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ShareScore release 0.9.0
Dataset results
27 results for “agricultural emissions”
Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
<p><span>The potent greenhouse gas nitrous oxide (N</span><sub><span>2</span></sub><span>O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N</span><sub><span>2</span></sub><span>O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited </span><span><span>geographic coverage;</span></span><span> process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured </span><span><span>soil</span></span><span> parameters. We used sub-daily N</span><sub><span>2</span></sub><span>O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model that explained 65-89% of daily flux variance with very few input variables –soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and N fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. </span><span><span>This represents a 2-3 times improvement over conventional process-based models and with substantially fewer input requirements.</span></span><span> This coupled approach </span><span><span>offers promise</span></span><span> for better predictions of agricultural N</span><sub><span>2</span></sub><span>O emissions and thus more precise global models and more effective </span><span><span>agricultural mitigation interventions.</span></span></p>
Global greenhouse gas emissions from agriculture: pathways to sustainable reductions
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Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
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Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment
<p>Abstract</p> <p>Purpose</p> <p>Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA.</p> <p>Methods</p> <p>Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts.</p> <p>Results and discussion</p> <p>According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N<sub>2</sub>O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner.</p> <p>Conclusions</p> <p>Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.</p>
CORNCOB (Characterizing Oil & gas & agRicultural emissions in the Northern COlorado Basin)
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Model output data for Fujimori et al., Impacts of GHG emissions abatement measures on agricultural market and food security
<p>Model output data for "Fujimori et al., Impacts of GHG emissions abatement measures on agricultural market and food security" submitted to Nature Food</p>
Solutions to achieve net-zero emissions in irrigated agriculture
<p>The original geospatial data of the figure.S5-9.</p> <p>The dataset contains:</p> <p>-Global energy consumption and carbon emissions from irrigation . Units: Terajoule/year and Million tonnes CO2/year. </p> <p>-Energy consumption and carbon emissions with different irrigation systems. Units: Terajoule/year and Million tonnes CO2/year. </p> <p>-Global energy consumption under drip and sprinkler scenarios. Units: Terajoule/year</p>
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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)
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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.