HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept
[
{
"url": "/models/2",
"family": "DNDC",
"title": "DNDC",
"description": "<p><p>DNDC\r\n(DeNitrification-DeComposition) is a process based model of carbon and nitrogen\r\nbiogeochemistry in agro-ecosystems. The model was developed by Professor\r\nChangsheng Li and colleagues at the University of New Hampshire, United States.\r\nThe model was originally developed to simulate nitrous oxide (N2O) emissions\r\nfrom annual cropping systems in the United States (Li et al., 1992). Since its\r\noriginal development other researchers have modified the model to adapt it to\r\nother production systems, such as rice paddies, grazed pastures and forests,\r\nand many of these modifications have been incorporated into the latest versions\r\nof the DNDC model (Giltrap et al., 2010). Adaptation has frequently involved\r\nintegration with other ecosystem models. The model can be used for simulating\r\ncrop growth, soil temperature and moisture regimes, soil carbon dynamics,\r\nnitrogen leaching, and emissions of greenhouse and trace gases including\r\nnitrous oxide (N2O), nitric oxide (NO), dinitrogen (N2), ammonia (NH3), methane\r\n(CH4) and carbon dioxide (CO2). </p>\r\n<p>In DNDC the\r\nprimary drivers (climate, soil, vegetation and management) determine the soil\r\nenvironmental factors (temperature, moisture, pH, Eh and substrate\r\nconcentration gradients). Biogeochemical processes of nitrification,\r\ndenitrification and fermentation are represented as microbially mediated\r\nprocesses regulated by the soil environmental factors. The functional\r\nrelationships were largely developed from experimental data in controlled\r\nenvironments. There are five interacting sub-models: thermal hydraulic; aerobic\r\ndecomposition; denitrification; fermentation; and plant growth (including the\r\neffects of land management). The first three sub-models are described in Li et\r\nal. (1992) while Li et al. (1994) describes the plant growth and land\r\nmanagement sub-models. A dynamic scheme describing soil redox potential\r\nevolution was added in DNDC for simulating fermentation processes (Li, 2000;\r\n2007). The methods of simulating nitrous oxide, methane and ammonia are\r\ndescribed in Li (2000; 2007). Model calculations are performed at a daily or\r\nhourly time step. </p>\r\n<p>The DNDC model can\r\nbe run in single site mode for crop rotations, or at regional scale using a GIS\r\ndatabase to manage environmental and land management data, through a\r\ncomprehensive graphical user interface which also displays summary model\r\noutput. The interface also enables Monte-Carlo uncertainty analysis at site\r\nscale for the majority of input data. Uncertainty analysis at regional scale is\r\nlimited to the Most Sensitive Factor method. </p>\r\nDNDC has been extensively\r\napplied in China and India to study greenhouse gas emissions from rice and\r\nwheat systems. DNDC has been developed to study grain and pasture lands in Australia\r\nand New Zealand, and has been developed to study forest ecosystems in Europe\r\nand the United States. The model has been used as part of the European Union\r\nnitrogen biogeochemistry projects NOFRETETE and NitroEurope and is currently is\r\nbeing used for trace gas inventory and Best Management Practice studies at\r\nsite, regional or continental scale. <br></p>",
"keywords": "Nitrous Oxide, Methane, Carbon, Greenhouse Gas, Agriculture, Soil, Ecosystem, Process Based, Model",
"principal_authors": "Changsheng Li, Steve Frolking and Tod Frolking",
"contact_name": "Changsheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Institute for the Study of Earth, Oceans and Space, University of New Hampshire, United States",
"latest_version": "",
"website": "http://www.dndc.sr.unh.edu/",
"language": "C",
"systems_supported": "Microsoft Windows",
"source_code_available": "On Request",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Cropland, Grassland, Forestry",
"submitted_by": "",
"reference_url": "",
"published_on": "1992-07-01",
"lft": 1,
"rght": 330,
"tree_id": 1,
"level": 0,
"parent": null
},
{
"url": "/models/3",
"family": "DNDC",
"title": "BE-DNDC",
"description": "<p>\r\n\t\r\n\t\r\n\t\r\n</p><p>A regional\r\nframework for calculating nitrous oxide emissions from intensive\r\nagricultural land was developed by integration of the DNDC model\r\n(version 8.3P) with regional data on soil and climate, land use and\r\nfarm practices for Belgium (Beheydt, 2006). The regional predictions\r\nof nitrous oxide emissions were based on regression equations\r\ndeveloped separately for cropland and grassland that scaled the DNDC\r\nmodel outputs. The regression equations corrected for the differences\r\nbetween simulated and measured emissions at 22 long-term field\r\nmonitoring sites in Belgium (Beheydt et al., 2007). To represent\r\nuncertainty in model inputs, the framework calculated emissions with\r\nhigh and low estimates of soil carbon content. Simulations with the\r\nmodified DNDC framework gave results that were comparable to the IPCC\r\nmethodology. </p><p></p>\r\n",
"keywords": "Nitrous Oxide, Regional Inventory, Calibration, Belgium",
"principal_authors": "Daan Beheydt",
"contact_name": "Daan Beheydt",
"contact_email": "daan.beheydt@UGent.be",
"organization": "Ghent University, Belgium",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "Cropland, Grassland",
"submitted_by": "",
"reference_url": "",
"published_on": "2006-07-01",
"lft": 2,
"rght": 17,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/4",
"family": "DNDC",
"title": "China-DNDC",
"description": "<p>A national inventory\r\nof nitrous oxide emissions from cropland in China was developed by integration\r\nof the DNDC model (version 8.3P) with county scale data on soil properties,\r\nclimate, nitrogen fertiliser use and livestock numbers - from which to derive\r\nmanure nitrogen inputs - and agricultural management (Li et al., 2001). The national total DNDC model outputs were\r\ncomparable to the IPCC methodology, although the regional patterns were\r\ndifferent - with higher losses predicted by DNDC in areas with high soil carbon\r\ncontent.<br></p>",
"keywords": "Nitrous Oxide, Regional Inventory, China",
"principal_authors": "Changsheng Li, Ligang Wang, Chaoqing Yu",
"contact_name": "Changsheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Institute for the Study of Earth, Oceans, and Space, Morse Hall, University of New Hampshire, Durham, USA.",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE",
"sectors": "Cropland",
"submitted_by": "",
"reference_url": "",
"published_on": null,
"lft": 18,
"rght": 33,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/5",
"family": "DNDC",
"title": "Crop-DNDC",
"description": "<p><p>Crop-DNDC is a process-orientated,\r\nagroecosystem model that integrated detailed crop growth algorithms with the\r\nDNDC soil biogeochemical model to better simulate carbon, nitrogen and water\r\ncycles. Crop-DNDC was developed at the\r\nCanada Center for Remote Sensing, Ottawa, and simulates crop growth through\r\ntracking physiological processes (such as phenology, leaf area index,\r\nphotosynthesis, respiration, assimilate allocation, rooting processes and\r\nnitrogen uptake) along with water stress and nitrogen stress. Thus, the model,\r\nwhich operates primarily on a daily time-step can simultaneously predict crop\r\ngrowth and soil biochemical dynamics. \r\nThe original DNDC model was developed to simulate nitrous oxide (N2O)\r\nemissions from annual cropping systems in the United States (Li et al., 1992). </p>\r\n<p>Zhang et al. (2002) describe the model as\r\nconsisting of three interacting submodels: a climatic submodel calculates water\r\ndynamics and soil temperature profile; a crop submodel simulated crop\r\nphenological development, photosynthesis, respiration, leaf area index,\r\nassimilate allocation, rooting processes and nitrogen uptake; and a soil\r\nbiogeochemistry submodel predicts decomposition, nitrification, denitrification\r\nand trace gas emissions. The soil\r\nprofile is divided into multiple layers, where analysis is conducted layer by\r\nlayer, and crop residue is incorporated into the soil at the end of each\r\ngrowing season. Further information on\r\nthe submodels can be found in Zhang et al. (2002). Required input data includes climate drivers,\r\nsoil features, crop parameters and farming practices. The output includes crop production, soil\r\ncarbon and nitrogen pools and fluxes, nitrate leaching and trace gas emissions.