Assessing water quality for cropping management practices: A new approach for dissolved inorganic nitrogen discharged to the Great Barrier Reef.

Applications of nitrogen (N) fertiliser to agricultural lands impact many marine and aquatic ecosystems, and improved N fertiliser management is needed to reduce these water quality impacts. Government policies need information on water quality and risk associated with improved practices to evaluate the benefits of their adoption. Policies protecting Great Barrier Reef (GBR) ecosystems are an example of this situation. We developed a simple metric for assessing the risk of N discharge from sugarcane cropping, the biggest contributor of dissolved inorganic N to the GBR. The metric, termed NiLRI, is the ratio of N fertiliser applied to crops and the cane yield achieved (i.e. kg N (t cane)-1). We defined seven classes of water quality risk using NiLRI values derived from first principles reasoning. NiLRI values calculated from (1) results of historical field experiments and (2) survey data on the management of 170,177 ha (or 53%) of commercial sugarcane cropping were compared to the classes. The NiLRI values in both the experiments and commercial crops fell into all seven classes, showing that the classes were both biophysically sensible (c.f. the experiments) and relevant to farmers' experience. We then used machine learning to explore the association between crop management practices recorded in the surveys and associated NiLRI values. Practices that most influenced NiLRI values had little apparent direct impact on N management. They included improving fallow management and reducing tillage and compaction, practices that have been promoted for production rather than N discharge benefits. The study not only provides a metric for the change in N water quality risk resulting from adoption of improved practices, it also gives the first clear empirical evidence of the agronomic practices that could be promoted to reduce water quality risk while maintaining or improving yields of sugarcane crops grown in catchments adjacent to the GBR. Our approach has relevance to assessing the environmental risk of N fertiliser management in other countries and cropping systems.

The changing face of science communication, technology, extension and improved decision-making at the farm-water quality interface.

In recent decades, significant advances have been made in understanding the generation, fates and consequences of water quality pollutants in the Great Barrier Reef ecosystem. However, skepticism and lack of trust in water quality science by farming stakeholders has emerged as a significant challenge. The ongoing failures of both compulsory and particularly voluntary practices to improve land management and reduce diffuse agricultural pollution from the Great Barrier Reef catchment underlines the need for more effective communication of water quality issues at appropriate decision-making scales to landholders. Using recent Great Barrier Reef catchment experiences as examples, we highlight several emerging themes and opportunities in using technology to better communicate land use-water quality impacts and delivery of actionable knowledge to farmers, specifically supporting decision-making, behavior change, and the spatial identification of nutrient generation 'hotspots' in intensive agriculture catchments. We also make recommendations for co-designed monitoring-extension platforms involving farmers, governments, researchers, and related agencies, to cut across stakeholder skepticism, and achieve desired water quality and ecosystem outcomes.

Can seasonal soil N mineralisation trends be leveraged to enhance pasture growth?

BACKGROUND: Soil N mineralisation is the process by which organic N is converted into plant-available forms, while soil N immobilisation is the transformation of inorganic soil N into organic matter and microbial biomass, thereafter becoming bio-unavailable to plants. Mechanistic models can be used to explore the contribution of mineralised or immobilised N to pasture growth through simulation of plant, soil and environment interactions driven by management. PURPOSE: Our objectives were (1) to compare the performance of three agro-ecosystems models (APSIM, DayCent and DairyMod) in simulating soil N, pasture biomass and soil water using the same experimental data in three diverse environments (2), to determine if tactical application of N fertiliser in different seasons could be used to leverage seasonal trends in N mineralisation to influence pasture growth and (3), to explore the sensitivity of N mineralisation to changes in N fertilisation, cutting frequency and irrigation rate. KEY RESULTS: Despite considerable variation in model sophistication, no model consistently outperformed the other models with respect to simulation of soil N, shoot biomass or soil water. Differences in the accuracy of simulated soil NH4 and NO3 were greater between sites than between models and overall, all models simulated cumulative N2O well. While tactical N application had immediate effects on NO3, NH4, N mineralisation and pasture growth, no long-term relationship between mineralisation and pasture growth could be discerned. It was also shown that N mineralisation of DayCent was more sensitive to N fertiliser and cutting frequency compared with the other models. MAJOR CONCLUSIONS: Our results suggest that while superfluous N fertilisation generally stimulates immobilisation and a pulse of N2O emissions, subsequent effects through N mineralisation/immobilisation effects on pasture growth are variable. We suggest that further controlled environment soil incubation research may help separate successive and overlapping cycles of mineralisation and immobilisation that make it difficult to diagnose long-term implications for (and associations with) pasture growth.

