Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models

Debasish Saha, Armen Ricardo Kemanian, Benjamin M. Rau, Paul R. Adler, Felipe Montes

Research output: Contribution to journalArticle

Abstract

Annual cumulative soil nitrous oxide (N2O) 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 N2O flux estimates with a known uncertainty level. Daily soil N2O 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 N2O 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−1yr−1in estimating cumulative N2O 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 N2O fluxes using the discrete chamber-based method.

Original languageEnglish (US)
Pages (from-to)189-198
Number of pages10
JournalAtmospheric Environment
Volume155
DOIs
StatePublished - Jan 1 2017

Fingerprint

agricultural ecosystem
nitrous oxide
sampling
simulation
maize
inorganic nitrogen
soil
ecoregion
flux measurement
soybean
wheat
method
decision
winter
cost

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Atmospheric Science

Cite this

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title = "Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models",
abstract = "Annual cumulative soil nitrous oxide (N2O) 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 N2O flux estimates with a known uncertainty level. Daily soil N2O 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 N2O 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−1yr−1in estimating cumulative N2O 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 N2O fluxes using the discrete chamber-based method.",
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Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models. / Saha, Debasish; Kemanian, Armen Ricardo; Rau, Benjamin M.; Adler, Paul R.; Montes, Felipe.

In: Atmospheric Environment, Vol. 155, 01.01.2017, p. 189-198.

Research output: Contribution to journalArticle

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