</p>\r\n<p>Validation was focused on the newly\r\ndeveloped parts of the Crop-DNDC model using 4 crop experiments (3 from China\r\nand 1 from the US) which included field measurements of soil moisture, leaf\r\narea index, crop biomass and nitrogen content. The Crop-DNDC model results were\r\nshown to capture the patterns of soil moisture, crop growth and soil carbon and\r\nnitrogen dynamics (Zhang et al., 2002). \r\nIn addition, sensitivity analysis by Zhang et al. (2002) revealed that\r\nmodelled results in crop yield, soil carbon dynamics and trace gas emissions\r\nwere sensitive to climate conditions, atmospheric carbon dioxide (CO2)\r\nconcentration and various farming practises. Since the model is able to\r\nsimulate crop yields, soil carbon sequestration and trace gas emissions it can\r\npotentially be used for predicting the impacts of alternative management\r\nstrategies or climate change on agricultural production and environmental\r\nsafety (Zhang et al., 2002).</p>\r\n<p>Much of the functionality in Crop-DNDC has\r\nbeen incorporated into the latest version of DNDC (DNDC9.5, 2011). </p></p>",
"keywords": "Crop, Model, Soil Biogeochemistry, Sustainable Agriculture",
"principal_authors": "Yu Zhang, Changsheng Li, Xiuji Zhaou, Berrien Moore III.",
"contact_name": "Yu Zhang",
"contact_email": "yu.zhang@ccrs.nrcan.gc.ca",
"organization": "Environmental Monitoring Section, Canada Center for Remote Sensing, Ottawa, Canada",
"latest_version": "Superseded by DNDC9.5 (2011) ",
"website": "",
"language": "Turbo C + +",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Agriculture, Cropping",
"submitted_by": "",
"reference_url": "",
"published_on": "2002-07-01",
"lft": 38,
"rght": 49,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/6",
"family": "DNDC",
"title": "DNDC-CSW",
"description": "<p>Kröbel et al. (2011)\r\nfound that DNDC (version 9.3) overpredicted the growth of above ground biomass\r\nand nitrogen during the first half of the growing season, when assessed using\r\nexperimental data from Swift Current (Saskatchewan) and St-Blaise (Quebec). Kröbel et al. (2011) therefore introduced a\r\nnew Canadian Spring Wheat (CSW) submodel to DNDC (DNDC-CSW) which had a\r\nmodified plant biomass curve, modified plant biomass fractioning curves and\r\ndynamic plant C/N ratios. Wheat root\r\ngrowth was also accounted for in DNDC-CSW as simulated soil depth was increased\r\nfrom 50 to 90cm. The new DNDC-CSW model\r\nwas found to better simulate plant biomass, nitrogen content, soil carbon\r\nchanges and inter-annual variations in crop growth for a variety of wheat rotations\r\nwhen tested at Canadian sites. The submodel was added as a stand alone section\r\nto the DNDC source code so that the model is flexible and the source code can\r\nbe copied to create other crop-specific modules. <br></p>",
"keywords": "DNDC, Model, Spring Wheat, Crop Growth, Biomass, Plant Nitrogen, Soil Moisture",
"principal_authors": "R. Köbel, W. Smith, R. Desjardins, C. Campell, N. Tremblay, C. Li, R. Zenter and B. McConkey",
"contact_name": "Roland Kröbel",
"contact_email": "kroebel@agr.gc.ca",
"organization": "Agriculture and Agri-Food Canada",
"latest_version": "",
"website": "",
"language": "C",
"systems_supported": "Windows",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Agriculture",
"submitted_by": "",
"reference_url": "",
"published_on": "2011-07-01",
"lft": 50,
"rght": 83,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/104",
"family": "DNDC",
"title": "DNDC-CAN",
"description": "<p><p>A Canadian version of DNDC (DNDC v.CAN) was\r\ndeveloped to simulate biogeochemical cycling in agro-ecosystems under cool climatic\r\nconditions and management representative of Canada. This model should also be applicable\r\nfor other temperate regions. It was first denoted as DNDC-CSW as conceived by\r\nKröbel et al. (2011) when investigating spring wheat growth in Canada using\r\nexperimental data from Swift Current, SK and St-Blaise, QC. A new sub-model was introduced into DNDC which\r\nincorporates an empirically derived spring wheat biomass curve, dynamic biomass\r\nfractioning, and dynamic plant C:N ratios. These developments helped improve\r\npredictions of spring wheat plant biomass, nitrogen content and inter-annual\r\nvariations in production in Canada. Subsequent\r\nmodel developments have led to the characterization of empirical growth curves for\r\ncorn and soybean, an improved crop growth response based on cardinal\r\ntemperatures, the inclusion of heat stress response at anthesis and improvements\r\nin crop growth response under elevated CO<sub>2</sub> conditions (Smith et al.,\r\n2013, Uzoma et al., 2015). Recent model developments\r\ninclude updated evapotranspiration algorithms (FAO-Penman Monteith) under\r\ntemperate climatic conditions along with an improved ammonia volatilization\r\nroutine for surface applied manure slurries.</p></p>",
"keywords": "",
"principal_authors": "W. Smith, B. Grant, R. Kröbel, B. Dutta, K. Congreves",
"contact_name": "Ward Smith",
"contact_email": "Ward.Smith@agr.gc.ca",
"organization": "Agriculture and Agri-Food Canada",
"latest_version": "DNDC v.CAN",
"website": "",
"language": "C",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "",
"submitted_by": "Brian Grant",
"reference_url": "",
"published_on": "2013-01-01",
"lft": 51,
"rght": 66,
"tree_id": 1,
"level": 2,
"parent": "http://gramp.ags.io/api/models/6/?format=api"
},
{
"url": "/models/7",
"family": "DNDC",
"title": "DNDC-Europe",
"description": "<p><p>The integrated DNDC-Europe model was\r\ndeveloped to assess the affect of agri-environmental policy on greenhouse gas\r\n(GHG) emissions. The large scale economic model for agriculture, CAPRI (Common\r\nAgricultural Policy Regional Impact Assessment) detailed in Britz (2005), was\r\nintegrated with the biogeochemical model DNDC (used DNDC v.8.9) to produce\r\nEuropean simulations of GHG emissions, carbon stock exchanges and nitrogen\r\nbudgets of soils. The modelling\r\nframework allows environmental impacts such as GHG emissions to be analysed in\r\nthe context of economic and social indicators provided by the CAPRI model.</p>\r\n<p>The CAPRI framework was developed to\r\ncapture the complex interaction between the agricultural market, environmental\r\npolicy, trade systems and the economic behaviours of farmers, consumers and\r\nprocessors at a regional scale and then provide a policy impact assessment on a\r\nglobal scale. The original DNDC model was developed to simulate nitrous oxide\r\n(N2O) emissions from annual cropping systems in the United States (Li et al.,\r\n1992). Linking the two models together required two key steps as outlined by\r\nLeip et al. (2008). Firstly spatially\r\nexplicit information from EU agricultural statistics covering, landuse, crop\r\nlevels and animal densities and climate were generated for a defined Homogenous\r\nSpatial Mapping Unit (HSMU). The HSMU\r\nwere defined based on information on soil, slope, landcover and administrative\r\nboundaries and encapsulated conditions that lead to similar GHG emissions in\r\nterms of agronomic practices and the environment. Secondly a database was\r\nprepared to drive DNDC with data for each HSMU to include, agricultural\r\nmanagement parameters such as fertilisation rates, irrigation, sowing dates\r\netc. in order to calculate soil nitrogen and carbon turnover. </p><br></p>",
"keywords": "Agri-environmental policy, Agricultural Soil, Biogeochemistry, Carbon Sequestration, Economic Analysis, Environmental Impact, Environmental Impact Assessment, Estimation Method, Flux Measurement, Greenhouse Gas, Modeling, Nitrogen, Policy Analysis, Europe",
