Abstract
Nitrous oxide (N2O) is an ozone-depleting gas and important greenhouse gas (GHG) that has been implicated in global climate change. Irrigated and N-fertilized turfgrass systems cover large areas of land and emit N2O. DAYCENT and Denitrification-Decomposition (DNDC) are two widely used process-based models that predict GHG fluxes in agricultural lands. Despite many experiments applying these models to other cropping systems, they have not been applied to warm-season (C4) turfgrass systems. Our objectives were to 1) calibrate DAYCENT and DNDC for N2O emissions from ‘Meyer’ zoysiagrass (Zoysia japonica Steud., referred to as zoysia); 2) validate both models and compare their prediction accuracies; and 3) predict long-term impacts of different N fertilization and irrigation management. A combination of global sensitivity analysis and a Bayesian method was used to calibrate DAYCENT and DNDC, followed by validation, using measurements from zoysia field studies. Validation results indicated DAYCENT (R2 = 0.30–0.90; relative RMSE = 30–163%) outperformed DNDC (R2 = 0.01–0.38; relative RMSE = 119–183%) in biweekly N2O fluxes. Annual N2O emissions from DAYCENT were validated within − 54 to + 14% of estimates interpolated from measurements of different N fertilization and irrigation management, whereas DNDC simulations generally underestimated emissions by − 24 to − 85%. DAYCENT, but not DNDC, adequately simulated the impacts of irrigation and N-fertilization practices on N2O emissions in zoysia, a C4 turfgrass. DAYCENT predicted that typical N-fertilization and irrigation practices in fairway zoysia turf would reduce net global warming potential (GWP) better than no N fertilization by encouraging soil carbon sequestration in the first 30–45 years of establishment. Instead of fixed-amount or no N inputs, gradually reducing N fertilization to maintain turfgrass growth over decades would mitigate the increases of net GWP and better alleviate climate challenges in this century.
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