Grasping at digitalisation: turning imagination into fact in the sugarcane farming community.

Nutrient runoff from catchments that drain into the Great Barrier Reef (GBR) is a significant source of stress for this World Heritage Area. An alliance of collaborative on-ground water quality monitoring (Project 25) and technologically driven digital application development (Digiscape GBR) projects were formulated to provide data that highlighted the contribution of a network of Australian sugar cane farmers, amongst other sources, to nutrient runoff. This environmental data and subsequent information were extended to the farming community through scientist-led feedback sessions and the development of specialised digital technology (1622™WQ) that help build an understanding of the nutrient movements, in this case nitrogen, such that farmers might think about and eventually act to alter their fertilizer application practices. This paper reflects on a socio-environmental sustainability challenge that emerged during this case study, by utilising the nascent concept of digi-grasping. We highlight the importance of the entire agricultural knowledge and advice network being part of an innovation journey to increase the utility of digital agricultural technologies developed to increase overall sustainability. We develop the digi-MAST analytical framework, which explores modes of being and doing in the digital world, ranging from 'the everyday mystery of the digital world (M)', through digital 'awareness (A)', digitally 'sparked' being/s (S), and finally the ability of individuals and/or groups to 'transform (T)' utilising digital technologies and human imaginations. Our digi-MAST framework allows us to compare agricultural actors, in this case, to understand present modes of digi-grasping to help determine the resources and actions likely to be required to achieve impact from the development of various forms of digital technological research outputs.

The nitrogen nutrition potential of arable soils.

Soils are an important source of nitrogen in many of the world's cropping systems. Especially in low-input production systems, nitrogen release from soil organic matter turn-over is the major part of the crop's nitrogen supply and research suggests that this process is significantly affected by changes in climate. The knowledge of the amount of nitrogen being accountable for crop nutrition is purely empirical in many production areas in the world and data as a foundation of global-scale climate change and food security assessments is scarce. Here we demonstrate that nitrogen mineralisation in general follows similar rules as for carbon, but with different implications for agricultural systems. We analysed 340 data sets from previously published incubation experiments for potential nitrogen mineralisation which covered a large range of soils and climate conditions. We find that under warm and all-year humid conditions the share of potentially mineralisable nitrogen in the soil's total nitrogen is significantly smaller than in dry or temperate environments. We conclude that - despite relatively high soil nitrogen stocks - soil-borne nitrogen supply for crop production is very low in tropical and humid subtropical environments, which is a critical piece of information for global assessments of agricultural production and food security.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Climate change impact and adaptation for wheat protein.

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha-1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Prioritizing Crop Management to Increase Nitrogen Use Efficiency in Australian Sugarcane Crops.

Sugarcane production relies on the application of large amounts of nitrogen (N) fertilizer. However, application of N in excess of crop needs can lead to loss of N to the environment, which can negatively impact ecosystems. This is of particular concern in Australia where the majority of sugarcane is grown within catchments that drain directly into the World Heritage listed Great Barrier Reef Marine Park. Multiple factors that impact crop yield and N inputs of sugarcane production systems can affect N use efficiency (NUE), yet the efficacy many of these factors have not been examined in detail. We undertook an extensive simulation analysis of NUE in Australian sugarcane production systems to investigate (1) the impacts of climate on factors determining NUE, (2) the range and drivers of NUE, and (3) regional variation in sugarcane N requirements. We found that the interactions between climate, soils, and management produced a wide range of simulated NUE, ranging from ∼0.3 Mg cane (kg N)-1, where yields were low (i.e., <50 Mg ha-1) and N inputs were high, to >5 Mg cane (kg N)-1 in plant crops where yields were high and N inputs low. Of the management practices simulated (N fertilizer rate, timing, and splitting; fallow management; tillage intensity; and in-field traffic management), the only practice that significantly influenced NUE in ratoon crops was N fertilizer application rate. N rate also influenced NUE in plant crops together with the management of the preceding fallow. In addition, there is regional variation in N fertilizer requirement that could make N fertilizer recommendations more specific. While our results show that complex interrelationships exist between climate, crop growth, N fertilizer rates and N losses to the environment, they highlight the priority that should be placed on optimizing N application rate and fallow management to improve NUE in Australian sugarcane production systems. New initiatives in seasonal climate forecasting, decisions support systems and enhanced efficiency fertilizers have potential for making N fertilizer management more site specific, an action that should facilitate increased NUE.