"principal_authors": "A Leip, G Marchi, R Koeble, M Kempen, W Britz, C Li",
"contact_name": "A Leip",
"contact_email": "adrian.leip@jrc.it",
"organization": "European Commission - DG Joint Research Centre, Institute for Envrionment and Sustainability, Ispar, Italy",
"latest_version": "",
"website": "http://afoludata.jrc.ec.europa.eu/index.php/models/detail/240",
"language": "PC-cygwin and linux g++",
"systems_supported": "Unix",
"source_code_available": "On Request",
"model_extended_family": "",
"sectors": "Agriculture",
"submitted_by": "",
"reference_url": "",
"published_on": "2008-07-01",
"lft": 88,
"rght": 99,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/8",
"family": "DNDC",
"title": "EFEM-DNDC",
"description": "<p><p>EFEM-DNDC is a GIS-coupled\r\neconomic-ecosystem model, which simulates greenhouse gas (GHG) emissions from\r\ntypical livestock and crop production systems in Baden-Württemberg,\r\nGermany. The model is a coupling of the\r\nEFEM model (Angenendt, 2003) of economic farm production (simulates crop and\r\nlivestock production systems and GHG emissions) and the biogeochemical DNDC\r\nmodel (Li, 2000) which simulates the C and N cycle of the soils in order to\r\nsimulate disaggregated agricultural GHG emissions. </p>\r\n<p>Direct soil emissions were mainly\r\ninfluenced by nitrous oxide (NO2) emissions and were found to be highly\r\ncorrelated to N fertilisation. GHG emissions from livestock production systems\r\nwere higher than for crop production systems. A good correlation between the\r\nstocking rate and GHG emissions was found to be a useful indicator of regional\r\nemission levels. GHG mitigation measures applied at regional scale can be\r\nevaluated in terms of their environmental and economic credentials through the\r\ndevelopment of scenarios for the EFEM-DNDC model.</p></p>",
"keywords": "Greenhouse Gas Emissions, Coupled Economic-Ecosystem Model, Agricultural Production Systems, Stocking Rates",
"principal_authors": "Henry Neufeldt",
"contact_name": "Henry Neufeldt",
"contact_email": "",
"organization": "Institute for Energy and Environment, Leipzig, Germany.",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; RICE-DNDC;",
"sectors": "Agriculture, Economics",
"submitted_by": "",
"reference_url": "",
"published_on": "2005-07-01",
"lft": 102,
"rght": 115,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/1",
"family": "DNDC",
"title": "Forest-DNDC",
"description": "<p></p><p></p><p>Many forest management practices are\r\nbelieved to enhance carbon mitigation, such as the restoration of forested\r\nwetlands however there is a lack of long-term field studies. </p>\r\n<p>Forest-DNDC model which runs on a daily\r\ntime-step, links forest and soil processes and was developed to quantify carbon\r\nsequestration in and trace gas emissions from the forest ecosystem. In the core\r\nof Forest-DNDC, the PnET and DNDC models were linked together to exchange\r\ninformation on litter production, plant demand for water and N, availability of\r\nwater and N in soil. DNDC is a soil biogeochemical model that predicts soil\r\norganic matter turnover, N emissions and trace gas emissions but lacks the\r\ninclusion of detailed vegetation processes. \r\nPnET is a forest physiological model that predicts forest\r\nphotosynthesis, respiration, C allocation and litter production but contained\r\nlimited representation of soil processes. \r\nForest-DNDC also includes new features such as detailed nitrification\r\nprocesses, soil freezing and thawing, the forest litter layer and soil\r\nanaerobic biogeochemistry. </p>\r\n<p>Forest-DNDC requires input data on\r\nmeteorology, forest type and age, soil properties and forest management\r\npractises. For wetland applications\r\nwater table information is also required. \r\nThe output includes estimates of model forest growth, net ecosystem C\r\nexchange, nitrogen (N) leaching from the root zone, and fluxes of carbon\r\ndioxide (CO2), methane (CH4), nitrous oxide (N2O), nitric oxide (NO), nitrogen\r\ngas (N2) and ammonia (NH3) emissions on a daily and annual basis. Twenty forest\r\nsites in North America, Europe and Oceania have been used for validation </p>\r\n<p>Many forest management practices are\r\nbelieved to enhance carbon mitigation, such as the restoration of forested\r\nwetlands. In absence of long-term field\r\nstudy data, Forest-DNDC can be used as an alternative tool to address these\r\nissues. </p><br><p></p>\r\n",
"keywords": "Carbon Sequestration, Forest Management, Process-based Model, Trace Gas Emissions, Wetland",
"principal_authors": "Changsheng Li, Carl Tretting, Ge Sun, Steve McNulty and Klaus Butterbach-Bah",
"contact_name": "Chansheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Institute for the Study of Earth, Oceans and Space, University of New Hampshire, United States",
"latest_version": "Version 1.0",
"website": "http://www.dndc.sr.unh.edu/",
"language": "English",
"systems_supported": "Microsoft Windows",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Agriculture, Forestry",
"submitted_by": "Lucy Calrow",
"reference_url": "http://www.dndc.sr.unh.edu/",
"published_on": "2004-07-01",
"lft": 118,
"rght": 131,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/11",
"family": "DNDC",
"title": "Landscape-DNDC",
"description": "<p><p>LandscapeDNDC is partly based on the DNDC\r\nmodel and contains a series of new features \"with regard to process\r\ndescriptions, model structure and data I/O functionality.\" (Haas et al.,\r\n2012). LandscapeDNDC incorporates\r\nfunctions of DNDC, PnET-N-DNDC/Forest-DNDC and was further developed from the\r\nMoBiLE model framework (Rahn et al.,2012). \r\nLandscapeDNDC simulates carbon (C), nitrogen (N) and water related\r\nbiospehere-atmosphere-hydrospehere fluxes for forest, arable and grassland\r\necosystems. It incorporates different\r\nmanagement practises and vegetation types and allows dynamic simulation of\r\nlanduse change. Ecosystems are divided\r\ninto six substates: canopy air chemistry, microclimate, physiology, water\r\ncycle, vegetation structure and soil biogeochemistry (Haas et al., 2012). Modules, derived from physical and chemical\r\nprinciples, that describe soil environmental conditions, soil-chemistry\r\nintegrating microbial C and N turnover processes and vegetation dynamics are\r\nintegrated within the model (Rahn et al., 2012).</p>\r\n<p> The\r\nmodel can be applied at site scale and three-dimensional region\r\nsimulations. The integration of all grid cells synchronously forward in time\r\nfor regional simulations, enables efficient two-way exchange of states and easy\r\ncoupling to other spatially distributed models.</p>\r\n<p>Comparison of simulated and measured flux\r\ndata by Rahn et al. (2012) showed high agreement, however, freeze-thaw\r\nprocesses (potential strong impact of N2O emission) could not be reproduced as\r\nthe underlying processes are not included in the Landscape-DNDC.</p></p>",
"keywords": "DNDC, Greenhouse gas emissions, Inventory, LandscapeDNDC, Model Coupling, Regionalization",
"principal_authors": "",
"contact_name": "E. Haas ",
"contact_email": "edwin.haas@kit.edu",
"organization": "Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstrasse 19, Grmisch-Partenkirchen",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; RICE-DNDC;",
"sectors": "",
"submitted_by": "",
"reference_url": "",
"published_on": "2012-07-01",
"lft": 156,
"rght": 161,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/12",
"family": "DNDC",
"title": "Manure-DNDC",
