2000. Member States reports. European Commission, Luxembourg. Anon, 2003. Reef Water Quality Protection Plan: For Catchments Adjacent to the Great Barrier Reef World Heritage Area. The State of Queensland, Department of Premier and Cabinet, Brisbane. Anon, 2009. Reef Water Quality Protection Plan: For Catchments Adjacent to the Great Barrier Reef World Heritage Area. Queensland Government, Department of Premier and Cabinet, Brisbane. Anon, 2010. Australian Sugar Industry Year Book. Rural Press, Ormiston. Armour, J.D., Nelson, P.N., Daniells, J.W., Rasiah, V., Inman-Bamber, N.G., this issue. Nitrogen leaching from the root zone of sugarcane and banana production in the humid tropics of Australia. Agric. Ecosys. Environ. Armour, J.D., Hateley, L.R., Pitt, G.L., 2009. Catchment modelling of sediment, nitrogen and phosphorus nutrient loads with SedNet/ANNEX in the Tully–Murray basin. Mar. Freshwater Res. 60, 1091–1096. Arnold, J.G., Fohrer, N., 2005. SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol. Proc. 19, 563–572. Ash, A.J., Corfield, J.P., 1998. Influence of pasture condition on plant selection patterns by cattle: its implications for vegetation change in a monsoon tallgrass rangeland. Trop. Grasslands 32, 178–187.

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Please cite this article in press as: Thorburn, P.J., Wilkinson, S.N., Conceptual frameworks for estimating the water quality benefits of improved agricultural management practices in large catchments. Agric. Ecosyst. Environ. (2012), doi:10.1016/j.agee.2011.12.021