Nitrogen Cycling from Increased Soil Organic Carbon Contributes Both Positively and Negatively to Ecosystem Services in Wheat Agro-Ecosystems.

Soil organic carbon (SOC) is an important and manageable property of soils that impacts on multiple ecosystem services through its effect on soil processes such as nitrogen (N) cycling and soil physical properties. There is considerable interest in increasing SOC concentration in agro-ecosystems worldwide. In some agro-ecosystems, increased SOC has been found to enhance the provision of ecosystem services such as the provision of food. However, increased SOC may increase the environmental footprint of some agro-ecosystems, for example by increasing nitrous oxide emissions. Given this uncertainty, progress is needed in quantifying the impact of increased SOC concentration on agro-ecosystems. Increased SOC concentration affects both N cycling and soil physical properties (i.e., water holding capacity). Thus, the aim of this study was to quantify the contribution, both positive and negative, of increased SOC concentration on ecosystem services provided by wheat agro-ecosystems. We used the Agricultural Production Systems sIMulator (APSIM) to represent the effect of increased SOC concentration on N cycling and soil physical properties, and used model outputs as proxies for multiple ecosystem services from wheat production agro-ecosystems at seven locations around the world. Under increased SOC, we found that N cycling had a larger effect on a range of ecosystem services (food provision, filtering of N, and nitrous oxide regulation) than soil physical properties. We predicted that food provision in these agro-ecosystems could be significantly increased by increased SOC concentration when N supply is limiting. Conversely, we predicted no significant benefit to food production from increasing SOC when soil N supply (from fertiliser and soil N stocks) is not limiting. The effect of increasing SOC on N cycling also led to significantly higher nitrous oxide emissions, although the relative increase was small. We also found that N losses via deep drainage were minimally affected by increased SOC in the dryland agro-ecosystems studied, but increased in the irrigated agro-ecosystem. Therefore, we show that under increased SOC concentration, N cycling contributes both positively and negatively to ecosystem services depending on supply, while the effects on soil physical properties are negligible.

Responses to atmospheric CO2 concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development.

Elevated atmospheric CO2 concentrations ([CO2 ]) cause direct changes in crop physiological processes (e.g. photosynthesis and stomatal conductance). To represent these CO2 responses, commonly used crop simulation models have been amended, using simple and semicomplex representations of the processes involved. Yet, there is no standard approach to and often poor documentation of these developments. This study used a bottom-up approach (starting with the APSIM framework as case study) to evaluate modelled responses in a consortium of commonly used crop models and illuminate whether variation in responses reflects true uncertainty in our understanding compared to arbitrary choices of model developers. Diversity in simulated CO2 responses and limited validation were common among models, both within the APSIM framework and more generally. Whereas production responses show some consistency up to moderately high [CO2 ] (around 700 ppm), transpiration and stomatal responses vary more widely in nature and magnitude (e.g. a decrease in stomatal conductance varying between 35% and 90% among models was found for [CO2 ] doubling to 700 ppm). Most notably, nitrogen responses were found to be included in few crop models despite being commonly observed and critical for the simulation of photosynthetic acclimation, crop nutritional quality and carbon allocation. We suggest harmonization and consideration of more mechanistic concepts in particular subroutines, for example, for the simulation of N dynamics, as a way to improve our predictive understanding of CO2 responses and capture secondary processes. Intercomparison studies could assist in this aim, provided that they go beyond simple output comparison and explicitly identify the representations and assumptions that are causal for intermodel differences. Additionally, validation and proper documentation of the representation of CO2 responses within models should be prioritized.

An AgMIP framework for improved agricultural representation in IAMs.

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

Long Term Sugarcane Crop Residue Retention Offers Limited Potential to Reduce Nitrogen Fertilizer Rates in Australian Wet Tropical Environments.