"description": "<p><p>Manure-DNDC is a process based model which\r\ndescribes manure organic matter turnover and gas emissions, where the relations\r\nbetween environmental factors and reactions such as decomposition, hydrolysis,\r\nnitrification, volatilisation etc are incorporated in a computable framework in\r\norder to estimate carbon dioxide, nitrous oxide and ammonia emissions. </p>\r\n<p>Each component on a farm where manure is\r\nstored and emissions emanate (e.g., feedlot, lagoon, compost, anaerobic digester\r\nand cropping field) can be selected and integrated in the model to describe the\r\nfacilities on any given farm. The variations in environmental factors in each\r\nfacility drive the biochemical reactions (Li et al., 2012) and since the model\r\nis based upon thermodynamic principles and chemical reaction kinetics it can be\r\napplied to a variety of livestock facilities as well as cultivated soils. Manure-DNDC requires livestock herd, farm\r\nfacility specifications, daily weather, \r\nsoil and manure management practise data to be inputted to the\r\nmodel. It runs at a daily time-step for\r\nat least one year, where daily and annual pools and fluxes of C, N, P and water\r\nare outputted.</p>\r\n<p>In 2012, Li et al. published research in\r\nwhich datasets of air emissions from six US farms and one Scottish pasture were\r\nused to verify the model with sensitivity analysis showing that Manure-DNDC is\r\nable to cope with the complexity simulating air emissions from livestock\r\nfarms. The model results showed that\r\nreduction in greenhouse gas emissions by 30% and ammonia emissions by 36% was\r\npotentially possible on a New York dairy farm, through a combination in changes\r\nin feed quality, planted crop type and lagoon coverage.</p>\r\n<p>Further development of Manure-DNDC will\r\nconsider additional functionality to address nutritional functions for\r\nlivestock, indirect emissions of GHG’s (transportation, feed import and\r\nmachinery) and an economic analysis. </p></p>",
"keywords": "Ammonia Volatilization, DNDC, Farm Scale, Greenhouse Gases, Livestock Operation Systems, Manure Life Cycle",
"principal_authors": "Changsheng Li, William Salas, Ruihong Zhang, Charley Krauter, Al Rotz, Frank Mitloehner.",
"contact_name": "Changsheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Institute for the Study of Earth, Oceans, and Space, Morse Hall, University of New Hampshire, Durham, USA.",
"latest_version": "Version 2",
"website": "http://www.dndc.sr.unh.edu/",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Agriculture, Livestock",
"submitted_by": "",
"reference_url": "",
"published_on": "2012-07-01",
"lft": 162,
"rght": 177,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/13",
"family": "DNDC",
"title": "Mobile-DNDC",
"description": "<p><p>Grote et al. (2011) integrated dimensional\r\ntree growth and mortality routines with MoBiLE-PSIM, a physiologically based\r\nprocess model, to enable quantification of \r\nthe impacts of direct and indriect impacts of forest management on\r\ncarbon balances within ecosystems. </p>\r\n<p>Simulations were performed using\r\nmicroclimate, water cycle, soil nutrient dynamics, physiology and dimensional\r\nchange models combined in a MOBiLE (Grote et al., 2008) framework. The framework manages the exchange of\r\nvariables between models and modules that describe the cycling of water, carbon\r\nand nitrogen in the biosphere, atmosphere and hydrosphere. An Empirical-based Canopy Model (Grote et al.,\r\n2009a) is used to provide hourly climatic information for canopy layers and\r\nDNDC calculates soil temperature, which drives biogeochemical calculations. The DNDC water balance module is used to\r\ncalculate water availability and DNDC calculations are used to to simulate the\r\nmineralisation, nitrification and denitrification above and below ground. PSIM and DNDC were linked previously in Grote\r\net al. (2009b) by plant uptake of nitrogen. \r\nThe DNDC model used in the MoBiLE framework is able to calculate '\"water\r\npools and fluxes throughout the total rooted soil profile with soil layer\r\nspecific parameterisations\", unlike in the orginal version. A physiology\r\nbased model, PSIM, simulates further vegetation processes such as plant\r\nrespiration, senescence and allocation of carbon and nitrogen and nitrogen\r\nuptake. </p>\r\n<p>Simulations are site specific and\r\ninformation is only exchanged vertically (from the top of the vegetation to the\r\ntotal rooting depth in the soil) between time steps, as it is 1-D column\r\nmodelling. The vertical dimension is\r\ndivided into flexible vegetation layers, of equal height, and a variety of soil\r\nlayers (defined by C content, N content, field capacity, wilting point, pH,\r\nsaturated conductivity, clay and stone content, bulk density). The model operates on either a sub-daily,\r\ndaily or any multiple of day time steps. \r\nVegetation information required includes species, height, canopy length,\r\naverage diameter at 1.3m, total stem volume, total above ground biomass and\r\ntree number.</p><p><p>MoBiLE-DNDC was shown to be capable of\r\nsimulating carbon fluxes in various types of pureforests, which covered old to\r\nyoung forests and a variety of species (deciduous, evergreen, needle and broad\r\nleaved) by Grote et al. (2011). The\r\nmodel is able to quantify real management impacts on the carbon cycle (the\r\nmodel recognises losses from thinning) and there feedback effects, which signifies\r\nprogress in carbon flux modelling.</p>\r\nMoBiLE-DNDC was adapted by Wolf et al. (2012) to\r\nexamine nitrous oxide emissions during freeze-thaw events in temperate\r\necoystems, through the addition of routines that relate maximum snow height to\r\nend of season biomass (ESSB). The model\r\nwas developed to better simulate plant production, snow height and soil\r\nmoisture for steppe terrain exposed to different grazing intensities in\r\nMongolia. The new routines account for\r\ndecreased plant productivity resulting from grazing and the increase of\r\nimpedance of soil ice on soil hydraulic conductivity. Modelling impedance within MoBiLE-DNDC improved\r\nsimulation of soil water content and decreased the oxygen content in the top\r\nsoil during periods of freeze-thaw. Nitrous\r\noxide emissions were shown to decrease during spring thaw as a result of lower\r\nwater content and anaerobiosis, which was also observed in field observations. <br></p></p>",
"keywords": "Physiologically orientated modelling, Integrated modelling, Eddy-flux measurements, Tree growth, Carbon balances, Thinning Freeze-thaw, Impedance concepth, N2O, Steppe, Biogeochemical Modelling, Grazing Intensity",
"principal_authors": "Rüdiger Grote, Ralf Kiese, Thomas Grünwald, Jean-Marc Ourcival and Andre Granier ",
"contact_name": "Rüdiger Grote",
"contact_email": "ruediger.grote@kit.edu",
"organization": "Karlsruhe Insititute of Technology",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Agriculture",
"submitted_by": "",
"reference_url": "",
"published_on": "2009-07-01",
"lft": 178,
"rght": 193,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/14",
"family": "DNDC",
"title": "NEST-DNDC",
"description": "<p><p>Nest-DNDC is an integration of Northern\r\nEcosyste Soil Temperature (NEST) model and DNDC that upscales methane fluxes\r\nfrom plant communities to ecosystem scale in permafrost peatlands. Information is exchanged between the two\r\nmodels at code level, so the model is able to simulate the interaction between\r\nbiogeochemical process and soil thermal-hydrological conditions. </p>\r\n<p>NEST-DNDC simulates an ecosytem domain that\r\nis made up of many plant communities, which share common weather and geological\r\nconditions but vary in their biophysical factors. An area-weighted sum of plant community fluxes\r\ncan calculate ecosystem scale fluxes. A\r\nsimulated deep ground profile captures changes in summer thaw depth, variations\r\nof permafrost with climate and provides a lower boundary condition for water\r\ntable depth.</p>\r\n<p>NEST-DNDC is also able to siumulate upland\r\nand wetland ecosystems without permafrost. \r\nThe modelled soil profile can contain may different soil textures and\r\nlayers of varying thickness and gravel content. \r\nThe model can be applied to a wide range of ecosystems from forest to\r\ntundra, as it can model an upper- and understory of woody plants, a layer of\r\nsedges or grass and a layer of mosses.</p>\r\nChanges of bubble volume in the soil profile\r\n(based on the ideal gas law and Henry's Law) were tracked using a newly\r\ndeveloped ebullition module. Model\r\nresults were in agreement with those measured by closed chamber and eddy\r\ncovariance method for the Lena River Delta, Russia. Zhang et al. (2012) found the model to be\r\ncapable of upscaling methane fluxes to larger scales.<br></p>",
