How does tillage intensity affect soil organic carbon? A systematic review protocol

Background Soils contain the greatest terrestrial carbon (C) pool on the planet. Since approximately 12% of soil C is held in cultivated soils, management of these agricultural areas has a huge potential to affect global carbon cycling; acting sometimes as a sink but also as a source. Tillage is one of the most important agricultural practices for soil management and has been traditionally undertaken to mechanically prepare soils for seeding and minimize effects of weeds. It has been associated with many negative impacts on soil quality, most notably a reduction in soil organic carbon (SOC), although still a matter of considerable debate, depending on factors such as depth of measurement, soil type, and tillage method. No tillage or reduced intensity tillage are frequently proposed mitigation measures for preservation of SOC and improvement of soil quality, for example for reducing erosion. Whilst several reviews have demonstrated benefits to C conservation of no till agriculture over intensive tillage, the general picture for reduced tillage intensity is unclear. This systematic review proposes to synthesise an extensive body of evidence, previously identified through a systematic map. Methods This systematic review is based on studies concerning tillage collated in a recently completed systematic map on the impact of agricultural management on SOC restricted to the warm temperate climate zone (i.e. boreo-temperate). These 311 studies were identified and selected systematically according to CEE guidelines. An update of the original search will be undertaken to identify newly published academic and grey literature in the time since the original search was performed in September 2013. Studies will be critically appraised for their internal and external validity, followed by full data extraction (meta-data describing study settings and quantitative study results). Where possible, studies will be included in meta-analyses examining the effect of tillage reduction (‘moderate' (i.e. shallow) and no tillage relative to ‘intensive' tillage methods such as mouldboard ploughing, where soil is turned over throughout the soil profile). The implications of the findings will be discussed in terms of policy, practice and research along with a discussion of the nature of the evidence base

Which agricultural management interventions are most influential on soil organic carbon (using time series data)?

Background Loss of soil organic carbon (SOC) from agricultural land is identified as one of the major threats to soils, as it influences both fertility and the production of ecosystem services from agriculture. Losses of SOC across regions are often determined by monitoring in different land use systems. Results from agricultural field experiments can reveal increasing SOC stocks after implementation of specific management practices compared to a control, though in time series experiments the relative rate of change is often negative and implying an overall loss. Long-term agricultural field experiments are indispensable for quantifying absolute changes in SOC stocks under different management regimes. Since SOC responses are seldom linear over time, time series data from these experiments are particularly valuable. Methods This systematic review is based on studies reporting time series data collated in a recently completed systematic map on the topic restricted to the warm temperate climate zone and the snow climate zone. These 53 studies were identified and selected systematically according to CEE guidelines. An update of the original search for studies will be repeated using Web of Science and Google Scholar to include newly published academic and grey literature in the time since the original search was performed in September 2013. Studies will be subject to critical appraisal of the internal and external validity, followed by full data extraction (meta-data describing study settings and quantitative study results). Where possible, studies will be included in a quantitative synthesis using time series meta-analytical approaches. The implications of the meta-analytical findings will be discussed in terms of policy, practice and research along with a discussion of the nature of the evidence base

Human well-being impacts of terrestrial protected areas

© 2013 Pullin et al.; licensee BioMed Central Ltd. Background: Establishing Protected Areas (PAs) is among the most common conservation interventions. Protecting areas from the threats posed by human activity will by definition inhibit some human actions. However, adverse impacts could be balanced by maintaining ecosystem services or introducing new livelihood options. Consequently there is an ongoing debate on whether the net impact of PAs on human well-being at local or regional scales is positive or negative. We report here on a systematic review of evidence for impacts on human well-being arising from the establishment and maintenance of terrestrial PAs. Methods: Following an a priori protocol, systematic searches were conducted for evidence of impacts of PAs post 1992. After article title screening, the review was divided into two separate processes; a qualitative synthesis of explanations and meaning of impact and a review of quantitative evidence of impact. Abstracts and full texts were assessed using inclusion criteria and conceptual models of potential impacts. Relevant studies were critically appraised and data extracted and sorted according to type of impact reported. No quantitative synthesis was possible with the evidence available. Two narrative syntheses were produced and their outputs compared in a metasynthesis. Results: The qualitative evidence review mapped 306 articles and synthesised 34 that were scored as high quality. The quantitative evidence review critically appraised 79 studies and included 14 of low/medium susceptibility to bias. The meta-synthesis reveals that a range of factors can lead to reports of positive and negative impacts of PA establishment, and therefore might enable hypothesis generation regarding cause and effect relationships, but resulting hypotheses cannot be tested with the current available evidence. Conclusions: The evidence base provides a range of possible pathways of impact, both positive and negative, of PAs on human well-being but provides very little support for decision making on how to maximise positive impacts. The nature of the research reported to date forms a diverse and fragmented body of evidence unsuitable for the purpose of informing policy formation on how to achieve win-win outcomes for biodiversity and human well-being. To better assess the impacts of PAs on human well-being we make recommendations for improving research study design and reporting

