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248 results for “management strategies”
Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies
<p>The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the "Soil management effect assessment" in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ("Knowledge gap in tab"-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other</p>
Dataset: Co-composting rose waste as a sustainable waste management strategy: Nutrient availability and disease control
<p>This dataset and these scripts supports the article 'Assessing the potential of co-composting rose waste as a sustainable waste management strategy: Nutrient availability and disease control' as published in Journal of Cleaner production. https://doi.org/10.1016/j.jclepro.2023.136685</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we investigated the potential of co-composting rose waste on a small scale. The objective was to increase the understanding of composting lignocellulosic rose waste, which will help with the implementation of composting practices within Kenyan rose production and thereby reduce its negative ecological impact.</p> <p>In a small-scale composting system (30L) the evolution of five mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the in-vitro disease suppressive capacity of mature rose waste was assessed. </p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
Survey used and data gathered for research into adoption of carbon management strategies amongst universities E Lewis-Brown et al 2022
<p>Survey used and data gathered for research into adoption of carbon management strategies amongst universities 2022, which forms part of a PhD thesis and will be submitted for publication in a journal. </p>
Figure 3 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
Figure 3. (a) Aboveground biomass of S. altissima in September 2019. (b) Biomass of rhizomes of S. altissima in September 2019. Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September. Data are presented as means ± standard errors of 14 replicates. Bars with different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey's test).
Figure 4 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
Figure 4. Effects of control practices on flowering rates of S. altissima in October 2019. Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September. Data are presented as means ± standard errors of 14 replicates. Bars with different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey's test).
Figure 1 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
Figure 1. (a) Coverage of S. altissima and (b) the average number of species per quadrat in the S. altissima-dominant areas or uninvaded areas. Data are presented as means ± standard errors of 14 replicates. Bars with different letters are significantly different at p <0.05 (Student's t-test).
Figure 2 in Mowing inhibits the invasion of the alien species Solidago altissima and is an effective management strategy
Figure 2. Number of shoots of S. altissima in March 2018 and April 2019. Mowing 1: mowed once in July; Mowing 2: mowed twice in May and September; Mowing 3: mowed three times in May, July, and September; Eco 200: 30% of shoots in the quadrats were cut near the ground, and the cut surfaces were covered with Eco 200 block. Data are presented as means ± standard errors of 14 replicates. Bars with different letters are significantly different at p <0.05 (ANOVA with post hoc Tukey's test).
Figure 2 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 2. Sustainability of the programs with stacked HT traits or residual herbicides, as influenced by application time (PRE and POST) and number of herbicide SOAs on (A) weed density and (B) resistance evolution. Resistance evolution is presented as % individuals that are resistant to at least one of the herbicides excluding H, either in the form of single or multiple resistance.The populations consist of 80% individuals resistant to H initially. Herbicide scenarios are detailed in Table 2. The simulations were set to stop when weed density exceeded 1 plant m−2, hence the incomplete lines of scenario EWM(i).
Figure 1 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 1. Sustainability of the POST-only programs,as influenced by the number of herbicide SOAs and the initial level of quantitative resistance to herbicide H. Cross-resistance between herbicides H and X is included in D–F. Results are presented as the year of weed control failure; bars represent the mean, and error bars represent the range of 100 replicates. Herbicide scenarios are detailed in Table 2. r-HX, correlation coefficient between phenotypic values of H and X.
Fig. 5 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 5. The average sum of FECs counts by year, demonstrating and increasing trend in FECs between 2015 and 2019. Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 4 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 4. The sum of FECs counted per gram of individual bison, demonstrating variation FECs between and among individuals. Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 3 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 3. The sum of FECs types, including "STRONGs" (Strongyle-type), "COCCs" (Coccidia), "NEMAs" (Nematodirus), "TRICHs" (Trichuris), "MONs" (Moniezia) counted per gram of sample from bison of various age classes. Ages classes included "NC" (New Calf; 0–1), "YR" (Yearling; 1–2), "JA" (Juvenile to Adult Transition; 2–4), "YA" (Young Adult; 4–6), "PA" (Peak Adult; 6–9), "MA" (Mature Adult; 9+). Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 2 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 2. The sum of FECs counted per gram of sample from bison of various age classes, including "NC" (New Calf; 0–1), "YR" (Yearling; 1–2), "JA" (Juvenile to Adult Transition; 2–4), "YA" (Young Adult; 4–6), "PA" (Peak Adult; 6–9), "MA" (Mature Adult; 9+). Black horizontal lines denote median values, while the top and bottom of boxes denote the upper and lower interquartile ranges (75th and 25th percentiles). Extending "whiskers" denote values of 1.5 times the interquartile range; points outside of this range constitute outliers.