"keywords": "NEST-DNDCDNDC, methane flux, modle, NEST, peatland, permafrost, upscale",
"principal_authors": "Yu Zhang, Torsten Sachs, Changsheng Li and Julia Biokes",
"contact_name": "Changsheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, Canada",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "",
"submitted_by": "",
"reference_url": "",
"published_on": "2012-07-01",
"lft": 196,
"rght": 209,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/15",
"family": "DNDC",
"title": "NZ-DNDC",
"description": "<p><p>NZ-DNDC is a modified version of DNDC that\r\nincludes a number of alterations to best reflect the conditions found in New\r\nZealand and was developed by a team at Landcare Research in New Zealand. The\r\npresence of distinctive and diverse soil types within a short distance and\r\nsoils having a higher organic carbon content than the world average; coupled\r\nwith climatic conditions and grazed pastoral systems which differ from many\r\nother countries meant that the application of the DNDC model to New Zealand was\r\nchallenging. </p>\r\n<p>Originally, the relationship between air\r\nand soil temperatures in the DNDC model was based on northern hemisphere\r\nconditions. This was altered for the New\r\nZealand model, based on calculations using New Zealand specific national soil\r\nand air temperature datasets. The Water\r\nFilled Pore Space (WFPS) threshold value to switch dentrification on/off was\r\nalso altered in this version of the DNDC from a value to 35% to the field\r\ncapacity value. Additionally, the model\r\nallows soil saturation by simulating drainage followed by infiltration (the\r\nreverse of the DNDC), which is representative of the winter months. </p>\r\n<p>Annual pasture growth rates vary with\r\nseason in New Zealand, with growth rates typically highest in spring and lowest\r\nin winter. A multiplicative day-length\r\nfactor was introduced into NZ-DNDC to reflect the seasonal changes and produce\r\nN uptake rates typical of New Zealand (more N is taken up on longer days, less\r\non shorter days). Excretal-N inputs\r\nfrom grazing animals were also included in the model and are described further\r\nin Saggar et al. (2004).</p>\r\n<p>Total yearly N2O emission estimates from\r\nboth grazed and ungrazed pastures in New Zealand were found to be within the\r\nuncertainty ranges of measured data. The\r\nmodel reflected observations due to climatic variations in rainfall and\r\ndifferences in soil texture on field scale experiments; measured emissions\r\nchanged with varying soil moisture and were approximately 20% higher in silt\r\nloam soil (poorly drained) than in sandy loam soil (well drained). Saggar et\r\nal. (2004) found the model to effectively simulate most of the patterns in WFPS\r\nand nitrous oxide (N2O) emissions observed on grazed and ungrazed land. The model represented real variability in\r\nthe processes regulating N2O emissions and is suitable for simulating emissions\r\nfor a range of New Zealand grazed pastures. </p></p>",
"keywords": "Excretal inputs, Grazed Pastures, IPCC methodology, New Zealand Modified DNDC, Nitrous Oxide, Temperate Grasslands",
"principal_authors": "S. Saggar, R. M. Andrew, K.R. Tate, C.C.Hedley, N.J. Rodda and J. A Townsend",
"contact_name": "S. Saggar",
"contact_email": "saggars@Landcareresearch.co.nz",
"organization": "Landcare Research, Palmerston North, New Zealand.",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "Agriculture, Grassland",
"submitted_by": "",
"reference_url": "",
"published_on": "2004-07-01",
"lft": 210,
"rght": 225,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/9",
"family": "DNDC",
"title": "PnET-N-DNDC",
"description": "<p><p>PnET-DNDC is a\r\nprocess based model that integrates three existing models (PnET, DNDC and a\r\nnitrification model) with several additional features. PnET is a forest physiology model that predicts\r\nphotosynthesis, respiration, organic carbon production and allocation and\r\nlitter production for forest ecosystems. \r\nDNDC is a process based model of carbon and nitrogen biogeochemistry in\r\nagro-ecosystems and the nitrification model was developed for prediction of\r\nnitrifier growth/death rates, nitrification rate and nitrifaction-induced\r\nnitric oxide (NO) and nitrous oxide (N2O) production (F. Stange, 2000b). </p>\r\n<p>Importantly\r\nPnET-DNDC is able to model soils where aerobic and anerobic microsites exist\r\nsimultaneously, as it can predict both nitrification and denitrification in the\r\nsoil at the same time (Li et al. 2000a). \r\nThe kinetic framework and interacting fractions of the model link\r\necological drivers to trace gas emissions.</p>\r\n<p>The PnET-DNDC\r\nmodel is described by Li et al. (2000a) as having two components: the first was\r\nestablished to predict the effects of ecological drivers (climate, soil,\r\nvegetation and anthropogenic activity) on soil environmental factors\r\n(temperature, moisture, pH, redox potential and substrates concentrations). This component has a further three sub models\r\nwhich predict soil climate, forest growth and turnover of organic matter. The second component predicts the effects of\r\nthe soil environmental factors on the biochemical or geochemical factors that\r\ncontrol nitric oxide (NO) and nitrous oxide (N2O) production and consumption,\r\nwhich then contain two sub models for nitrification and denitrification. </p>\r\nA kinetic scheme was developed\r\nto calculate the anaerobic status of the soil and divide the soil into aerobic\r\nand anaerobic fractions. Nitrification\r\ncan only occur in the aerobic fraction and denitrification in the anaerobic\r\nfraction. Li et al. (2000a) describe\r\nthis as a dynamic \"anaerobic balloon\" within a soil matrix. The balloon size is determined by the\r\nsimulated oxygen partial pressure, which is calculated from oxygen diffusion\r\nand consumption rates in the soil. As\r\nthe balloon swells and shrinks the model dynamically allocates substrates,\r\nincluding dissolved organic carbon (DOC), ammonium (NH4+), nitrate (NO3-), into\r\nthe aerobic and anaerobic soil fractions. \r\nWhen the balloon swells more substrates (DOC, NH4+, NO3-, NO and N20)\r\nwill be allocated to the anaerobic microsites for denitrification, less DOC and\r\nNH4+ will be left in the aerobic microsites <span style=\"line-height: 1.45em;\">for nitrification\r\nand the pathway for denitrification gas products to leave the balloon will become longer. The trends are reversed as the balloon\r\nshrinks. Rainfall duration, soil plus\r\nroot respiration and soil texture affect the anaerobic volumetric fraction as a\r\nresult of their effects on oxygen diffusion and oxygen consumption.</span>\r\n<p>Partial pressue of\r\noxygen (pO2) is estimated in the forest soil profile by a one-dimensional soil\r\noxygen diffusion algorithm. The soil\r\nprofile is divided into a series of layers and the oxygen flux between these is\r\ndetermined by the soil pO2 gradients, where oxygen diffusion rate is driven by\r\nsoil gradient and texture (Li et al. 2000a).</p>\r\n<p>Stange et al.\r\n(2000b) reported that PnET-N-DNDC can be successfully used to predict N2O and\r\nNO emissions from a broad range of temperature forest sites, based on the\r\nresults obtained from sensitivity analysis and model validation with field data\r\n(from several forest sites in the United States and Europe).</p>\r\nPnET-N-DNDC has since been\r\nintegrated with WETLAND-DNDC to produce FOREST-DNDC (Giltrap et al., 2010).<br></p>",
"keywords": "Emission, Forest Soil, Nitric Oxide, Nitrous Oxide",
"principal_authors": "Changsheng Li, John Aber, Florian Stange, Klaus Butterbach-Bahl and Hans Papen",
