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27 results for “agricultural emissions”
Global energy use and carbon emissions from irrigated agriculture
<p>This repository contains supporting data for: "<strong>Global energy use and carbon emissions from irrigated agriculture"</strong></p><p>Email: qinjingxiu17@mails.ucas.ac.cn and duanweili@ms.xjb.ac.cn</p><p>The dataset contains:</p><p>-Global energy consumption and CO2 emissions from irrigation . </p><p>-Global CO2 emissions from groundwater degassing . </p><p>-Energy consumption and CO2 emissions with different irrigation and pumping systems and irrigation water sources. </p><p>-Global energy consumption and CO2 under drip and sprinkler scenarios. </p><p>-Global energy consumption and CO2 under mix electricity scenarios. </p><p>-Energy units: Terajoule (TJ); CO2 emissions units: (Tonnes CO2)</p><p>-Files are uploaded in .tif raster data. </p>
SPATIAL DIFFERENTIATION OF THE EMISSIVITY OF AGRICULTURE IN EUROPE
<p>The file contains the data used in the article: <br>DOI:10.5604/01.3001.0054.4326</p> <p>Replacements included in the file (for 2020):<br>Country<br>Item: IPCC Agriculture<br>Total emissions in tonnes<br>Emissions per hectare of agricultural land<br>Emissions per unit value of goods produced by agriculture<br>Emissions per capita</p> <p><br>Source: FAOSTAT database</p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N2O). We used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N2O fluxes. Diminished snow cover resulted in increased N2O emissions each year; over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N2O fluxes from agricultural soils subject to wintertime freezing.
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Agricultural Residue Burning Emissions 2019 v1
<p>The data provides the agricultural residue burning emission estimates over 11 agroclimatic zones of Madhya Pradesh, India, for the 2019 rabi season. It also includes supplementary information used in the emission estimation for the reader's reference. </p> <p>References:</p> <p>1. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3)https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html </p> <p>2. Gupta, P. K.; Sahai, S.; Singh, N.; Dixit, C. K.; Singh, D. P.; Sharma, C.; Tiwari, M. K.; Gupta, R. K.; Garg, S. C. Residue Burning in Rice-Wheat Cropping System: Causes and Implications. Curr. Sci. 2004, 87 (12).</p> <p>3. Jain, N.; Bhatia, A.; Pathak, H. Emission of Air Pollutants from Crop Residue Burning in India. Aerosol Air Qual. Res. 2014, 14 (1).<a href="https://doi.org/10.4209/aaqr.2013.01.0031"> https://doi.org/10.4209/aaqr.2013.01.0031</a>.</p> <p>4. Venkatramanan, V.; Shah, S.; Rai, A. K.; Prasad, R. Nexus Between Crop Residue Burning, Bioeconomy and Sustainable Development Goals Over North-Western India. Front. Energy Res. 2021, 8.<a href="https://doi.org/10.3389/fenrg.2020.614212"> https://doi.org/10.3389/fenrg.2020.614212</a>.</p> <p>5. Van Der Werf, G. R.; Randerson, J. T.; Giglio, L.; Collatz, G. J.; Mu, M.; Kasibhatla, P. S.; Morton, D. C.; Defries, R. S.; Jin, Y.; Van Leeuwen, T. T. Global Fire Emissions and the Contribution of Deforestation, Savanna, Forest, Agricultural, and Peat Fires (1997-2009). Atmos. Chem. Phys. 2010, 10 (23). <a href="https://doi.org/10.5194/acp-10-11707-2010">https://doi.org/10.5194/acp-10-11707-2010</a>.</p>
DeDuCE: Deforestation and carbon emissions due to agriculture and forestry activities from 2001-2022
<h2>Overview</h2> <p>This dataset provides country-level estimates of agriculture and forestry-driven deforestation and associated carbon emissions for the period 2001-2022. A sub-national level attribution dataset is available for Brazil. Generated by the Deforestation Driver and Carbon Emission (DeDuCE) model, it amalgamates remotely sensed datasets with extensive agricultural statistics to estimate deforestation attributable to agricultural and forestry activities globally. Developed utilizing Google Earth Engine and Python, DeDuCE comprehensively covers over 9300 unique country-commodity footprints across <strong>179 countries and 184 commodities</strong> within the specified period, presenting an unmatched scope and granularity of data.