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{
    "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.&nbsp; </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).&nbsp; Modifications included\r\nsimulation of anaerobic biogeochemistry and rice growth and parameterisation of\r\npaddy rice management.&nbsp; 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.&nbsp; 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.&nbsp; The\r\ntwo simulations produced a range, which included the real flux from the grid\r\ncell with a high probability.&nbsp; The MSF\r\napproach was verified against Monte Carlo analysis for three counties or\r\nprovinces in China, Thailand or the United States.&nbsp; 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.&nbsp; 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.&nbsp;\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.&nbsp; 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.&nbsp; Rice growth is simulated through tracking\r\nphotosynthesis, reparation, tillering, C allocation and release of organic C\r\nand O2 from roots.&nbsp; The model quantifies\r\nthe production of electron donors for anaerobic soil processes, by rice root\r\nexudation and decomposition.&nbsp; CH4\r\nproduction and other reductive reactions are simulated based on the\r\navailability of electron donors and acceptors.&nbsp;\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).&nbsp; Unlike the original DNDC model, the rice\r\nadapted version predicted the negative effect of (NH4)2SO2 on CH4 emission\r\nsuccessfully.&nbsp; 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\".&nbsp; 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.&nbsp; </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.&nbsp; 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.&nbsp; 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.&nbsp;\r\nCO2-C emissions changed from 21.16-60.96 Tg under continuous flooding to\r\n16.66-48.0 with intermittent flooding.&nbsp;\r\nHowever, N2O-N emissions increased from 0.04-0.05 tp0.05-0.06 Tg\r\nN2O-N.&nbsp; Global Warming Potential (GWP)\r\ndecreased from 130.93-272.83 to 91.73-211.8 Tg CO2 equivalent.&nbsp; 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.&nbsp; This used to same model\r\nas Fumoto et al. (2008), but the model was thereafter referred to as\r\nDNDC-Rice.&nbsp; When tested on three rice\r\nfields initally, DNDC-Rice showed acceptable predictions of daily and season\r\nmethane emissions under different water regimes.&nbsp; 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.&nbsp; 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"
}

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A number of studies adapt the DNDC model
for use on rice paddy systems.&nbsp; 
The DNDC model was adapted to better
simulate greenhouse gas emissions from rice paddy ecosystems by Li et al.
(2004).&nbsp; Modifications included
simulation of anaerobic biogeochemistry and rice growth and parameterisation of
paddy rice management.&nbsp; Sensitivity analysis
by Li et al. (2004) showed that management practises could significantly affect
greenhouse gas emissions, which was affected by soil properties.&nbsp; The most sensitive management practises and
soil properties varied with greenhouse gas.
The Most Sensitive Factor (MSF) approach
was used to test the model (Li et al., 2004), where the model was run twice for
each grid cell with the minimum and maximum values of the most sensitive soil
factors observed in each grid cell.&nbsp; The
two simulations produced a range, which included the real flux from the grid
cell with a high probability.&nbsp; The MSF
approach was verified against Monte Carlo analysis for three counties or
provinces in China, Thailand or the United States.&nbsp; MSF was found to be a feasible and reliable method
61-99% of the Monte Carlo GHG fluxes were located in the MSF ranges.
Li et al. (2004) ran the adapted DNDC model
for all the rice paddies in China under two different water management
practises, continuous flooding and mid-season drainage.&nbsp; Under continuous flooding methane (CH4)
emissions from the 30 million rice ha of paddy fields was found to range
between 6.4 and 12.0 Tg CH4-C per year, whereas under midseason drainage the
CH4 flux was reduced to 1.7-7.9 Tg CH4-C.&nbsp;
However, shifting water management practise from continuous flooding to
mid-season drainage increased nitrous oxide (N2O) emissions by 0.13-0.2 Tg
N2O-N/yr and carbon dioxide (CO2) emissions were only slightly altered.&nbsp; The increase in N2O emissions offset
approximately 65% of the benefit caused by the decrease in CH4 emissions, as
N2O has a radiative forcing more than 10 times greater than CO2.DNDC was adapted by Fumoto et al. (2008) to
explicitly simulate soil processes, crop growth and methane emissions from rice
fields under a variety of climatic and agronomic conditions.&nbsp; Rice growth is simulated through tracking
photosynthesis, reparation, tillering, C allocation and release of organic C
and O2 from roots.&nbsp; The model quantifies
the production of electron donors for anaerobic soil processes, by rice root
exudation and decomposition.&nbsp; CH4
production and other reductive reactions are simulated based on the
availability of electron donors and acceptors.&nbsp;
A diffusion routine, based on conductance of tillers and CH4
concentration in soil water, simulates CH4 emission through rice. 
The adapted DNDC model produced estimates
that were consistent with observations when tested against observed CH4
emissions from 3 rice paddy sites in Japan and China with varying rice residue
management and fertilisation (Fumoto et al., 2008).&nbsp; Unlike the original DNDC model, the rice
adapted version predicted the negative effect of (NH4)2SO2 on CH4 emission
successfully.&nbsp; Although the adapted DNDC
gave good predictions of seasonal CH4 emissions, daily CH4 emissions were
inaccurate, "suggesting the models immaturity in describing soil
heterogeneity or rice-cultivar specific characteristics of CH4
transport".&nbsp; CH4 emissions in a year
of low temperatures at one site was overestimated, which indicates uncertainty
in root biomass estimates as the model does not consider temperature dependence
of leaf area development.&nbsp; 
The model can be used to quantitatively
estimate CH4 emissions from rice fields under a range of conditions (Fumoto et
al., 2008).
Pathak et al. (2005) calibrated and
validated the DNDC model against field observations in New Dehli, India.&nbsp; Predicted yield, N uptake and GHG emissions
were in agreement with those observed.
A newly compiled soil, climate and landuse
database was used to simulate GHG emissions from rice fields in India.&nbsp; Continuous flooding of 42.25ha of rice fields
resulted in modelled annual net emissions of 1.07-1.10 Tg of CH4-C, compared to
0.12-0.13 Tg CH4-C with intermittent flooding.&nbsp;
CO2-C emissions changed from 21.16-60.96 Tg under continuous flooding to
16.66-48.0 with intermittent flooding.&nbsp;
However, N2O-N emissions increased from 0.04-0.05 tp0.05-0.06 Tg
N2O-N.&nbsp; Global Warming Potential (GWP)
decreased from 130.93-272.83 to 91.73-211.8 Tg CO2 equivalent.&nbsp; Pathak et al. (2008) suggest that the model
could be used to estimate GHG emissions and the affect of management, soil and
climatic factors on GHG emissions from rice fields in India.
Fumoto et al. (2010) used DNDC-Rice to assess
the impact of Alternate Water Regimes on methane emissions from rice fields on
a region scale.&nbsp; This used to same model
as Fumoto et al. (2008), but the model was thereafter referred to as
DNDC-Rice.&nbsp; When tested on three rice
fields initally, DNDC-Rice showed acceptable predictions of daily and season
methane emissions under different water regimes.&nbsp; A GIS database (rice field area, soil
properites, daily weather and farm management) was created for the region
scale, which covered 3.2% of rice fields in the Hokkaido region of Japan.&nbsp; To use the model at national scale a database
must also be constructed for national scale, as input parameters are highly
variable