"contact_name": "Changsheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Institute for the Study of Earth, Oceans and Space, University of New Hampshire, United States",
"latest_version": "Superseded by Forest-DNDC",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; FOREST-DNDC-TROPICA; DNDC-RICE;",
"sectors": "Agriculture, Forestry",
"submitted_by": "",
"reference_url": "",
"published_on": "2000-07-01",
"lft": 226,
"rght": 273,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/10",
"family": "DNDC",
"title": "Forest-DNDC-Tropica",
"description": "<p><p>PnET-N-DNDC was modified by Kiese et al.\r\n(2005) to produce reliable estimates of N2O emissions from tropical rainforest\r\necosystems, a significant source of global N2O. \r\nDifferences in forest growth and soil hydrological properties between\r\ntemperate and tropical regions meant the model required adaption prior to\r\napplication in tropical regions, although the general structure of the original\r\nmodel was maintained. </p>\r\n<p>New parameterisations associated with plant\r\nphysiology and soil hydrology were added to PnET-DNDC as well as algorithms\r\nrelating to biological fixation of N, representing the effects of heavy\r\nrainfall damage and of water stress on daily leaf litterfall. Soil moisture conditions drive a dentrifier\r\nactivity index which influences N turnover by denitrification. A biological N fixation algorithm was also\r\nadded. Tree growth functions were also modified in Forest-DNDC-Tropica to\r\nenable growth to occur throughout the year as it is not limited by temperature\r\n(Kiese et al., 2005).</p>\r\n<p>Daily N2O emissions simulated by Kiese et\r\nal. (2005) were in agreement with field observations in the wet tropics of\r\nAustralia and Costa Rica and the model reproduced dynamic N2O patterns during\r\nthe wet season. The model was shown to\r\nbe sensitive to soil properties (pH, clay content, soil organic carbon) and\r\nclimatic factors (rainfall and temperature). \r\nThe tool may be used to scale up N2O emissions from site to regional\r\nscale, in order to improve regional or global N2O inventories. Kiese et al.\r\n(2005) recognise that further validation against detailed observed data would\r\nbe desirable and that Forest-DNDC-Tropica should be networked with hydrological\r\nmodels in the future. </p>\r\n<p>Some further development of the\r\nForest-DNDC-Tropica model developed by Kiese et al. (2005) was then made by\r\nWerner (2007). Werner et al. (2007)\r\nprovides further details of the three sub-models which comprise\r\nForest-DNDC-Tropica and which simulate soil climate, soil decomposition and\r\nforest growth. The distribution of soil\r\norganic carbon for the Australian rainforest soils was revised. Pedo-transfer functions which are specific to\r\ntropical soils and vital for simulating soil hydrology were added. Parameters\r\nthat relate to wood mass, leaf mass and floor mass were updated with externally\r\nsupplied values specifically calibrated for tropical rainforests (Werner et\r\nal., 2006). The model was also revised\r\nby Werner (2007) to comply with ANSI C++. </p></p>",
"keywords": "Biogeochemical Model, GIS, N2O Emission, PnET-DNDC, Regionalization, Sensitivity Analysis, Tropical Forest",
"principal_authors": "Ralf Kiese, Changsheng Li, David W. Hilbert, Hans Paper and Klaus Butterbach-Bahl",
"contact_name": "Klaus Butterbach-Bahl",
"contact_email": "",
"organization": "Institute for Meteorology/Climate Res., Atmospheric Environmental Research, Karlsruhe Research Centre, Kreuzeckbahnstrasse 19, Garmisch-Partenkirchen, Germany.",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "UK-DNDC; NZ-DNDC; LANDSCAPE-DNDC; DNDC-EUROPE; RICE-DNDC;",
"sectors": "Agriculture, Forestry",
"submitted_by": "",
"reference_url": "",
"published_on": "2005-07-01",
"lft": 227,
"rght": 242,
"tree_id": 1,
"level": 2,
"parent": "http://gramp.ags.io/api/models/9/?format=api"
},
{
"url": "/models/18",
"family": "DNDC",
"title": "Wetland DNDC",
"description": "<p><p>The PnET-N-DNDC process-orientated model\r\nwhich simulates C and N dynamics in upland forest ecosystems and the FLATWOODS\r\n(Sun et al., 1998) distributed hydrological model have been integrated to\r\ncreate the Wetland-DNDC model. The main\r\nstructure of Wetland-DNDC is taken from PnET-N-DNDC (Li et al., 2000), with\r\nseveral additional functions and algorithms developed for Wetland-DNDC to\r\nrepresent features unique to wetland ecosystems such as anaerobic conditions,\r\ngrowth of mosses and herbaceous plants and water table dynamics. The model is capable of predicting carbon\r\nbiogeochemical cycles in wetland ecosystems through the integration of the\r\nprimary drives of climate, hydrology, soil and vegetation (Zhang et al. 2002). </p>\r\n<p>Zhang et al. (2002) describes the model as\r\nconsisting of four interacting components: hydrological conditions, soil\r\ntemperature, plant growth and soil C dynamics. \r\nInitial conditions need to be set (e.g. for plant biomass, soil\r\nporosity, soil C content, and water table position). In addition the climate\r\ndrivers are inputs to the model and some model parameters (e.g., lateral\r\ninflow/outflow parameters, maximum photosynthesis rate). The model output\r\nincludes C pools and fluxes and thermal/hydrological conditions.</p>\r\n<p>The model was validated by Zhang et al.\r\n(2002) against observations from three wetland sites in North America, which\r\nwere in agreement with measurements of water table dynamics, soil temperature,\r\nmethane (CH4) fluxes, Net Ecosystem Productivity (NEP) and annual carbon\r\nbudgets. Plant photosynthesis capacity,\r\ninitial soil C content, air temperature and water outflow parameters were shown\r\nto be the most critical input factors for C dynamics in wetland ecosystems\r\nthrough sensitivity analysis.</p><p><p>Wetland-DNC has had additional enhancements\r\nby (Li et al 2004) to enable changes in management practices that affect carbon\r\nsequestration to be represented, such as forest harvest, tree planting,\r\nchopping and burning and water management.</p></p></p>",
"keywords": "Carbon Cycles, Hydrology, Methane Emissions, Mode, Wetland",
"principal_authors": "Yu Zhang, Changsheng Li, Jianbo Cui, Carl Trettin and Harbin Li",
"contact_name": "Yu Zhang",
"contact_email": "yu.zhang@ccrs.nrcan.gc.ca",
"organization": "Environmental Monitoring Section, Canada Center for Remote Sensing, Ottawa, Canada",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "Agriculture, Wetlands",
"submitted_by": "",
"reference_url": "",
"published_on": "2002-07-01",
"lft": 257,
"rght": 272,
"tree_id": 1,
"level": 2,
"parent": "http://gramp.ags.io/api/models/9/?format=api"
},
{
"url": "/models/16",
"family": "DNDC",
"title": "Rice-DNDC",
"description": "<p><p>A number of studies adapt the DNDC model\r\nfor use on rice paddy systems. </p>\r\n<p>The DNDC model was adapted to better\r\nsimulate greenhouse gas emissions from rice paddy ecosystems by Li et al.\r\n(2004). Modifications included\r\nsimulation of anaerobic biogeochemistry and rice growth and parameterisation of\r\npaddy rice management. Sensitivity analysis\r\nby Li et al. (2004) showed that management practises could significantly affect\r\ngreenhouse gas emissions, which was affected by soil properties. The most sensitive management practises and\r\nsoil properties varied with greenhouse gas.</p>\r\n<p>The Most Sensitive Factor (MSF) approach\r\nwas used to test the model (Li et al., 2004), where the model was run twice for\r\neach grid cell with the minimum and maximum values of the most sensitive soil\r\nfactors observed in each grid cell. The\r\ntwo simulations produced a range, which included the real flux from the grid\r\ncell with a high probability. The MSF\r\napproach was verified against Monte Carlo analysis for three counties or\r\nprovinces in China, Thailand or the United States. MSF was found to be a feasible and reliable method\r\n61-99% of the Monte Carlo GHG fluxes were located in the MSF ranges.