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.
View Article and Find Full Text PDFCrop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change.
View Article and Find Full Text PDFIn 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.
View Article and Find Full Text PDFTo protect and improve water quality in the Great Barrier Reef, the Queensland Government's Reef 2050 Water Quality Improvement Plan targets that 90% of sugarcane, horticulture, cropping and grazing lands in priority areas be managed using best management practices for sediment, nutrient and pesticides by 2025. Progress towards this target is insufficient and variable across catchments and industries. The motivation to adopt improvements in management practices is heavily influenced by social, economic, cultural and institutional dimensions.
View Article and Find Full Text PDFIncreasing the precision of nitrogen (N) fertiliser management in cropping systems is integral to increasing the environmental and economic sustainability of cropping. In a simulation study, we found that natural variability in year-to-year climate had a major effect on optimum N fertiliser rates for sugarcane in the Tully region of north-eastern Australia, where N discharges pose high risks to Great Barrier Reef ecosystems. There were interactions between climate and other factors affecting crop growth that made optimum N rates field-specific.
View Article and Find Full Text PDFBackground: Temperature regulation in women undergoing emergency caesarean section is a complex topic about which there is a paucity of evidence-based recommendations. The adverse effects of inadvertent peri-operative hypothermia are well described. Hyperthermia is also associated with adverse neonatal outcomes, an increased risk of obstetric intervention and increased treatment for suspected sepsis.
View Article and Find Full Text PDFBackground: 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.
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.
View Article and Find Full Text PDFSmallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking.
View Article and Find Full Text PDFNitrification inhibitors show great potential to reduce nitrogen losses from agricultural systems and to improve nitrogen use efficiency. The most recently developed nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) is gaining popularity due to its benefits relative to other compounds. However, the behaviour of DMPP and its effect on nitrification in soils has been characterised using inconsistent and confusing terminology.
View Article and Find Full Text PDFSoils 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.
View Article and Find Full Text PDFEfforts 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.
View Article and Find Full Text PDFA 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.
View Article and Find Full Text PDFUse of chemical agricultural inputs such as nitrogen fertilisers (N) in agricultural production can cause diffuse source pollution thereby degrading the health of coastal and marine ecosystems in coastal river catchments. Previous reviewed economic assessments of N management in agricultural production seldom consider broader environmental impacts and uncertain climatic and economic conditions. This paper presents an economic risk framework for assessing economic and environmental trade-offs of N management strategies taking into account variable climatic and economic conditions.
View Article and Find Full Text PDFHistorically 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.
View Article and Find Full Text PDFIntegrated 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.
View Article and Find Full Text PDFSugarcane 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.
View Article and Find Full Text PDFIncreasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C.
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