Fig. 1 in Gastrointestinal parasites of a reintroduced semi-wild plains bison (Bison bison bison) herd: Examining effects of demographic variation, deworming treatments, and management strategy
Fig. 1. Aerial image of the Crane Trust bison pastures. The smaller North metapopulation was continuously grazed in the Visitor Center ("VC" – 50 acres) pasture (outlined in pink). The larger South metapopulation was rotated through Ruge-South Brown ("RS" – 387 acres) pasture (outlined in orange), Calving-Office ("CO" – 267 acres) pasture (outlined in yellow), and North Meadow ("NM" – 177 acres) pasture (outlined in green). The North (orange) and South (pink) metapopulation pastures were separated by a minimum distance of 200 m, including an 80 m channel of the Platte River. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Data and analysis code for Repo et al., "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes"
<p>This repository contains analysis code and pre-processed data for the study "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes" by Repo et al.<br>Data processing and analysis mainly done by Aapo Jantunen, Katharina Albrich<br>Due to respository space limitations, the original model outputs are archived in the Finnish "Allas" data storage service. For access, contact katharina.albrich@luke.fi<br>The code used to process the raw data is included here for reproducibility.</p> <p>If you are interested in using iLand, visit https://iland-model.org/ and https://iland-model.org/iland-book/ for information on using the model and a guide to setting up a landscape.</p> <p><span>This work was supported by the Ministry of Agriculture and Forestry by funding project Future multifunctional forests and their disturbance risk in the changing climate (Foster) through the “Catch the Carbon” initiative (<span>project number VN/28654/2020)</span>. A.R. has been supported by the grant [TRACY Trade-offs and synergies in land-based climate change mitigation and biodiversity conservation decision 322066 by the Academy of Finland.], J. H by the grant [CASCADE - Changing Disturbance Regimes and Forest Landscapes of Fennoscandia 342569 by the Academy of Finland]. </span></p> <p> </p>
Data from: Comparing alternative harvest strategies to address robustness to recruitment variability and uncertainty: Implications for Alaska Sablefish tested with management strategy evaluation
Open the record for dataset details and reuse information.
Density responses of lesser-studied carnivores to habitat and management strategies in southern Tanzania's Ruaha-Rungwa landscape
<p>Study using camera trapping and Spatially Explicit Capture-Recapture (SECR) to provide baseline status estimates for population densities of serval, striped hyaena and aardwolf in the Ruaha-Rungwa landscape and to assess the impact of habitat, management strategies and levels of anthropogenic impact on their population densities.</p>
Public opinion about management strategies for a low-profile Species across multiple jurisdictions: whitebark pine in the northern Rockies
<p>1. As public land managers seek to adopt and implement conservation measures aimed at reversing or slowing the negative effects of climate change, they are looking to understand public opinion regarding different management strategies.</p> <p>2. This study explores drivers of attitudes toward different management strategies (i.e., no management, protection, and restoration) for a low-profile but keystone tree species, the whitebark pine (<i>Pinus albicaulis</i>), in the Greater Yellowstone Ecosystem. Since the whitebark pine species has a range that traverses different federal land designations, we examine whether attitudes toward management strategies differ by jurisdiction (i.e., wilderness or federal lands more generally).</p> <p>3. We conducted a web and mail survey of residents from Montana, Idaho, and Wyoming, with 1,617 valid responses and a response rate of 16%. <a name="_Hlk36455244"></a></p> <p>4. We find that active management strategies have substantially higher levels of support than does no management, with relatively little differentiation across protection and restoration activities or across different land designations. We also find that support for management strategies is not influenced by values (political ideology) but is influenced by beliefs (about material vs. post-material environmental orientation, global climate change, and federal spending for public lands) and some measures of experience (e.g., knowledge of threats).</p> <p>5. This study helps land managers understand that support for active management of the whitebark pine species is considerable and nonpartisan and that beliefs and experience with whitebark pine trees are important for support</p>
Data from: Recognition of endophytic Trichoderma species by leaf-cutting ants and their potential in a Trojan-horse management strategy
Interactions between leaf-cutting ants, their fungal symbiont (Leucoagaricus) and the endophytic fungi within the vegetation they carry into their colonies are still poorly understood. If endophytes antagonistic to Leucoagaricus were found in plant material being carried by these ants, then this might indicate a potential mechanism for plants to defend themselves from leaf-cutter attack. In addition, it could offer possibilities for the management of these important Neotropical pests. Here, we show that, for Atta sexdens rubropilosa, there was a significantly greater incidence of Trichoderma species in the vegetation removed from the nests—and deposited around the entrances—than in that being transported into the nests. In a no-choice test, Trichoderma-infested rice was taken into the nest, with deleterious effects on both the fungal gardens and ant survival. The endophytic ability of selected strains of Trichoderma was also confirmed, following their inoculation and subsequent reisolation from seedlings of eucalyptus. These results indicate that endophytic fungi which pose a threat to ant fungal gardens through their antagonistic traits, such as Trichoderma, have the potential to act as bodyguards of their plant hosts and thus might be employed in a Trojan-horse strategy to mitigate the negative impact of leaf-cutting ants in both agriculture and silviculture in the Neotropics. We posit that the ants would detect and evict such 'malign' endophytes—artificially inoculated into vulnerable crops—during the quality-control process within the nest, and, moreover, that the foraging ants may then be deterred from further harvesting of 'Trichoderma-enriched' plants.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.