</p>\r\n<p>Li et al. (2004) ran the adapted DNDC model\r\nfor all the rice paddies in China under two different water management\r\npractises, continuous flooding and mid-season drainage. Under continuous flooding methane (CH4)\r\nemissions from the 30 million rice ha of paddy fields was found to range\r\nbetween 6.4 and 12.0 Tg CH4-C per year, whereas under midseason drainage the\r\nCH4 flux was reduced to 1.7-7.9 Tg CH4-C. \r\nHowever, shifting water management practise from continuous flooding to\r\nmid-season drainage increased nitrous oxide (N2O) emissions by 0.13-0.2 Tg\r\nN2O-N/yr and carbon dioxide (CO2) emissions were only slightly altered. The increase in N2O emissions offset\r\napproximately 65% of the benefit caused by the decrease in CH4 emissions, as\r\nN2O has a radiative forcing more than 10 times greater than CO2.</p><p>DNDC was adapted by Fumoto et al. (2008) to\r\nexplicitly simulate soil processes, crop growth and methane emissions from rice\r\nfields under a variety of climatic and agronomic conditions. Rice growth is simulated through tracking\r\nphotosynthesis, reparation, tillering, C allocation and release of organic C\r\nand O2 from roots. The model quantifies\r\nthe production of electron donors for anaerobic soil processes, by rice root\r\nexudation and decomposition. CH4\r\nproduction and other reductive reactions are simulated based on the\r\navailability of electron donors and acceptors. \r\nA diffusion routine, based on conductance of tillers and CH4\r\nconcentration in soil water, simulates CH4 emission through rice. </p>\r\n<p>The adapted DNDC model produced estimates\r\nthat were consistent with observations when tested against observed CH4\r\nemissions from 3 rice paddy sites in Japan and China with varying rice residue\r\nmanagement and fertilisation (Fumoto et al., 2008). Unlike the original DNDC model, the rice\r\nadapted version predicted the negative effect of (NH4)2SO2 on CH4 emission\r\nsuccessfully. Although the adapted DNDC\r\ngave good predictions of seasonal CH4 emissions, daily CH4 emissions were\r\ninaccurate, \"suggesting the models immaturity in describing soil\r\nheterogeneity or rice-cultivar specific characteristics of CH4\r\ntransport\". CH4 emissions in a year\r\nof low temperatures at one site was overestimated, which indicates uncertainty\r\nin root biomass estimates as the model does not consider temperature dependence\r\nof leaf area development. </p>\r\n<p>The model can be used to quantitatively\r\nestimate CH4 emissions from rice fields under a range of conditions (Fumoto et\r\nal., 2008).</p>\r\n<p>Pathak et al. (2005) calibrated and\r\nvalidated the DNDC model against field observations in New Dehli, India. Predicted yield, N uptake and GHG emissions\r\nwere in agreement with those observed.</p>\r\n<p>A newly compiled soil, climate and landuse\r\ndatabase was used to simulate GHG emissions from rice fields in India. Continuous flooding of 42.25ha of rice fields\r\nresulted in modelled annual net emissions of 1.07-1.10 Tg of CH4-C, compared to\r\n0.12-0.13 Tg CH4-C with intermittent flooding. \r\nCO2-C emissions changed from 21.16-60.96 Tg under continuous flooding to\r\n16.66-48.0 with intermittent flooding. \r\nHowever, N2O-N emissions increased from 0.04-0.05 tp0.05-0.06 Tg\r\nN2O-N. Global Warming Potential (GWP)\r\ndecreased from 130.93-272.83 to 91.73-211.8 Tg CO2 equivalent. Pathak et al. (2008) suggest that the model\r\ncould be used to estimate GHG emissions and the affect of management, soil and\r\nclimatic factors on GHG emissions from rice fields in India.</p>\r\nFumoto et al. (2010) used DNDC-Rice to assess\r\nthe impact of Alternate Water Regimes on methane emissions from rice fields on\r\na region scale. This used to same model\r\nas Fumoto et al. (2008), but the model was thereafter referred to as\r\nDNDC-Rice. When tested on three rice\r\nfields initally, DNDC-Rice showed acceptable predictions of daily and season\r\nmethane emissions under different water regimes. A GIS database (rice field area, soil\r\nproperites, daily weather and farm management) was created for the region\r\nscale, which covered 3.2% of rice fields in the Hokkaido region of Japan. To use the model at national scale a database\r\nmust also be constructed for national scale, as input parameters are highly\r\nvariable<br></p>",
"keywords": "Biogeochemical ModelingDecomposition, Electron Donors, Greenhouse, Climate Change, Methane, Nitrous Oxide",
"principal_authors": "Tamon Fumoto, Kazuhiko Kobayashi, Changsheng Li and Toshihiro Hasengawa.",
"contact_name": "T. Fumoto",
"contact_email": "tamon@affrc.go.jp",
"organization": "National Agricultural Research Organization, Kannondai 3-1-3, Tsukuba, Japan",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "Agriculture",
"submitted_by": "",
"reference_url": "",
"published_on": "2008-07-01",
"lft": 274,
"rght": 295,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/17",
"family": "DNDC",
"title": "UK-DNDC",
"description": "<p><p>UK-DNDC is a modification of DNDC, the\r\nprocess based model of carbon and nitrogen biogeochemistry in agro-ecosystems,\r\ndesigned specifically for application in the UK. This involved the addition of UK-specific\r\ninput data to the DNDC database and simulation of daily C and N inputs from\r\ngrazing animals and applied animal waste. The UK-DNDC model simulates N2O\r\nemissions from 18 crop types (crop height, optimum yield, C:N ratio of grain,\r\nroot and shoot and water requirement are parameterised) on the 3 dominant soil\r\ntypes in each county (Brown et al., 2002). \r\n</p>\r\n<p>An\r\nadditional irrigation module was included in UK-DNDC, which specified\r\nirrigation for each crop type (using county and crop specific statistics). The livestock element of the database was\r\nalso significantly modified from DNDC, details of which can be found in Brown\r\net al. (2002).</p>\r\n<p>UK-DNDC\r\nwas compared with 16 datasets of measured field experiments by Brown et al.\r\n(2002) and was found to generally be in agreement. Field scale validation of the model showed\r\nthat predictions matched observations well.</p>\r\n<p>The\r\nUK-DNDC was used to provide a national inventory of N2O emissions in 1990 and\r\nhas the advantage of taking contrasting soil, crop, climate and farming\r\npractises into account, unlike the traditional IPCC approach. This makes the model an ideal platform for\r\ninvestigating the effect of a number of different scenarios of N2O emissions\r\n(Brown et al., 2002). The 1990 inventory\r\nof N2O emissions from UK agriculture was estimated as 50.9Gg (31.7Gg from soil,\r\n5.9Gg from animals and 13.2Gg from the indirect sector). When these figures were compared to those\r\ncalculated by the IPCC methodology; emissions from soil and the indirect\r\nsectors were found to smaller with the UK-DNDC approach whereas emissions from\r\nthe livestock sector were larger.</p></p>",
"keywords": "Agricultural cropland, Grassland, Greenhouse gas, Inventory, IPCC",
"principal_authors": "",
"contact_name": "Lorna Brown",
"contact_email": "lorna.brown@bbsrc.ac.uk",
"organization": "Institute of Grassland, Environmental Research, Okehampton, Devon, United Kingdom",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "Agriculture",
"submitted_by": "",
"reference_url": "",
"published_on": "2002-07-01",
"lft": 298,
"rght": 313,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/100",
"family": "DNDC",
"title": "US Cropland Greenhouse Gas Calculator",
"description": "<p>A decision support system for quantifying impacts of management alternatives on greenhouse gas emissions from agro-ecosystems in the U.S.<br></p>",
"keywords": "",
"principal_authors": "Changsheng Li",
"contact_name": "Changsheng Li",
"contact_email": "changsheng.li@unh.edu",
"organization": "Institute for the Study of Earth, Oceans and Space, University of New Hampshire, United States",
"latest_version": "",
"website": "",
"language": "",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "",
"sectors": "",
"submitted_by": "",
"reference_url": "",
