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Optimising the spatial distribution of global croplands could substantially reduce carbon emissions and biodiversity loss associated with rain-fed crop production, according to a mathematical framework applied to environmental impact and crop yield data

Abstract Cassava is consumed by 800 million people and is a staple crop in Africa. Its production may increase under climate change due to its high drought tolerance. We produced a systematic map of scientific studies about cassava farming practices, with the aim of identifying knowledge gaps and clusters. Our secondary aim was to develop a classification system for [1] farming interventions and [2] agricultural, economic and environmental outcomes. Standardised classification systems facilitate data reuse, including for evidence synthesis, and promote research efficiency. Following our published protocol, we searched eight publication databases using the search string ‘cassava OR mandioca OR manihot OR manioc OR yuca’ in December 2017. We screened 36,580 records and included publications that measured the impact of cassava farming practices on agricultural, economic or environmental outcomes, including yield, soil, water, wildlife and labour. We classified the resultant 1599 publications by interventions, outcomes, location, study year and study design. We assessed coding consistency using Kappa scores. We found regional knowledge clusters (Nigeria, Columbia and Brazil accounted for 45.5% of country occurrences) and gaps (e.g. the Democratic Republic of Congo). There were knowledge clusters for interventions testing cultivar type, fertiliser use and diversifying crop rotations and outcomes related to crop production (e.g. yield/biomass). We found knowledge gaps for environmental interventions and outcomes (e.g. 5% of studies measured pollutants or wildlife). In terms of study design, reporting standards were poor (e.g. 24% of studies did not report start dates), average study duration was 2 years, and average publication delays were 4 years. The Kappa scores indicated that we successfully developed consistent ontologies (named Agri‐ontologies 1.0). The map and ontologies are available online: https://www.metadataset.com/. This systematic map of cassava farming practices can direct researchers and funders to knowledge gaps that need addressing, and reviewers to knowledge clusters for synthesis. Better research practices should be promoted within cassava research, as poor reporting standards, short study durations and long publication delays result in an ineffective research environment. This systematic map provides an evidence base for cassava production and the ontologies (Agri‐ontologies 1.0) can be applied to other systems to facilitate more efficient and effective synthesis.

Abstract The need for conservation action to be cost‐effective is widely accepted, resulting in increased interest and effort to assess effectiveness. Assessing the financial and economic costs of conservation is equally important for assessing cost‐effectiveness, yet their measurement and assessment are repeatedly identified as lacking. The healthcare sector, in contrast, has made substantial progress in identifying and including costs in decision‐making. Here, we consider what conservation can learn from this experience. We present a three‐step framework for identifying and recording the relevant economic costs and benefits of conservation interventions where the user (1) describes the costing context, (2) determines which types of cost and benefit to include, and (3) obtains values for these costs and benefits alongside metadata necessary for others to interpret the data. This framework is designed to help estimate economic costs but can also be used flexibly to record the direct costs of interventions (i.e., financial costs) and calculate financial and economic benefits. Although recording data on economic costs and benefits is deceptively complex, this framework facilitates improved recording, and indicates how collating this data could enhance the assessment of cost‐effectiveness across conservation contexts using a range of decision‐making tools.

Abstract Background Meta-analysis is often used to make generalisations across all available evidence at the global scale. But how can these global generalisations be used for evidence-based decision making at the local scale, if the global evidence is not perceived to be relevant to local decisions? We show how an interactive method of meta-analysis—dynamic meta-analysis—can be used to assess the local relevance of global evidence. Results We developed Metadataset ( www.metadataset.com ) as a proof-of-concept for dynamic meta-analysis. Using Metadataset, we show how evidence can be filtered and weighted, and results can be recalculated, using dynamic methods of subgroup analysis, meta-regression, and recalibration. With an example from agroecology, we show how dynamic meta-analysis could lead to different conclusions for different subsets of the global evidence. Dynamic meta-analysis could also lead to a rebalancing of power and responsibility in evidence synthesis, since evidence users would be able to make decisions that are typically made by systematic reviewers—decisions about which studies to include (e.g. critical appraisal) and how to handle missing or poorly reported data (e.g. sensitivity analysis). Conclusions In this study, we show how dynamic meta-analysis can meet an important challenge in evidence-based decision making—the challenge of using global evidence for local decisions. We suggest that dynamic meta-analysis can be used for subject-wide evidence synthesis in several scientific disciplines, including agroecology and conservation biology. Future studies should develop standardised classification systems for the metadata that are used to filter and weight the evidence. Future studies should also develop standardised software packages, so that researchers can efficiently publish dynamic versions of their meta-analyses and keep them up-to-date as living systematic reviews. Metadataset is a proof-of-concept for this type of software, and it is open source. Future studies should improve the user experience, scale the software architecture, agree on standards for data and metadata storage and processing, and develop protocols for responsible evidence use.