</p> <h2>Documentation</h2> <p>The manuscript detailing the dataset is currently archived at EarthArXiV: <strong><em>Singh, C., & Persson, U. M. (2024). Global patterns of commodity-driven deforestation and associated carbon emissions</em></strong>. <a href="https://doi.org/10.31223/X5T69B" target="_blank" rel="noopener">https://doi.org/10.31223/X5T69B</a></p> <p>The insights from this dataset can also be viewed at: <strong><a href="https://www.deforestationfootprint.earth" target="_blank" rel="noopener">https://www.deforestationfootprint.earth</a></strong></p> <h2>Repository contents</h2> <p>The input and output/data generated by the model are archived here at <strong>Zenodo, </strong>and their description is available in <strong>'README (files in the directory).txt'</strong>.</p> <p>The columns of the (final) dataset '<em>DeDuCE_Deforestation_attribution_v1.0.1 (2001-2022).xlsx</em>' in the folder <em><strong>'Final Attribution Results'</strong></em> represent the following:</p> <ul> <li><strong>Continent/Country group: </strong>All countries are divided into 8 geographical regions</li> <li><strong>ISO: </strong>Three-letter country codes defined by ISO</li> <li><strong>Producer country: </strong>Country of deforestation</li> <li><strong>Year: </strong>Year of deforestation, ranges from 2001-2022 </li> <li><strong>Commodity group: </strong>All commodities are divided into 11 commodity groups</li> <li><strong>Commodity: </strong>Name of commodity aligning with FAOSTAT</li> <li><strong>Deforestation attribution, unamortized (ha): </strong>Annual deforestation estimates</li> <li><strong>Deforestation risk, amortized (ha): </strong>5-year amortised deforestation estimates</li> <li><strong>Deforestation emissions excl. peat drainage, unamortized (MtCO2): </strong>Annual estimates of carbon emissions (based on AGB, BGB, deadwood, litter, soil organic carbon and carbon stock of replacing commodity)</li> <li><strong>Deforestation emissions excl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, excluding carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Peatland drainage emissions (MtCO2): </strong>Annual estimates of carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Deforestation emissions incl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, including emissions from peatland drainage</li> <li><strong>Quality Index: </strong>Flagging deforestation estimates </li> </ul> <h2>Contact</h2> <p>If you have any questions, you can contact us at: </p> <p> Chandrakant Singh and U. Martin Persson <br> <em><strong><a href="mailto:chandrakant.singh@chalmers.se;martin.persson@chalmers.se">Email</a></strong>: chandrakant.singh@chalmers.se and martin.persson@chalmers.se </em><br> Physical Resource Theory, Department of Space, Earth & Environment, <br> Chalmers University of Technology, Gothenburg, Sweden</p>
District-level Agricultural Residue Burning Emissions (DARBE) in India 2011-2020 v1.3
<p>The data provides satellite-based estimates of agricultural burned area, crop residue dry matter burned and agricultural residue burning emissions across India from 2011-2020. It also includes visualisation tools used in the emission estimation and visualisation of the data for the reader's reference.</p> <p>References: </p> <p>1. IPCC 1996, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3) https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html (accessed May 04, 2023)</p> <p>2. Jain M, Mondal P, DeFries RS, Small C, Galford GL. Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sens Environ 2013;134:210–23. https://doi.org/10.1016/j.rse.2013.02.029.</p> <p>3. Deshpande MV, Pillai D, Jain M. Detecting and quantifying residue burning in smallholder systems: An integrated approach using Sentinel-2 data. Int J Appl Earth Obs Geoinf 2022a;108:1–10. https://doi.org/10.1016/j.jag.2022.102761.</p> <p>4. Deshpande MV, Pillai D, Jain M. Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite. MethodsX 2022b;9. https://doi.org/10.1016/j.mex.2022.101741.</p> <p>5. Sahu SK, Mangaraj P, Beig G, Samal A, Chinmay Pradhan, Dash S, et al. Quantifying the high resolution seasonal emission of air pollutants from crop residue burning in India. Environ Pollut 2021;286:117165. https://doi.org/10.1016/j.envpol.2021.117165.</p>
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
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Carbon-sink potential of continuous alfalfa agriculture lowered by short-term nitrous oxide emission events
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Data from: Nitrous oxide (N2O) emissions from subsurface soils of agricultural ecosystems