"published_on": null,
"lft": 322,
"rght": 329,
"tree_id": 1,
"level": 1,
"parent": "http://gramp.ags.io/api/models/2/?format=api"
},
{
"url": "/models/101",
"family": "ECOSSE",
"title": "ECOSSE",
"description": "<p><p>Estimate Carbon in Organic Soils –Sequestration and Emissions (ECOSSE):</p><p>Whilst a few models have been developed to describe deep peat formation and turnover (for example Clymo 1992), until recently, none had been developed that were suitable for examining the impacts of land use and climate change on the types of thin organo-mineral soils that are often subject to land use change (Smith et al. 2007). These organo-mineral soils have a thin surface organic horizon <50 cm thick. The main aim of the ECOSSE model is to simulate the impacts of land use and climate change on GHG emissions from these types of soils, as well as mineral and peat soils. The model is a) driven by commonly available meteorological, land use and soil data, b) able to predict the impacts of land-use change and climate change on C and N stores in organic and mineral soils, and c) able to function at national scale as well as field scale, so allowing results to be used to directly inform policy decisions.</p><p>The ECOSSE was developed from concepts originally derived for mineral soils in the RothC (Jenkinson & Rayner 1977, Jenkinson et al. 1987, Coleman & Jenkinson 1996) and SUNDIAL (Bradbury et al. 1993, Smith et al. 1996a) models. Following these established models, ECOSSE uses a pool type approach, describing soil organic matter (SOM) as pools of inert organic matter (IOM), humus (HUM), biomass (BIO), resistant plant material (RPM) and decomposable plant material (DPM). All of the major processes of C and N turnover in the soil are included in the model, but each of the processes is simulated using only simple equations driven by readily available input variables, allowing the model to be applied at both field and national scales, without a great loss of accuracy. ECOSSE differs from RothC and SUNDIAL in the addition of descriptions of a number of processes and impacts that are not crucial in the mineral arable soils that these models were originally developed for. More importantly, ECOSSE differs from RothC and SUNDIAL in the way that it makes full use of the limited information that is available to run models at national scale. In particular, measurements of soil C are used to interpolate the activity of the SOM and the plant inputs needed to achieve those measurements. If data are available describing soil water, plant inputs, nutrient applications and timing of management operations, these can be used to drive the model and so better apportion the factors determining the interpolated activity of the SOM. However, if any of this information is missing, the model can still provide adequate simulations of SOM turnover, although the impact of changes in conditions will be estimated with less accuracy due to the reduced detail of the inputs. A complete and detailed description of the structure and formulation of the ECOSSE model is given in Smith et al. (2010). </p><a href=\"http://yellow:10888/media/files/ECOSSE_User_manual_310810.pdf\">ECOSSE User Manual</a><br></p>",
"keywords": "",
"principal_authors": "Jo Smith",
"contact_name": "Jo Smith",
"contact_email": "jo.smith@abdn.ac.uk",
"organization": "University of Aberdeen, Scotland, UK",
"latest_version": "6.2",
"website": "",
"language": "Fortran",
"systems_supported": "",
"source_code_available": "",
"model_extended_family": "ECOSSE",
"sectors": "",
"submitted_by": "Jagadeesh Yeluripati",
"reference_url": "",
"published_on": "2015-06-04",
"lft": 1,
"rght": 48,
"tree_id": 4,
"level": 0,
"parent": null
},
{
"url": "/models/102",
"family": "ECOSSE",
"title": "RothC",
"description": "<p><p>RothC is a model for the turnover of organic carbon in non-waterlogged topsoil that allows for the effects of soil type, temperature, soil moisture and plant cover on the turnover process. It uses a monthly time step to calculate total organic carbon (t ha-1), microbial biomass carbon (t ha-1) and Δ14C (from which the equivalent radiocarbon age of the soil can be calculated) on a years to centuries timescale. It requires few inputs and those it needs are easily obtainable.</p><p>RothC was originally developed and parameterized to model the turnover of organic C in arable topsoil from the Rothamsted long-term field experiments - hence the name. Later, it was extended to model turnover in grassland and in woodland and to operate in different soils and under different climates. It has now been widely tested and used at the plot, field, regional and global scales using data from many long-term experiments, different regions, and counties throughout the world.</p><p>RothC is designed to run in two modes: ‘forward’ in which known inputs are used to calculate changes in soil organic matter and ‘inverse’, when inputs are calculated from known changes in soil organic matter.</p><p>Recent developments include a version for volcanic soils, dry soils and a carbon in the subsoil version (RothPC).</p></p>",
"keywords": "",
"principal_authors": "Kevin Coleman and David Jenkinson�",
"contact_name": "Kevin Coleman and David Jenkinson�",
"contact_email": "kevin.coleman@rothamsted.ac.uk",
"organization": "Rothamsted Research, UK�",
"latest_version": "",
"website": "http://www.rothamsted.ac.uk/sustainable-soils-and-grassland-systems/rothamsted-carbon-model-rothc�",
"language": "Fortran",
"systems_supported": "MIcrosoft Windows or DOS",
"source_code_available": "On Request",
"model_extended_family": "",
"sectors": "",
"submitted_by": "",
"reference_url": "",
"published_on": "1996-01-01",
"lft": 18,
"rght": 31,
"tree_id": 4,
"level": 1,
"parent": "http://gramp.ags.io/api/models/101/?format=api"
},
{
"url": "/models/103",
"family": "ECOSSE",
"title": "Sundial",
"description": "<p><p>SUNDIAL is a dynamic computer model of nitrogen turnover in the crop/soil system. It incorporates current scientific knowledge on the individual processes of nitrogen turnover and integrates these processes to simulate what happens in the whole soil. SUNDIAL requires readily available information on soil type, cropping history and weather data as model inputs. It is widely used by scientists to interpret the results of field experiments, in particular the effects of crop management, soil type and different weather patterns on nitrate leaching (SUNDIAL the Research tool). SUNDIAL is also being developed into a Fertiliser Recommendation System (SUNDIAL-FRS), a management tool for farmers, growers and advisors, which will provide nitrogen fertiliser recommendations on a field-by-field basis, aiming to minimise nitrate leaching whilst achieving the desired yield.</p><p>SUNDIAL is a dynamic model with a weekly description of the effects of different weather patterns, soil and crop types on nitrogen turnover. SUNDIAL is designed to be used in a ‘carry-forward’ mode, with information from one years run providing inputs for the next. This allows it to investigate more complex systems involving rotations of crops and planning timescales of several years (modelling whole farm systems). SUNDIAL is also used in modelling nitrate leaching from whole catchments (catchment modelling).The SUNDIAL model can be used to predict nitrate losses by simulating the dynamics of nitrogen turnover in the soil/crop system. SUNDIAL-FRS can be used by farmers and advisors to devise a management strategy to minimise nitrate losses for individual farms.</p></p>",
"keywords": "",
"principal_authors": "",
"contact_name": "Sustainable Soils and Grassland Systems Department ",
"contact_email": "",
"organization": "Rothamsted Research, UK",
"latest_version": "",
"website": "",
"language": "Fortran",
"systems_supported": "DOS",
"source_code_available": "On Request",
"model_extended_family": "",
"sectors": "",
"submitted_by": "",
"reference_url": "",
"published_on": "1996-09-01",
"lft": 32,
"rght": 47,
"tree_id": 4,
"level": 1,
"parent": "http://gramp.ags.io/api/models/101/?format=api"
}
]
API