Mangroves are often cleared for aquaculture, agriculture, and coastal development despite the range of benefits for people and nature that they provide. In response to these losses, there are multiple global, and regional efforts aimed at accelerating mangrove forest restoration, resulting in many restoration projects being implemented and managed by different groups with highly diverse objectives. The information reported from these restoration projects is extremely variable, limiting our ability to identify whether desired objectives have been met or key factors that determine effective and durable restoration have been applied. To address this problem, we developed a holistic monitoring framework that captures the key indicators of restoration, spanning project aims, intervention type, costs, and ecological and socioeconomic outcomes. Subsequently, using a systematic literature search, we examined 123 published case studies to identify the range and quality of reported information on restoration, relative to our framework. We found that there were many gaps in reporting, for multiple indicators. Sections related to site conditions prior to restoration (reported in only 32% of case studies) and socioeconomic outcomes (26%) were consistently missing from most project reporting. Conversely, information on the type of intervention was reported for all case studies, and the aims of the project (reported in 76% of case studies) and ecological monitoring (82%) were far more prevalent. Generally, the restoration literature did not follow any specific framework in terms of reporting which likely contributed to the gaps in the information recorded. These gaps hinder comparisons between case studies, inhibiting the ability to learn lessons from previous restoration attempts by identifying commonalities. The need for more structure and consistent reporting supports the development of a standard restoration tracking tool that can facilitate the comparison of restoration efforts, aiding the implementation of future projects.

Randomised controlled experiments are the gold standard for scientific inference, but environmental and social scientists often rely on different study designs. Here the authors analyse the use of six common study designs in the fields of biodiversity conservation and social intervention, and quantify the biases in their estimates.

Evidence-based decision-making is most effective with comprehensive access to scientific studies. If studies face significant publication delays or barriers, the useful information they contain may not reach decision-makers in a timely manner. This represents a potential problem for mission-oriented disciplines where access to the latest data is required to ensure effective actions are undertaken. We sought to analyse the severity of publication delay in conservation science—a field that requires urgent action to prevent the loss of biodiversity. We used the Conservation Evidence database to assess the length of publication delay (time from finishing data collection to publication) in the literature that tests the effectiveness of conservation interventions. From 7,447 peer-reviewed and non-peer-reviewed studies of conservation interventions published over eleven decades, we find that the raw mean publication delay was 3.2 years (±2SD = 0.1) and varied by conservation subject. A significantly shorter delay was observed for studies focused on Bee Conservation, Sustainable Aquaculture, Management of Captive Animals, Amphibian Conservation, and Control of Freshwater Invasive Species (Estimated Marginal Mean range from 1.4–1.9 years). Publication delay was significantly shorter for the non-peer-reviewed literature (Estimated Marginal Mean delay of 1.9 years ± 0.2) compared to the peer-reviewed literature (i.e., scientific journals; Estimated Marginal Mean delay of 3.0 years ± 0.1). We found publication delay has significantly increased over time (an increase of ~1.2 years from 1912 (1.4 years ± 0.2) to 2020 (2.6 years ± 0.1)), but this change was much weaker and non-significant post-2000s; we found no evidence for any decline. There was also no evidence that studies on more threatened species were subject to a shorter delay—indeed, the contrary was true for mammals, and to a lesser extent for birds. We suggest a range of possible ways in which scientists, funders, publishers, and practitioners can work together to reduce delays at each stage of the publication process.

Abstract Background Invasive alien plant species often have negative environmental and social impacts, such as loss of biodiversity and alteration of ecosystem services. As a result, managing the introduction, establishment, and abundance of invasive species is a major priority. To do this effectively, we need evidence on the effect of management interventions (such as using herbicide or cutting to control invasive plants). This evidence should not only include the effects of these management interventions on invasive alien species, but also on native species and other non-target outcomes such as ecosystem services. Such evidence would allow for comparison of the trade-offs between different management interventions. In the planned review we aim to assess how management interventions to control nine priority invasive alien plants species in England and Wales affect environmental outcomes. These species are: Japanese knotweed (Fallopia japonica) Nuttall’s waterweed (Elodea nuttallii), Chilean rhubarb (Gunnera tinctoria), Giant hogweed (Heracleum mantegazzianum), Floating pennywort (Hydrocotyle ranunculoides), Himalayan balsam (Impatiens glandulifera), Curly waterweed (Lagarosiphon major), American skunk cabbage (Lysichiton americanus), Parrot’s feather (Myriophyllum aquaticum). Methods Searches will be in English and use bibliographic databases (Scopus, Web of Science Core Collection, Open Access Theses and Dissertations, and Conservation Evidence) and internet searches (Google Scholar), as well as specialist databases. Our methodology will only use the names of each species (scientific names and common names, including synonyms) as our search string (we will not use qualifiers, such as “AND invasive”). This will give low specificity but will increase the likelihood of capturing all relevant information. We will use predefined criteria for study inclusion and data extraction. We will screen publications in two stages: (1) using titles and abstracts and (2) using full texts. Consistency of inclusion will be checked by two people screening a random sample of 10% of titles and abstracts. This dual-screening will be subject to kappa analysis and any disagreements resolved through discussion. We will use critical appraisal to assess study validity by identifying studies that are potentially prone to bias.