Upscaling regional emissions of greenhouse gases from rice cultivation: methods and sources of uncertainty

Verburg, P.H.; Bodegom, Peter M. van; Gon, H.A.C. Denier van der; Bergsma, A.R.; Breemen, N. van

doi:10.1007/978-1-4020-4443-4_7

Cited by 8 publications

(12 citation statements)

References 44 publications

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“…The within-cell heterogeneity of the model estimate will therefore be lost after averaging, which will cause errors in the model's estimation. This type of error is referred to as the "fallacy of average" (Verburg et al, 2006). In contrast, the within-cell PDF of the variation in the model variable can also be established by statistical analysis of the data and/or expert estimation (Ogle et al, 2010;IPCC, 2000).…”

Section: Within-cell Variation In Model Estimatesmentioning

confidence: 99%

“…A properly partitioned subject region should balance the differences in spatial data abundance among model inputs. If the cell size is too large, substantial spatial variation in the model input variables will be lost after within-cell averaging (van Bodegom et al, 2002;Verburg et al, 2006). Scientists tend to use the finest spatial resolution possible to express details in spatial variation in their modeling results.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Zhang

Huang

et al. 2014

Geosci. Model Dev.

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Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5-89.3 % of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44-7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3 % of the total emissions, ranging from 18.3-28.0 % for early, late and middle rice ecosystems. The 95 % confidence interval of the estimation was 4.5-8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

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Section: Within-cell Variation In Model Estimatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Zhang

Huang

et al. 2014

Geosci. Model Dev.

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“…The RWCS supplied with 75 kg N ha -1 had mean annual emissions of N2O of 1.49 kg N ha -1 , or 2.97-3.04 kg N ha -1 when supplied with >150 kg N ha -1 [26]. [38] The flooding conditions in rice cause the anaerobic decomposition of organic matter, which produces methane (CH4) in the soil [39]. Globally, rice contributes ~20% of the total CH4 emissions [40].…”

Section: Greenhouse Gas Emissionsmentioning

confidence: 99%

Agricultural Innovation and Sustainable Development: A Case Study of Rice–Wheat Cropping Systems in South Asia

et al. 2021

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The rice–wheat cropping system is the main food bowl in Asia, feeding billions across the globe. However, the productivity and long-term sustainability of this system are threatened by stagnant crop yields and greenhouse gas emissions from flooded rice production. The negative environmental consequences of excessive nitrogen fertilizer use are further exacerbating the situation, along with the high labor and water requirements of transplanted rice. Residue burning in rice has also severe environmental concerns. Under these circumstances, many farmers in South Asia have shifted from transplanted rice to direct-seeded rice and reported water and labor savings and reduced methane emissions. There is a need for opting the precision agriculture techniques for the sustainable management of nutrients. Allelopathic crops could be useful in the rotation for weed management, the major yield-reducing factor in direct-seeded rice. Legume incorporation might be a viable option for improving soil health. As governments in South Asia have imposed a strict ban on the burning of rice residues, the use of rice-specific harvesters might be a pragmatic option to manage rice residues with yield and premium advantage. However, the soil/climatic conditions and farmer socio-economic conditions must be considered while promoting these technologies in rice-wheat system in South Asia.

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“…A first source of error can be introduced by the scale and the accuracy of different land cover maps (Ellis, 2004;Bach et al, 2006;Schmit et al, 2006;Verburg et al, 2006). The high relation between land use and N 2 O emissions highlights the importance of the land cover data when carrying out N 2 O emissions inventories (Plant, 1999;Matthews et al, 2000).…”

Section: G Vilain Et Al: Budget Of N 2 O Emissions At the Watershedmentioning

confidence: 99%

Budget of N<sub>2</sub>O emissions at the watershed scale: role of land cover and topography (the Orgeval basin, France)

Vilain¹,

Garnier²,

Passy³

et al. 2012

Biogeosciences

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Abstract. Agricultural basins are the major source of N 2 O emissions, with arable land accounting for half of the biogenic emissions worldwide. Moreover, N 2 O emission strongly depends on the position of agricultural land in relation with topographical gradients, as footslope soils are often more prone to denitrification. The estimation of land surface area occupied by agricultural soils depends on the available spatial input information and resolution. Surface areas of grassland, forest and arable lands were estimated for the Orgeval sub-basin using two cover representations: the pan . In this study, we have analyzed how different landcover representations influence and introduce errors into the results of regional N 2 O emissions inventories. A further introduction of the topography concept was used to better identify the critical zones for N 2 O emissions, a crucial issue to better adapt the strategies of N 2 O emissions mitigation. Overall, we observed that a refinement of the land-cover database led to a 5 % decrease in the estimation of N 2 O emissions, while the integration of the topography decreased the estimation of N 2 O emissions up to 25 %.

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Upscaling regional emissions of greenhouse gases from rice cultivation: methods and sources of uncertainty

Cited by 8 publications

References 44 publications

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Agricultural Innovation and Sustainable Development: A Case Study of Rice–Wheat Cropping Systems in South Asia

Budget of N<sub>2</sub>O emissions at the watershed scale: role of land cover and topography (the Orgeval basin, France)

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Upscaling regional emissions of greenhouse gases from rice cultivation: methods and sources of uncertainty

Cited by 8 publications

References 44 publications

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Agricultural Innovation and Sustainable Development: A Case Study of Rice–Wheat Cropping Systems in South Asia

Budget of N&lt;sub&gt;2&lt;/sub&gt;O emissions at the watershed scale: role of land cover and topography (the Orgeval basin, France)

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Budget of N<sub>2</sub>O emissions at the watershed scale: role of land cover and topography (the Orgeval basin, France)