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Keywords

warm-season turfgrass, greenhouse gas emissions, process-based model, nitrogen, irrigation, global warming potential

Abstract

Nitrous oxide (N2O) is an important greenhouse gas (GHG) implicated in global climate change. Process-based ecosystem models, such as DAYCENT and DNDC, have been widely used to predict GHG fluxes in agricultural systems. However, neither model has yet been applied to warm-season turfgrasses such as zoysiagrass. This study parameterized, calibrated, and validated the DAYCENT and DNDC models for N2O emissions from Meyer zoysiagrass (Zoysia japonica Steud.) using Bayes’ theorem and field data from Braun and Bremer (2018a, 2019) and Lewis and Bremer (2013). Results indicated DAYCENT, but not DNDC, reasonably simulated the impacts of irrigation and N-fertilization practices on biweekly and annual N2O emissions in zoysia turfgrass. When assuming no further climate change, the validated DAYCENT model predicted that typical recommendations for N-fertilization and irrigation in zoysiagrass (a low-input turfgrass) would reduce its cumulative global warming potential (GWP) for the first 45 years after establishment by encouraging soil carbon sequestration. Thereafter, soils would become saturated with carbon and hence, reductions of N inputs would be beneficial for mitigating further increases in N2O emissions and GWP.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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