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Agriculture is being called upon to increase carbon (C) storage in soils to reduce greenhouse gas (GHG) accumulation in the atmosphere. Cropping systems research can be used to support GHG mitigation efforts, but we must quantify land management impacts using appropriate assumptions and unambiguous methods. Soil C sequestration is considered temporary because it can be re-emitted as carbon dioxide (CO2) if the effecting practice is not maintained and/or the soil–plant system is disturbed, for example, as the result of changing climate. Because of this, the climate benefit of soil C sequestration depends on the time that C is held out of the atmosphere. When assessing the net GHG impact of management practices, soil C storage is often aggregated with non-CO2 (N2O and CH4) emissions after converting all components to CO2 equivalents (CO2e) and assuming a given time horizon (TH), in what is known as stock change accounting. However, such analyses do not consider potential re-emission of soil C or apply consistent assumptions about time horizons. Here, we demonstrate that tonne-year accounting provides a more conservative estimate of the emissions offsetting potential of soil C storage compared to stock change accounting. Tonne-year accounting can be used to reconcile differences in the context and timeframes of soil C sequestration and non-CO2 GHG emissions. The approach can be applied post hoc to commonly observed cropping systems data to estimate GHG emissions offsets associated with agricultural land management over given THs and with more clearly defined assumptions.
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GC51P-0859 Distributed Carbon Dioxide Flux Assessment in Agricultural Soils: Can Robotic Rovers Monitor Gas Emissions While Carrying the Produce?
Building healthy soils that store more carbon and reduce greenhouse gas (GHG) emissions while increasing food security is a multi-pronged climate action for the world. This work examines affordable technologies for rapidly assessing soil surface efflux of carbon dioxide quickly and accurately at multiple locations over short time periods (approximately 1 hr) in agricultural fields. Soil carbon dioxide efflux or respiration rate is known to be a strong function of soil texture, moisture content, and temperature. Thus, spatiotemporal variation of the efflux signal is complex and dynamic, particularly when soil texture and irrigation patterns are heterogenous. We use a combination of computational modeling and empirical measurement to study this problem at the UC Merced Experimental Smart Farm, on a roughly 2 ha track of flood-irrigated land. Using computation model (Hydrus 1d), we simulate soil conditions and CO2 emissions for a variety of ambient temperature and irrigation conditions. We calibrated the model parameters using efflux data obtained during multiple sampling campaigns using low-cost CO2 efflux chambers. Results indicate that relatively elevated emissions occur as key soil pore pathways drain following irrigation events. The timing of these emissions depends strongly on soil texture, with tighter clayey soils causing more dramatic “hot moments” and more smoothly draining sandy soils. While initial campaigns were carried out by researchers, future campaigns are being planned in which robotic micro-tractors will be equipped with the CO2 chambers and maneuvered using path planning algorithms programmed to adequately characterize the field-scale CO2 efflux while performing their primary agricultural functions. In this context, the farmer can monitor and achieve compliance with GHG emission goals with a minimal time investment.
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- Award ID(s):
- 2326310
- PAR ID:
- 10540706
- Editor(s):
- httpsessopenarchiveorgeditorial-board
- Publisher / Repository:
- ESS Open Archive
- Date Published:
- Edition / Version:
- 1
- Volume:
- 1
- Issue:
- 1
- Page Range / eLocation ID:
- 1
- Subject(s) / Keyword(s):
- agricultural ecology environmental sciences geochemistry soil sciences robotics
- Format(s):
- Medium: X Size: 1 Other: pdf/a
- Size(s):
- 1
- Institution:
- ESS Open Archive
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.22541/essoar.172554820.06095296/v1
