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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.