Mapping global research on climate and health using machine learning (a systematic evidence map)

Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that conventional evidence synthesis methods are no longer sufficient or feasible. Here, we outline a protocol using machine learning approaches to systematically synthesize global evidence on the relationship between climate change, climate variability, and weather (CCVW) and human health. We will use supervised machine learning to screen over 300,000 scientific articles, combining terms related to CCVW and human health. Our inclusion criteria comprise articles published between 2013 and 2020 that focus on empirical assessment of: CCVW impacts on human health or health-related outcomes or health systems; relate to the health impacts of mitigation strategies; or focus on adaptation strategies to the health impacts of climate change. We will use supervised machine learning (topic modeling) to categorize included articles as relevant to impacts, mitigation, and/or adaptation, and extract geographical location of studies. Unsupervised machine learning using topic modeling will be used to identify and map key topics in the literature on climate and health, with outputs including evidence heat maps, geographic maps, and narrative synthesis of trends in climate-health publishing. To our knowledge, this will represent the first comprehensive, semi-automated, systematic evidence synthesis of the scientific literature on climate and health

Contributions of scale: What we stand to gain from Indigenous and local inclusion in climate-health monitoring and surveillance systems

Understanding how climate change will affect global health is a defining challenge this century. This is predicated, however, on our ability to combine climate and health data to investigate the ways in which variations in climate, weather, and health outcomes interact. There is growing evidence to support the value of place- and community-based monitoring and surveillance efforts, which can contribute to improving both the quality and equity of data collection needed to investigate and understand the impacts of climate change on health. The inclusion of multiple and diverse knowledge systems in climate-health surveillance presents many benefits, as well as challenges. We conducted a systematic review, synthesis, and confidence assessment of the published literature on integrated monitoring and surveillance systems for climate change and public health. We examined the inclusion of diverse knowledge systems in climate-health literature, focusing on: 1) analytical framing of integrated monitoring and surveillance system processes 2) key contributions of Indigenous knowledge and local knowledge systems to integrated monitoring and surveillance systems processes; and 3) patterns of inclusion within these processes. In total, 24 studies met the inclusion criteria and were included for data extraction, appraisal, and analysis. Our findings indicate that the inclusion of diverse knowledge systems contributes to integrated climate-health monitoring and surveillance systems across multiple processes of detection, attribution, and action. These contributions include: the definition of meaningful problems; the collection of more responsive data; the reduction of selection and source biases; the processing and interpretation of more comprehensive datasets; the reduction of scale dependent biases; the development of multi-scale policy; long-term future planning; immediate decision making and prioritization of key issues; as well as creating effective knowledge-information-action pathways. The value of our findings and this review is to demonstrate how neither scientific, Indigenous, nor local knowledge systems alone will be able to contribute the breadth and depth of information necessary to detect, attribute, and inform action along these pathways of climate-health impact. Rather, it is the divergence or discordance between the methodologies and evidences of different knowledge systems that can contribute uniquely to this understanding. We critically discuss the possibility of what we, mainly local communities and experts, stand to lose if these processes of inclusion are not equitable. We explore how to shift the existing patterns of inclusion into balance by ensuring the equity of contributions and justice of inclusion in these integrated monitoring and surveillance system processes

Flexible prey handling, preference and a novel capture technique in invasive, sub-adult Chinese mitten crabs

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The multifunctional roles of vegetated strips around and within agricultural fields : A systematic map protocol.

Background: Agriculture and agricultural intensification can have significant negative impacts on the environment, including nutrient and pesticide leaching, spreading of pathogens, soil erosion and reduction of ecosystem services provided by terrestrial and aquatic biodiversity. The establishment and management of vegetated strips adjacent to farmed fields (including various field margins, buffer strips and hedgerows) are key mitigation measures for these negative environmental impacts and environmental managers and other stakeholders must often make decisions about how best to design and implement vegetated strips for a variety of different outcomes. However, it may be difficult to obtain relevant, accurate and summarised information on the effects of implementation and management of vegetated strips, even though a vast body of evidence exists on multipurpose vegetated strip interventions within and around fields. To improve the situation, we describe a method for assembling a database of relevant research relating to vegetated strips undertaken in boreo-temperate farming systems (arable, pasture, horticulture, orchards and viticulture). Methods: We will search 13 bibliographic databases, 1 search engine and 37 websites for stakeholder organisations using a predefined and tested search string that focuses on a comprehensive list of vegetated strip synonyms. Non-English language searches in Danish, Finnish, German, Spanish, and Swedish will also be undertaken using a web-based search engine. We will screen search results at title, abstract and full text levels, recording the number of studies deemed non-relevant (with reasons at full text). A systematic map database that displays the meta-data (i.e. descriptive summary information about settings and methods) of relevant studies will be produced following full text assessment. The systematic map database will be displayed as a web-based geographical information system (GIS). The nature and extent of the evidence base will be discussed