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{
 "url": "/models/16",
 "family": "DNDC",
 "title": "Rice-DNDC",
 "description": "A number of studies adapt the DNDC model\r\nfor use on rice paddy systems.&nbsp; \r\nThe DNDC model was adapted to better\r\nsimulate greenhouse gas emissions from rice paddy ecosystems by Li et al.\r\n(2004).&nbsp; Modifications included\r\nsimulation of anaerobic biogeochemistry and rice growth and parameterisation of\r\npaddy rice management.&nbsp; 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.&nbsp; The most sensitive management practises and\r\nsoil properties varied with greenhouse gas.\r\nThe 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.&nbsp; The\r\ntwo simulations produced a range, which included the real flux from the grid\r\ncell with a high probability.&nbsp; The MSF\r\napproach was verified against Monte Carlo analysis for three counties or\r\nprovinces in China, Thailand or the United States.&nbsp; 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.\r\nLi 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.&nbsp; 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.&nbsp;\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.&nbsp; 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.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.&nbsp; Rice growth is simulated through tracking\r\nphotosynthesis, reparation, tillering, C allocation and release of organic C\r\nand O2 from roots.&nbsp; The model quantifies\r\nthe production of electron donors for anaerobic soil processes, by rice root\r\nexudation and decomposition.&nbsp; CH4\r\nproduction and other reductive reactions are simulated based on the\r\navailability of electron donors and acceptors.&nbsp;\r\nA diffusion routine, based on conductance of tillers and CH4\r\nconcentration in soil water, simulates CH4 emission through rice. \r\nThe 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).&nbsp; Unlike the original DNDC model, the rice\r\nadapted version predicted the negative effect of (NH4)2SO2 on CH4 emission\r\nsuccessfully.&nbsp; 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\".&nbsp; 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.&nbsp; \r\nThe model can be used to quantitatively\r\nestimate CH4 emissions from rice fields under a range of conditions (Fumoto et\r\nal., 2008).\r\nPathak et al. (2005) calibrated and\r\nvalidated the DNDC model against field observations in New Dehli, India.&nbsp; Predicted yield, N uptake and GHG emissions\r\nwere in agreement with those observed.\r\nA newly compiled soil, climate and landuse\r\ndatabase was used to simulate GHG emissions from rice fields in India.&nbsp; 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.&nbsp;\r\nCO2-C emissions changed from 21.16-60.96 Tg under continuous flooding to\r\n16.66-48.0 with intermittent flooding.&nbsp;\r\nHowever, N2O-N emissions increased from 0.04-0.05 tp0.05-0.06 Tg\r\nN2O-N.&nbsp; Global Warming Potential (GWP)\r\ndecreased from 130.93-272.83 to 91.73-211.8 Tg CO2 equivalent.&nbsp; 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.\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.&nbsp; This used to same model\r\nas Fumoto et al. (2008), but the model was thereafter referred to as\r\nDNDC-Rice.&nbsp; When tested on three rice\r\nfields initally, DNDC-Rice showed acceptable predictions of daily and season\r\nmethane emissions under different water regimes.&nbsp; 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.&nbsp; To use the model at national scale a database\r\nmust also be constructed for national scale, as input parameters are highly\r\nvariable ",
 "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"
}

Gramp Model Instance