Simulating Soil Organic Carbon Stock Changes in Agroecosystems using CQESTR, DayCent, and IPCC Tier 1 Methods

Grosso, Stephen J. Del; Gollany, Hero T.; Reyes-Fox, Melissa

doi:10.2134/advagricsystmodel6.2013.0001.5

Cited by 11 publications

(10 citation statements)

References 44 publications

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“…This effort should include the implementation of a mutual feedback mechanism between land use change processes on grid‐level (LandSHIFT) and economic processes on country‐level (IMPACT) in order to simulate effects such as the influence of reduced land availability due to urbanization on land management or on market prices and trade of agricultural commodities more realistically (see Long & Qu, ). In addition, a process‐based ecosystem model could replace the IPCC Tier 1 approach to allow for more detailed analysis of soil management and climate effects of soil carbon storage (e.g., Del Grosso et al, ). At this point it is important to note that more sophisticated models typically require input data in a high level of detail that is often not available in large‐scale studies (Ruane et al, ).…”

Section: Discussionmentioning

confidence: 99%

Agricultural Development and Land Use Change in India: A Scenario Analysis of Trade‐Offs Between UN Sustainable Development Goals (SDGs)

et al. 2020

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India has the second largest population in the world and is characterized by a broad diversity in climate, topography, flora, fauna, land use, and socioeconomic conditions. To help ensure food security in the future, agricultural systems will have to respond to global change drivers such as population growth, changing dietary habits, and climate change. However, alterations of how food is produced in the future may conflict with other UN Sustainable Development Goals (SDGs), such as the protection of land resources and climate change mitigation. It is crucial for decision‐makers to understand potential trade‐offs between these goals to find a balance of human needs and environmental impacts. In this paper, we analyze pathways of agricultural productivity, land use, and land‐cover changes in India until 2030 and their impacts on terrestrial biodiversity and carbon storage. The results show that in order to meet future food production demands, agricultural lands are likely to expand, and existing farmlands need to be intensified. However, both processes will result in biodiversity losses. At the same time, the projections reveal carbon stock increases due to intensification processes and decreases due to conversions of natural land into agriculture. On balance, we find that carbon stocks increase with the scenarios of future agricultural productivity as modeled here. In conclusion, we regard further agricultural intensification as a crucial element to help ensure food security and to slow down the expansion of cropland and pasture. At the same time, policies are required to implement this intensification in a way that minimizes biodiversity losses.

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Section: Discussionmentioning

confidence: 99%

Agricultural Development and Land Use Change in India: A Scenario Analysis of Trade‐Offs Between UN Sustainable Development Goals (SDGs)

et al. 2020

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“…, Del Grosso et al. ). The functionality of DayCent is implemented in four primary submodels: plant growth and production, soil decomposition, land surface, and trace gas fluxes (Del Grosso et al.…”

Section: Methodsmentioning

confidence: 99%

Assessing precipitation, evapotranspiration, andNDVIas controls of U.S. Great Plains plant production

et al. 2019

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Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation‐related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi‐year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite‐derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April‐to‐July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April‐to‐July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May‐to‐September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county‐level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county‐level NRCS ANPP. Accounting for how different precipitation‐related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision‐making by land managers and use of grassland ANPP for biofuels.

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“…, Del Grosso et al. ). In the PPR, extensive work has been done to quantify carbon sequestration rates for restored wetlands (Gleason et al.…”

Section: Introductionmentioning

confidence: 99%

“…We applied the existing Avoided Conversion of Grassland and Shrublands to Row Crop Agriculture v1.0 methodology to the soil types across the study area to quantify the climate benefits of grassland protection in this region. We used the existing and well-tested DAYCENT model calibrated for the study area to quantify potential emissions (Hartman et al 2011, Del Grosso et al 2016). In the PPR, extensive work has been done to quantify carbon sequestration rates for restored wetlands (Gleason et al 2005(Gleason et al , 2008 and others have quantified sequestration of restored grasslands at a local scale (Phillips et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Potential carbon dioxide emission reductions from avoided grassland conversion in the northern Great Plains

2016

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Protection of lands threatened with conversion to agriculture can reduce carbon emissions. Until recently, most climate change mitigation incentive programs for avoided conversion have focused on forested ecosystems. We applied the Avoided Conversion of Grasslands and Shrublands v.1.0 (ACoGS) methodology now available through the American Carbon Registry to a threatened region of grasslands in the northern Great Plains. For all soil types across 14 counties in North and South Dakota, we used the DAYCENT model calibrated to the study area to quantify the difference in CO2 and N2O emissions under a cropping and a protection scenario, and we used formulas in the ACoGS methodology to calculate CH4 emissions from enteric fermentation under the protection scenario. We mapped the resulting GHG emissions across the entire project area. Emissions averaged 51.6 tCO2e/ha over 20 years, and with a 31% reduction for leakage and uncertainty from the ACoGS methodology, carbon offsets averaged 35.6 tCO2e/ha over 20 years. Protection of 10% of the 2.1 million unprotected ha in the project area with the highest emissions would reduce emissions by 11.7 million tCO2e over 20 years (11% of the total emissions from all unprotected grassland) and avoid a social cost of $430 million worth of CO2 emissions. These results suggest that carbon offsets generated from avoided conversion of grasslands can meaningfully contribute to climate mitigation and grassland conservation objectives.

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Simulating Soil Organic Carbon Stock Changes in Agroecosystems using CQESTR, DayCent, and IPCC Tier 1 Methods

Cited by 11 publications

References 44 publications

Agricultural Development and Land Use Change in India: A Scenario Analysis of Trade‐Offs Between UN Sustainable Development Goals (SDGs)

Agricultural Development and Land Use Change in India: A Scenario Analysis of Trade‐Offs Between UN Sustainable Development Goals (SDGs)

Assessing precipitation, evapotranspiration, andNDVIas controls of U.S. Great Plains plant production

Potential carbon dioxide emission reductions from avoided grassland conversion in the northern Great Plains

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