The 18O ecohydrology of a grassland ecosystem - predictions and observations

This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. SCHN 557/9-1), the Agence Nationale de la Recherche (grant no. ANR-13-BS06-0005), and the European Commission (grant no. SOLCA 338264). This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.Peer reviewedPublisher PD

First 20 years of DNDC: Model evolution and GRAMP.

The DNDC (DeNitrification and DeComposition) model was first developed by Li et al. (1992) as a rain event-driven process-orientated simulation model for nitrous oxide, carbon dioxide and nitrogen gas emissions from the agricultural soils in the U.S. Over the last 20 years, the model has been modified and adapted by various research groups around the world to suit specific purposes and circumstances. The Global Research Alliance Modelling Platform (GRAMP) is a UK-led initiative for the establishment of a purposeful and credible web-based platform initially aimed at users of the DNDC model. With the aim of improving the predictions of soil C and N cycling in the context of climate change the objectives of GRAMP are to: 1) to document the existing versions of the DNDC model; 2) to create a family tree of the individual DNDC versions; 3) to provide information on model use and development; and 4) to identify strengths, weaknesses and potential improvements for the model

Greenhouse gas emissions from agricultural food production to supply Indian diets: Implications for climate change mitigation

Agriculture is a major source of greenhouse gas (GHG) emissions globally. The growing global population is putting pressure on agricultural production systems that aim to secure food production while minimising GHG emissions. In this study, the GHG emissions associated with the production of major food commodities in India are calculated using the Cool Farm Tool. GHG emissions, based on farm management for major crops (including cereals like wheat and rice, pulses, potatoes, fruits and vegetables) and livestock-based products (milk, eggs, chicken and mutton meat), are quantified and compared. Livestock and rice production were found to be the main sources of GHG emissions in Indian agriculture with a country average of 5.65 kg CO2eq kg-1 rice, 45.54 kg CO2eq kg-1 mutton meat and 2.4 kg CO2eq kg-1 milk. Production of cereals (except rice), fruits and vegetables in India emits comparatively less GHGs with <1 kg CO2eq kg-1 product. These findings suggest that a shift towards dietary patterns with greater consumption of animal source foods could greatly increase GHG emissions from Indian agriculture. A range of mitigation options are available that could reduce emissions from current levels and may be compatible with increased future food production and consumption demands in India

A new integrated assessment framework for climate-smart nutrition security in sub-Saharan Africa : the integrated Future Estimator for Emissions and Diets (iFEED)

Funding statement This work was supported by the Biotechnology and Biological Sciences Research Council through UK Research and Innovation as part of the Global Challenges Research Fund, AFRICAP programme, grant number BB/P027784/1.Peer reviewedPublisher PD

The Comprehensive Post-Acute Stroke Services (COMPASS) study: design and methods for a cluster-randomized pragmatic trial

Background: Patients discharged home after stroke face significant challenges managing residual neurological deficits, secondary prevention, and pre-existing chronic conditions. Post-discharge care is often fragmented leading to increased healthcare costs, readmissions, and sub-optimal utilization of rehabilitation and community services. The COMprehensive Post-Acute Stroke Services (COMPASS) Study is an ongoing cluster-randomized pragmatic trial to assess the effectiveness of a comprehensive, evidence-based, post-acute care model on patient-centered outcomes. Methods: Forty-one hospitals in North Carolina were randomized (as 40 units) to either implement the COMPASS care model or continue their usual care. The recruitment goal is 6000 patients (3000 per arm). Hospital staff ascertain and enroll patients discharged home with a clinical diagnosis of stroke or transient ischemic attack. Patients discharged from intervention hospitals receive 2-day telephone follow-up; a comprehensive clinic visit within 2 weeks that includes a neurological evaluation, assessments of social and functional determinants of health, and an individualized COMPASS Care PlanTM integrated with a community-specific resource database; and additional follow-up calls at 30 and 60 days post-stroke discharge. This model is consistent with the Centers for Medicare and Medicaid Services transitional care management services provided by physicians or advanced practice providers with support from a nurse to conduct patient assessments and coordinate follow-up services. Patients discharged from usual care hospitals represent the control group and receive the standard of care in place at that hospital. Patient-centered outcomes are collected from telephone surveys administered at 90 days. The primary endpoint is patient-reported functional status as measured by the Stroke Impact Scale 16. Secondary outcomes are: caregiver strain, all-cause readmissions, mortality, healthcare utilization, and medication adherence. The study engages patients, caregivers, and other stakeholders (including policymakers, advocacy groups, payers, and local community coalitions) to advise and support the design, implementation, and sustainability of the COMPASS care model. Discussion: Given the high societal and economic burden of stroke, identifying a care model to improve recovery, independence, and quality of life is critical for stroke survivors and their caregivers. The pragmatic trial design provides a real-world assessment of the COMPASS care model effectiveness and will facilitate rapid implementation into clinical practice if successful

