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354 results for “Budget”
Balancing the oceanic Zn isotope budget: the key role of deep-sea pelagic sediments
<p><span>Supplementary</span><span> i</span><span>nformation</span><span> present analytical methods, figures, and tables.</span></p> <p><span><span>Supplementary Table present geochmical data of deep-sea sediments from the Indian and Pacific.</span></span></p>
Data for "STOCK EXCHANGE LISTING INDUCES SOPHISTICATION OF CAPITAL BUDGETING"
<p>This data support the following research: This article compares capital budgeting techniques employed in listed and unlisted companies in Brazil. We surveyed the Chief Financial Officers (CFOs) of 398 listed companies and 300 large unlisted companies, and based on 91 respondents, the results suggest that the CFOs of listed companies tend to use less simplistic methods more often, for example: NPV and CAPM, and that CFOs of unlisted companies are less likely to estimate the cost of equity, despite being large companies. These findings indicate that stock exchange listing may require greater sophistication of the capital budgeting process.</p>
The Seasonality of the Heat Budget on the Ross Sea Continental Shelf in a Coupled Regional Ocean-Sea Ice-Ice Shelf Model
<p>This dataset includes the model data used in our paper entitled 'The Seasonality of the Heat Budget on the Ross Sea Continental Shelf in a Coupled Regional Ocean-Sea Ice-Ice Shelf Model'</p>
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
<p><strong>Aims: </strong>Soil dissolved organic carbon (DOC) is a primary form of labile carbon in terrestrial ecosystems and therefore plays a vital role in soil carbon cycling. This study aims to quantify the budgets of soil DOC at biome- and global levels and to examine the variations in soil DOC and their environmental controls. Location: Global Time period: 1981 - 2019 Method: We compiled a global dataset and analyzed the concentration and distribution of DOC across 10 biomes.</p> <p><strong>Results: </strong>Large variations in DOC are found among biomes across space and the soil DOC concentration declines exponentially along soil depths. Tundra has the highest soil DOC concentration in 0 - 30 cm soils (453.75 (95% confidence interval: 324.95 – 633.5) mg·kg-1); whereas tropical and temperate forests have relatively lower DOC concentrations, ranging from 30.20 (24.78 - 36.80) mg·kg-1 to 54.54 (49.77 – 59.77) mg·kg-1. DOC generally accounts for < 1% of total organic carbon in soils, and DOC in 0 - 30 cm contributes more than half of total DOC in 0 - 100 cm soil profile. Furthermore, variations in DOC are primarily controlled by soil texture, moisture, and total organic carbon.</p> <p><strong>Main conclusion: </strong>A global synthesis is combined with an empirical model to extrapolate the DOC concentration along soil profiles across the globe, and global budgets of DOC are estimated as 7.20 Pg C in top 0 - 30 cm and 12.97 Pg C in 0 - 100 cm, respectively, with a considerable variation among biomes. The strong soil texture control but weak TOC control on DOC variations suggest that the investigation of physical protection of soil organic carbon might need to expand to consider the labile C in soils. The global maps of DOC concentration serve as a benchmark for validating land surface models in estimating carbon storage in soils.</p>
Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget
<p>The dataset is the boundary conditions of TM5-4DVAR inversions for COS tracer in years 2016-2019. The dataset is companion with publication: </p> <p>Ma, J., Kooijmans, L. M. J., Cho, A., Montzka, S. A., Glatthor, N., Worden, J. R., Kuai, L., Atlas, E. L., and Krol, M. C.: Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget, Atmos. Chem. Phys., 21, 3507–3529, https://doi.org/10.5194/acp-21-3507-2021, 2021.</p>
Fig. 7 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 7. Diurnal activity cycle of three gibbons (two males and one female) from 0530−1600 hours.
Fig. 9 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 9. Activity of three gibbons (two males and one female) at each height.
