Development of Best Turfgrass Management Practices Using the DAYCENT Model

Abstract

To predict the best management practices for Kentucky bluegrass (Poa pratensis L.) lawns in Colorado, the DAYCENT ecosystem model was parameterized and applied on a turfgrass ecosystem. In this study, field‐measured data on clipping yields, leaf N content, evapotranspiration (ET), deep percolation, nitrate leaching, and soil temperature from a 3‐yr lysimeter study were used for parameterization and validation. The simulation result for clipping yield was improved compared to the monthly time step CENTURY ecosystem model, with correlation coefficient (r) increased from −0.32 to 0.74. The prediction of ET and deep percolation was acceptable for the 3 yr. The long‐term irrigation and fertilization effects on Kentucky bluegrass biomass and soil C and N were also examined. We predicted a 50% reduction in the annual net production as irrigation decreases from 100% potential evapotranspiration (PET) to 60% PET in this semiarid region. The simulation result suggests that the annual fertilization rates should be gradually reduced for both moderately and highly managed lawns with increasing age of the turfgrass stand.

Simulation of Nitrous Oxide Emissions and Estimation of Global Warming Potential in Turfgrass Systems Using the DAYCENT Model

“…Although the DAYCENT model has been adjusted to simulate turfgrass ecosystems and validated using data of biomass, evapotranspiration, leaching, and soil temperature (Zhang et al, 2013), no research is available to compare the DAYCENT‐predicted N 2 O flux with measured data in turfgrass lawns. The objectives of this research were (i) to validate the DAYCENT model's ability to predict N 2 O emissions from turfgrasses by coupling field measurements of two published experiments with the DAYCENT simulations; (ii) to simulate the impact of different management practices, including a conventional management practice and a DAYCENT model‐generated BMP, on N 2 O emissions; and (iii) to determine the net GWP of turfgrass management over time by combining energy expenses associated with turfgrass maintenance and N 2 O emissions simulated in current study with SOC sequestration estimated in previous study by Zhang et al (2013).…”

Turfgrass and Climate Change

“…There have been some recent assessments of the value of turfgrass stands to sequester C and through a modeling assessment for lawns, Zirkle et al (2011) showed the potential sequestration rate varied between 25.4 to 204.3 g C m -2 yr -1 with variation among lawn management practices. Zhang et al (2013b) utilized a simulation model, DAYCENT, to evaluate potential best management practices for lawns in Colorado to demonstrate how soil C, nitrate leaching, and water use could be managed in response to the changing temperature and precipitation conditions among growing seasons. Computer simulation models can be used to evaluate the potential for turfgrass management for N 2 O emissions over the lifetime of a stand.…”

Implications of urbanization related land use change on the Carbon and Nitrogen cycle from subtropical soils

“…The necessity of organic bound nitrogen for long-term N accumulation can easily be improved by management practices such as leaving clippings behind after mowing as suggested from modelled long-term turf grass management in temperate zones (Zhang et al 2013b). Additionally, the mineral fertilizer use in turf grass systems may increase denitrification (Raciti et al 2011b) and potential N losses in form of N 2 , which then need to be compensated for with more fertilizer to ensure turf grass productivity.…”

“…However, there is potential to significantly reduce those GHG emissions from turf grassland use by modifying current management strategies. For example, modelled scenarios in the USA identified a substantial longterm benefit for GHG reduction from turf grass systems with the simple management practice of reducing fertilizer input and leaving mowed grass clipping behind instead of removing them from the ecosystem (Zhang et al 2013a;2013b). To calibrate process models to develop improved management strategies, new datasets from public and private peri-urban environments are needed from various climates and soil types to accurately predict local and global climate change impacts, which are often neglected due to limited data available (Betts 2007;IPCC 2006IPCC , 2013.…”

“…The fertilization of these sandy soils may support long-term increases in organic N in form of SOM rather than frequent mineral N supply. This necessity of organic bound nitrogen for longterm N accumulation can easily be improved by management practices such as leaving clippings behind after mowing instead of removal as suggested from modelled long-term turf grass management in temperate zones (Zhang et al 2013b). …”