The warming of world climate systems is driving interest in the mitigation of greenhouse gas (GHG) emissions. In the agricultural sector, practices that mitigate GHG emissions include those that (1) reduce emissions [e.g., those that reduce nitrous oxide (N2O) emissions by avoiding excess nitrogen (N) fertilizer application], and (2) increase soil organic carbon (SOC) stocks (e.g., by retaining instead of burning crop residues). Sugarcane is a globally important crop that can have substantial inputs of N fertilizer and which produces large amounts of crop residues ('trash'). Management of N fertilizer and trash affects soil carbon and nitrogen cycling, and hence GHG emissions. Trash has historically been burned at harvest, but increasingly is being retained on the soil surface as a 'trash blanket' in many countries. The potential for trash retention to alter N fertilizer requirements and sequester SOC was investigated in this study. The APSIM model was calibrated with data from field and laboratory studies of trash decomposition in the wet tropics of northern Australia. APSIM was then validated against four independent data sets, before simulating location × soil × fertilizer × trash management scenarios. Soil carbon increased in trash blanketed soils relative to SOC in soils with burnt trash. However, further increases in SOC for the study region may be limited because the SOC in trash blanketed soils could be approaching equilibrium; future GHG mitigation efforts in this region should therefore focus on N fertilizer management. Simulated N fertilizer rates were able to be reduced from conventional rates regardless of trash management, because of low yield potential in the wet tropics. For crops subjected to continuous trash blanketing, there was substantial immobilization of N in decomposing trash so conventional N fertilizer rates were required for up to 24 years after trash blanketing commenced. After this period, there was potential to reduce N fertilizer rates for crops when trash was retained (≤20 kg N ha(-1) per plant or ratoon crop) while maintaining ≥95% of maximum yields. While these savings in N fertilizer use were modest at the field scale, they were potentially important when aggregated at the regional level.

Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia.

Carbon sequestration in agricultural soils has the capacity to mitigate greenhouse gas emissions, as well as to improve soil biological, physical, and chemical properties. The review of literature pertaining to soil organic carbon (SOC) dynamics within Australian grain farming systems does not enable us to conclude on the best farming practices to increase or maintain SOC for a specific combination of soil and climate. This study aimed to further explore the complex interactions of soil, climate, and farming practices on SOC. We undertook a modeling study with the Agricultural Production Systems sIMulator modeling framework, by combining contrasting Australian soils, climates, and farming practices (crop rotations, and management within rotations, such as fertilization, tillage, and residue management) in a factorial design. This design resulted in the transposition of contrasting soils and climates in our simulations, giving soil-climate combinations that do not occur in the study area to help provide insights into the importance of the climate constraints on SOC. We statistically analyzed the model's outputs to determinate the relative contributions of soil parameters, climate, and farming practices on SOC. The initial SOC content had the largest impact on the value of SOC, followed by the climate and the fertilization practices. These factors explained 66, 18, and 15% of SOC variations, respectively, after 80 years of constant farming practices in the simulation. Tillage and stubble management had the lowest impacts on SOC. This study highlighted the possible negative impact on SOC of a chickpea phase in a wheat-chickpea rotation and the potential positive impact of a cover crop in a sub-tropical climate (QLD, Australia) on SOC. It also showed the complexities in managing to achieve increased SOC, while simultaneously aiming to minimize nitrous oxide (N2O) emissions and nitrate leaching in farming systems. The transposition of contrasting soils and climates in our simulations revealed the importance of the climate constraints on SOC.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

A Paddock to reef monitoring and modelling framework for the Great Barrier Reef: Paddock and catchment component.

Targets for improvements in water quality entering the Great Barrier Reef (GBR) have been set through the Reef Water Quality Protection Plan (Reef Plan). To measure and report on progress towards the targets set a program has been established that combines monitoring and modelling at paddock through to catchment and reef scales; the Paddock to Reef Integrated Monitoring, Modelling and Reporting Program (Paddock to Reef Program). This program aims to provide evidence of links between land management activities, water quality and reef health. Five lines of evidence are used: the effectiveness of management practices to improve water quality; the prevalence of management practice adoption and change in catchment indicators; long-term monitoring of catchment water quality; paddock & catchment modelling to provide a relative assessment of progress towards meeting targets; and finally marine monitoring of GBR water quality and reef ecosystem health. This paper outlines the first four lines of evidence.

Can the nitrogenous composition of xylem sap be used to assess salinity stress in Casuarina glauca?