CCAFS Mitigation Options Tool, Beta version

The CCAFS Mitigation Options Tool estimates greenhouse gas emissions from multiple crop and livestock management practices in different geographic regions, providing policy-makers across the globe access to reliable information needed to make science-informed decisions about emission reductions from agriculture. CCAFS0MOT joins several empirical models to estimate GHG emissions from different land uses and suggests mitigation options that are compatible with food productions. Researchers at the University of Aberdeen, in partnership with the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the University of Vermont’s Gund Institute of Environment, are developing the tool. CCAFS-MOT has also received support from the United States Agency for International Development (USAID), the United States Department of Agriculture (USDA) and the International Center for Tropical Agriculture (CIAT). CCAFS is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors

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Not Availablefood security, and the livelihoods of farming communities globally. Whilst adaptation to climate change is necessary to ensure food security and protect livelihoods of poor farmers, mitigation of greenhouse gas (GHG) emissions can lessen the extent of climate change and future needs for adaptation. Many agricultural practices can potentially mitigate GHG emissions without compromising food production. India is the third largest GHG emitter in the world where agriculture is responsible for 18% of total national emissions. India has identified agriculture as one of the priority sectors for GHG emission reduction in its Nationally Determined Contributions (NDCs). Identification of emission hotspots and cost-effective mitigation options in agriculture can inform the prioritisation of efforts to reduce emissions without compromising food and nutrition security. We adopted a bottom-up approach to analyse GHG emissions using large datasets of India's ‘cost of cultivation survey’ and the ‘19th livestock census’ together with soil, climate and management data for each location. Mitigation measures and associated costs and benefits of adoption, derived from a variety of sources including the literature, stakeholder meetings and expert opinion, were presented in the form of Marginal Abatement Cost Curves (MACC). We estimated that by 2030, business-as-usual GHG emissions from the agricultural sector in India would be 515 Megatonne CO2 equivalent (MtCO2e) per year with a technical mitigation potential of 85.5 MtCO2e per year through adoption of various mitigation practices. About 80% of the technical mitigation potential could be achieved by adopting only cost-saving measures. Three mitigation options, i.e. efficient use of fertilizer, zero-tillage and rice-water management, could deliver more than 50% of the total technical abatement potential.Not Availabl

Model-based analysis of causes of habitat segregation in Idotea species (Crustacea, Isopoda)

On the shore of the rocky island of Helgoland (North Sea) two closely related isopod species, Idotea balthicaPallas, 1772, and Idotea granulosaRathke, 1843, share a similar fundamental niche but inhabit well-separated habitats. Idotea balthica inhabits floating algae at the sea surface and accumulations of decaying algae on the seafloor, whereas I. granulosa primarily occurs in intertidal macroalgal belts. In laboratory experiments on individually reared isopods I. balthica outperformed I. granulosa with regard to growth, reproduction, and mortality in both a fully inundated habitat and in a tidal habitat with 5 h of daily emergence. We hypothesized that habitat segregation in the two isopod species is driven by one or multiple types of biotic interactions: (1) no interaction, (2) cannibalism, (3) intraguild predation, and (4) terrestrial predation. In order to evaluate how habitat segregation can be explained by each of these interaction types we employed a size-structured population model to account for the body-size-dependent predation. Net population growth rates were fitted to the simulations as a measure of population fitness. Experimental results served as database for parameter and process identification. As predation rates were unknown, we performed a sensitivity analysis for these. We found that below 5 h of daily tidal emergence either cannibalism or terrestrial predation sufficed to explain habitat segregation. Intraguild predation, in contrast, advantaged I. balthica in any case. From linear extrapolation of the effects occurring under conditions of 5 h of daily tidal emergence, we predict that contrasting physiological responses in I. balthica and I. granulosa would cause segregation even without any interaction if emergence lasted long enough