Components of the complete budget for SAFE intensive carbon plots
<p><strong>Description: </strong></p> <p>Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.<br> <br> These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.<br> <br> SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/113"><strong>Changing carbon dioxide and water budgets from deforestation and habitat modification</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>Sime Darby Foundation (Grant, SAFE Core data)</li> <li>European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)</li> <li>NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)</li> <li>NERC standard grant: The multi-year impacts of the 2015/2016 El Niño on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1)</li> <li>HSBC Malaysia (Grant)</li> <li>The University of Zurich (Grant)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7307449">here</a></p> <p><strong>Files: </strong>This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx</p> <p><strong>SAFE_CarbonBalanceComponents.xlsx</strong></p> <p>This file contains dataset metadata and 1 data tables:</p> <ol> <li> <p><strong>Carbon balance components data</strong> (described in worksheet Data)</p> <p>Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project</p> <p>Number of fields: 64</p> <p>Number of data rows: 11</p> <p>Fields:</p> <ul> <li><strong>ForestType</strong>: Old-growth or Logged (Field type: categorical)</li> <li><strong>SAFEPlotName</strong>: SAFE plot name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>PlotName</strong>: Plot name (used in field work) (Field type: id)</li> <li><strong>ForestPlotsCode</strong>: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id)</li> <li><strong>WoodyNPP_Stem</strong>: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_CoarseRoot</strong>: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_BranchTurnover</strong>: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric)</li> <li><strong>WoodyNPP_Total</strong>: Total woody net primary producivity (Field type: numeric)</li> <li><strong>CanopyNPP_Leaf</strong>: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Twig</strong>: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Reproductive</strong>: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Miscellaneous</strong>: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Herbivory</strong>: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric)</li> <li><strong>CanopyNPP_Total</strong>: Total canopy net primary producivty (Field type: numeric)</li> <li><strong>FineRootNPP</strong>: Fine root productivity (Field type: numeric)</li> <li><strong>TotalNPP_WithoutMycorrhiza</strong>: Total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>TotalNPP_WithMycorrhiza</strong>: Total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithoutMycorrhiza</strong>: Gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>GPP_WithMycorrhiza</strong>: Gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>R_Stem</strong>: Respiration from woody stems (Field type: numeric)</li> <li><strong>R_Leaf</strong>: Leaf Respiration (Field type: numeric)</li> <li><strong>R_FineRoots</strong>: Respiration from fine roots (Field type: numeric)</li> <li><strong>R_CoarseRoots</strong>: Respiration from coarse roots (Field type: numeric)</li> <li><strong>R_SOM</strong>: Respiration from soil organic matter (Field type: numeric)</li> <li><strong>R_Mycorrhiza</strong>: Respiration from mycorrhiza (Field type: numeric)</li> <li><strong>R_Litter</strong>: Respiration from litter layer (Field type: numeric)</li> <li><strong>R_Deadwood</strong>: Deadwood respiration (Field type: numeric)</li> <li><strong>R_auto</strong>: Total autotrophic respiration (Field type: numeric)</li> <li><strong>R_het</strong>: Total heterotrophic respiration (Field type: numeric)</li> <li><strong>R_eco</strong>: Total ecosystem respiration (Field type: numeric)</li> <li><strong>NEP_WithoutMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>NEP_WithMycorrhiza</strong>: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric)</li> <li><strong>AbovegroundBiomassCarbonStock</strong>: Plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>CoarseRootBiomassCarbonStock</strong>: Biomass carbon stock of coarse roots (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Stem</strong>: Standard error of woody stem productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_CoarseRoot</strong>: Standard error of coarse root productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_BranchTurnover</strong>: Standard error of branch turnover productivity (Field type: numeric)</li> <li><strong>SE_WoodyNPP_Total</strong>: Standard error of total woody net primary producivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Leaf</strong>: Standard error of leaf productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Twig</strong>: Standard error of twig productivity (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Reproductive</strong>: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Miscellaneous</strong>: Standard error of unidentified canopy debris (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Herbivory</strong>: Standard error of leaf productivity lost to herbivory (Field type: numeric)</li> <li><strong>SE_CanopyNPP_Total</strong>: Standard error of total canopy net primary producivty (Field type: numeric)</li> <li><strong>SE_FineRootNPP</strong>: Standard error of fine root productivity (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithoutMycorrhiza</strong>: Standard error of total net primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_TotalNPP_WithMycorrhiza</strong>: Standard error of total net primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithoutMycorrhiza</strong>: Standard error of gross primary productivity without mycorrhiza (Field type: numeric)</li> <li><strong>SE_GPP_WithMycorrhiza</strong>: Standard error of gross primary productivity including mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Stem</strong>: Standard error of respiration from woody stems (Field type: numeric)</li> <li><strong>SE_R_Leaf</strong>: Standard error of leaf Respiration (Field type: numeric)</li> <li><strong>SE_R_FineRoots</strong>: Standard error of respiration from fine roots (Field type: numeric)</li> <li><strong>SE_R_CoarseRoots</strong>: Standard error of respiration from coarse roots (Field type: numeric)</li> <li><strong>SE_R_SOM</strong>: Standard error of respiration from soil organic matter (Field type: numeric)</li> <li><strong>SE_R_Mycorrhiza</strong>: Standard error of respiration from mycorrhiza (Field type: numeric)</li> <li><strong>SE_R_Litter</strong>: Standard error of litter layer respiration (Field type: numeric)</li> <li><strong>SE_R_Deadwood</strong>: Standard error of deadwood respiration (Field type: numeric)</li> <li><strong>SE_R_auto</strong>: Standard error of total autotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_het</strong>: Standard error of total heterotrophic respiration (Field type: numeric)</li> <li><strong>SE_R_eco</strong>: Standard error of total ecosystem respiration (Field type: numeric)</li> <li><strong>SE_NEP_WithoutMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_NEP_WithMycorrhiza</strong>: Standard error of total net ecosystem productivity (Field type: numeric)</li> <li><strong>SE_AbovegroundBiomassCarbonStock</strong>: Standard error of plot above-ground biomass carbon stock (Field type: numeric)</li> <li><strong>SE_CoarseRootBiomassCarbonStock</strong>: Standard error of biomass carbon stock of coarse roots (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-08-25 to 2018-07-17</p> <p><strong>Latitudinal extent: </strong>4.1830 to 5.0700</p> <p><strong>Longitudinal extent: </strong>114.0190 to 117.8200</p>
Shortwave radiation budget under Arctic Sea ice in Earth System models
<p>Contains data and scripts of the study " Improving the representation of shortwave radiation budget under Arctic Sea ice in Earth System models using observations", submitted to Journal of Geophysical Research - Oceans. </p>
Migration strategies, performance and annual activity budget in a short-distance migrant, the common starling Sturnus vulgaris
<p><span>Migratory birds typically separate energetically demanding parts of the annual cycle like breeding, moult, and migration with some species engaging in so-called moult-migration. Moult-migration is known to occur in starlings from the northern breeding populations, however, little is known about the dynamics of this phenomenon and the costs and benefits for the involved individuals. Here, using state-of-the-art multi-sensor geolocators, we gathered information about the annual cycles of 10 starlings from two breeding sites in Latvia. We used a novel analytical approach based on atmospheric pressure measurements to reveal that all but one of the tracked individuals migrated to wintering sites in the British Isles. Tracking data exposed two separate migration strategies – (1) departure from the breeding grounds in mid-June soon after chick fledging with long stationary periods at moulting sites approx. 900 km westward (n = 5 of 10); (2) residing in close vicinity of the breeding sites up until the end of October (n = 5 of 10). Accelerometer data revealed significantly higher activity budgets during moult for the individuals exhibiting moult-migration. Furthermore, birds that underwent moult-migration arrived at the breeding sites in the following year on average 10 days later and showed significantly higher activity levels during the pre-breeding period compared to birds without moult-migration. Activity tracking also showed that 67% of all migratory flights were performed during the night, contradicting previous assumptions of starlings being predominantly diurnal migrants. Maximum recorded flight altitudes reached 2500 m.a.s.l. and the longest uninterrupted flight lasted 22.5 hours. Our results highlight energetic trade-offs of moult-migration in starling, but their downstream consequences remain to be tested.</span></p>
Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic
<p>Modeled wintertime sea ice drift, sea ice thickness, dynamic sea surface height and sea ice drift budget terms in the Arctic between 1981 and 2020.</p>
MOM5 FAFMIP datasets for the study of Tropical Pacific Ocean Heat Budget in Control, FAF-STRESS and FAF-HEAT experiments
<p>Data sets for reproducing results in "Tropical Pacific Ocean Heat Budget and the Role of Shallow and Deep Circulations" by Navarro-Labastida and Farneti, including all time-mean temperature tendencies and Meridional Overturning mass transports in density space for both Control and perturbation experiments.</p>
Future projections of surface heat budgets at Lough Feeagh
<p>ISIMIP_modifiedSimstratv212_Feeagh_v2.rar includes:</p> <p>1) modified Simstrat v212 outputs (surface heat fluxes and water temperature) for Lough Feeagh under future climate projectios (historical + RCP 2.6., 6.0 and 8.5) and natural climate (PiControl).</p> <p>2) Figures and Tables derived from outputs.</p> <p>3) Modifications in Simstratv212 code.</p> <p>The differences with the previos version are that Table 3 and Figure 5 have been updated (changes have been made to the format, not to the content).</p>
Data from: The fate of nitrogen during agricultural intensification in East Africa: nitrogen budgets in contrasting agroecosystems
<p>The intensification of agricultural systems in sub-Saharan Africa (SSA) is necessary to reduce poverty and improve food security but requires increased nutrient applications to smallholder systems. To avoid the negative consequences of intensification that result from many large-scale agricultural systems (e.g., eutrophication), we must better understand how diverse soil systems in SSA will respond to increased fertilizer applications. We tracked nitrogen (N) inputs and outputs in fertilizer trials at two maize (Zea mays)-agroecological regions of contrasting soil type. We measured maize biomass, grain yields, N leaching, and N gaseous losses from a clayey soil in Yala, Kenya, and a sandy soil in Tumbi, Tanzania, with application rates of 0, 50, 75, 100, 150, and 200 kg N ha<sup>-1</sup> yr<sup>‑1</sup> over multiple years. Using measurements of NO, N<sub>2</sub>O, NO<sub>3</sub><sup>-</sup>, biomass N, and an <sup>15</sup>N enrichment experiment, we show that N budgets in Yala were nearly always negative, meaning more N was exported in yields or lost from the system than was added in fertilizer. In Tumbi, however, N budgets were negative at lower fertilizer levels (0 and 50 kg N ha<sup>-1</sup>), but positive at higher fertilizer levels (75 and 200 kg N ha<sup>-1</sup>). At both sites, most of the N was lost through maize biomass/grain removal and N leaching (over 96% of total losses). Gas losses were a minor component of N budgets. These results highlight the importance of tailoring fertilizer recommendations to a farm's specific soil and climatic conditions. However, on these two contrasting sites, fertilizer additions at or below 50 kg N ha<sup>-1</sup> do not lead to major losses of N (via gaseous or leaching) and may be recommended at a range of sites across SSA soils in maize agroecosystems.</p>
Data from: Correcting a bias in the computation of behavioral time budgets that are based on supervised learning
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Data from: Separating the impact of individual land surface properties on the terrestrial surface energy budget in both the coupled and uncoupled land–atmosphere system
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Data from: Phenological mismatch with trees reduces wildflower carbon budgets
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Social rank, female competition for food, and behavioural time-budgets in a temperate primate multi-level society
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Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
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Diverging carbonate budgets following tropicalisation of temperate reefs
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