Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models. (April 2017)

• Record Type: Journal Article
• Authors: Saha, Debasish; Kemanian, Armen R.; Rau, Benjamin M.; Adler, Paul R.; Montes, Felipe
• Is Part Of: Atmospheric environment. Volume 155(2017)
• Page Start: 189
• Page End: 198
• Publication Date: 2017-04
• Subjects: Cumulative nitrous oxide flux -- Simulation model -- Sampling -- Fixed interval -- Rule-based -- Decision tree; RF Random Forest -- DOY day of year -- Tavg average air temperature -- R cumulative rainfall (and irrigation) -- I net water inflow -- SIN total soil inorganic nitrogen -- T soil temperature -- θ volumetric soil water -- HI high input rule-based -- LI low input rule-based; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2017.01.052
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Annual cumulative soil nitrous oxide (N2 O) emissions calculated from discrete chamber-based flux measurements have unknown uncertainty. We used outputs from simulations obtained with an agroecosystem model to design sampling strategies that yield accurate cumulative N2 O flux estimates with a known uncertainty level. Dailys oil N2 O fluxes were simulated for Ames, IA (corn-soybean rotation), College Station, TX (corn-vetch rotation), Fort Collins, CO (irrigated corn), and Pullman, WA (winter wheat), representing diverse agro-ecoregions of the United States. Fertilization source, rate, and timing were site-specific. These simulated fluxes surrogated daily measurements in the analysis. We "sampled" the fluxes using a fixed interval (1–32 days) or a rule-based (decision tree-based) sampling method. Two types of decision trees were built: a high-input tree (HI) that included soil inorganic nitrogen (SIN) as a predictor variable, and a low-input tree (LI) that excluded SIN. Other predictor variables were identified with Random Forest. The decision trees were inverted to be used as rules for sampling a representative number of members from each terminal node. The uncertainty of the annual N2 O flux estimation increased along with the fixed interval length. A 4- and 8-day fixed sampling interval was required at College Station and Ames, respectively, to yield ±20% accuracy in the flux estimate; a 12-day interval rendered the same accuracy at Fort Collins and Pullman. Both the HI and the LI rule-based methods provided the same accuracy as that of fixed interval method with up to a 60% reduction in sampling events, particularly at locations with greater temporal flux variability. For instance, at Ames, the HI rule-based and the fixed interval methods required 16 and 91 sampling events, respectively, to achieve the same absolute bias of 0.2 kg N ha −1 yr −1 in estimating cumulative N2 O flux. These results suggest that using simulation models along with decision trees can reduce the cost and improve the accuracy of the estimations of cumulative N2 O fluxes using the discrete chamber-based method. Graphical abstract: Highlights: Nitrous oxide flux estimated from discrete measurements have unknown uncertainty. This uncertainty is location-specific for regular-interval sampling. Rule-based sampling yields better and less costly estimates than regular sampling. The performance of rule-based sampling is location and system specific.