It is predicted that dryland salinity will affect up to 17 Mha of the Australian landscape by 2050, and therefore, monitoring the health of tree plantings and remnant native vegetation in saline areas is increasingly important. Casuarina glauca Sieber ex Spreng. has considerable salinity tolerance and is commonly planted in areas with a shallow, saline water table. To evaluate the potential of using the nitrogenous composition of xylem sap to assess salinity stress in C. glauca, the responses of trees grown with various soil salinities in a greenhouse were compared with those of trees growing in field plots with different water table depths and groundwater salinities. In the greenhouse, increasing soil salinity led to increased allocation of nitrogen (N) to proline and arginine in both stem and root xylem sap, with coincident decreases in citrulline and asparagine. Although the field plots were ranked as increasingly saline-based on ground water salinity and depth-only the allocation of N to citrulline differed significantly between the field plots. Within each plot, temporal variation in the composition of the xylem sap was related to rainfall, rainfall infiltration and soil salinity. Periods of low rainfall and infiltration and higher soil salinity corresponded with increased allocation of N to proline and arginine in the xylem sap. The allocation of N to citrulline and asparagine increased following rainfall events where rain was calculated to have infiltrated sufficiently to decrease soil salinity. The relationship between nitrogenous composition of the xylem sap of C. glauca and soil salinity indicates that the analysis of xylem sap is an effective method for assessing changes in salinity stress in trees at a particular site over time. However, the composition of the xylem sap proved less useful as a comparative index of salinity stress in trees growing at different sites.

A comparison of heat pulse and deuterium tracing techniques for estimating sap flow in Eucalyptus grandis trees.

Sap flow rates were measured simultaneously by the heat pulse and deuterium tracing techniques in nine Eucalyptus grandis W. Hill ex Maiden. trees at two sites (1) to compare results from the two techniques and (2) to assess the impact of the assumptions underlying the deuterium tracing method on the calculation of sap flow for a range of tree sizes. The trees ranged in height from 4 to 14 m with leaf areas of 5 to 35 m(2). In all trees, sap flow estimated by the deuterium tracing technique was higher than sap flow estimated by the heat pulse method, with differences of 11 to 43% in eight of the trees and 113% in one tree. The largest difference was attributed to errors in the heat pulse method, as indicated by aberrant relationships between sap flow measured by the heat pulse method and tree size characteristics (i.e., diameter, sap wood area, leaf area) for that tree compared with the other experimental trees. Drilling holes in the trees to allow injection of deuterium had no significant effect on sap flow, even when 32 holes were drilled. Sap flow measured by the heat pulse method was only lower after drilling than before drilling in three trees, and the difference only persisted for about 1 h. Deuterium concentrations of water collected from the tree canopies had not returned to background values 17 days after injection. Twenty-one days after injection, sapwood and heartwood samples taken from trunks near the injection sites contained considerable concentrations of deuterium, indicating that some of the deuterium injected into the trees was still present. An experiment performed on two trees showed that deuterium was stored in the heartwood and sapwood throughout the trees, and its distribution within the trees four days after injection was similar whether it was injected into only the sapwood (where it should mix with sap and be transported from the tree most readily) or into both the sapwood and heartwood, indicating that there was considerable movement of deuterium between the heartwood and sapwood. Deuterium storage was accounted for by an approximate means in the sap flow calculations, and may have resulted in an error of about 10% in sap flow estimated by this method. We conclude that the heat pulse and deuterium tracing techniques can be used simultaneously to increase the number of sap flow measurements obtained from a forest, thereby increasing the precision of forest water use estimates. Their combination would be most effective in stands with a wide range of tree sizes and sap flow rates, where the relative differences in sap flux estimates between the methods is small compared with differences in sap flow between trees.

Variations in stream water uptake by Eucalyptus camaldulensis with differing access to stream water.

The stable isotopes 2H and 18O were used to determine the water sources of Eucalyptus camaldulensis at three sites with varying exposure to stream water, all underlain by moderately saline groundwater. Water uptake patterns were a function of the long-term availability of surface water. Trees with permanent access to a stream used some stream water at all times. However, water from soils or the water table commonly made up 50% of these trees' water. Trees beside an ephemeral stream had access to the stream 40-50% of the time (depending on the level of the stream). No more than 30% of the water they used was stream water when it was available. However, stream water use did not vary greatly whether the trees had access to the stream for 2 weeks or 10 months prior to sampling. Trees at the third site only had access to surface water during a flood. These trees did not change their uptake patterns during 2 months inundation compared with dry times, so were not utilising the low-salinity flood water. Pre-dawn leaf water potentials and leaf 13C measurements showed that the trees with permanent access to the stream experienced lower water stress and had lower water use efficiencies than trees at the least frequently flooded site. The trees beside the ephemeral stream appeared to change their water use efficiency in response to the availability of surface water; it was similar to the perennial-stream trees when stream water was available and higher at other times. Despite causing water stress, uptake of soil water and groundwater would be advantageous to E. camaldulensis in this semi-arid area, as it would provide the trees with a supply of nutrients and a reliable source of water. E. camaldulensis at the study site may not be as vulnerable to changes in stream flow and water quality as previously thought.