Nitrous oxide (N2O) is a major greenhouse gas and cultivated soils are the most important anthropogenic source. N2O production and consumption are known to occur at depths below the A or Ap horizon but their magnitude in situ is largely unknown. At a site in SW Michigan USA we measured N2O concentrations at different soil depths and used diffusivity models to examine the importance of depth-specific production and consumption. We also tested the influence of crop and management practices on subsurface N2O production in 1) till vs. no-till, 2) a nitrogen fertilizer gradient, and 3) perennial crops including successional vegetation. N2O concentrations below 20 cm exceeded atmospheric concentrations by up to 900 times, and profile concentrations increased markedly with depth except immediately after fertilization when production was intense in the surface horizon, and in winter, when surface emissions were blocked by ice. Diffusivity analysis showed that N2O production at depth was especially important in annual crops, accounting for over 50% of total N2O production when crops were fertilized at recommended rates. At nitrogen fertilizer rates exceeding crop need, subsurface N2O production contributed 25-35% of total surface emissions. Dry conditions deepened the maximum depth of N2O production. Tillage did not. In systems with perennial vegetation, subsurface N2O production contributed <20% to total surface emissions. Results suggest that the fraction of total N2O produced in subsurface horizons can be substantial in annual crops, is low under perennial vegetation, appears to be largely controlled by subsurface nitrogen and moisture, and is insensitive to tillage.
Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands
<p>Drained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one-third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gas fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes were more evenly distributed across space than methane (CH<sub>4</sub>). In the corn system, hot moments of CH<sub>4</sub> flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N<sub>2</sub>O fluxes in the annual corn agroecosystem but was less informative for perennial alfalfa N<sub>2</sub>O fluxes or CH<sub>4</sub> fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.</p>
Livestock increasingly drove global agricultural emissions in 1910-2015
<p>supplementary material</p>
N2O and N2 emission from DE and PA agriculture
<p>N2, N2O, CO2, soil moisture (%), and headspace oxygen (%) data from two corn fields in the northeast US; a coastal site in Delawre and an inland site in Pennsylvania. Emissions were measured using the nitrogen free air recirculation method (NFARM). Measurements were made a three time points over the growing season of 2014 (post-fertilization, mid-season, and pre-harvest) from plots treated with five different of fertilizer treatments (control, urea, manure, compost, and biochar).</p>
Testing novel climate accounting identifies animal agriculture as the leading emissions sector
<p>Data and spreadsheet supporting the paper <span>"Testing novel climate accounting identifies animal agriculture as the leading emissions sector" G Wedderburn-Bisshop, 2024</span></p>
Emissions from energy used in agriculture(on-farm)
<p>The FAOSTAT domain “Energy Use’’ disseminates information on activity data, emission factors and GHG emissions from fossils fuels and electricity. Information on fossil fuels used is sourced from UNSD and IEA (see methods), covering: Gas-Diesel Oil, Coal, Natural gas (including LNG), Motor Gasoline, Fuel Oil, Liquefied Petroleum Gas (LPG); and electricity.</p>
China's agricultural greenhouse gas emission intensity and its influencing factors
<p>This dataset includes the agricultural greenhouse gas emissions intensity of China and various factors that affect agricultural greenhouse gas emissions (agricultural patent intensity, agricultural per capita value added, urbanization rate, environmental investment intensity, and urban rural income gap.</p>
Data from: Global evaluation of inhibitor impacts on ammonia and nitrous oxide emissions from agricultural soils: A meta-analysis
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Data from: Nitrous oxide (N2O) emissions from subsurface soils of agricultural ecosystems
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Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands
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