Abstract
Measuring the impact of livestock grazing on grassland above-ground net primary production (ANPP) is essential for grass yield estimation and pasture management. However, since there is a lack of accurate and repeatable techniques to obtain the details of grazing locations and stocking rates at the regional scale, it is an extremely challenging task to study the influence of regional grazing on the grassland ANPP. Taking Zoige County as a case, this paper proposes an approach to quantify the spatial and temporal variation of grazing intensity and grazing period through time-series remote sensing data, simulated grassland ANPP through the denitrification and decomposition (DNDC) model, and then explores the impact of grazing on grassland ANPP. The result showed that the model-estimated ANPP while considering grazing had a significant relationship with the field-observed ANPP, with the coefficient of determination (R2) of 0.75, root mean square error (RMSE) of 122.86 kgC/ha, and average relative error (RE) of 8.77%. On the contrary, if grazing activity was not considered in simulation, a large uncertainty was found when the model-estimated ANPP was compared with the field observation, showing R2 of 0.4, RMSE of 211.51 kgC/ha, and average RE of 32.5%. For the whole area of Zoige County in 2012, the statistics of the estimation showed that the total regional ANPP was up to 3.815 × 105 tC, while the total regional ANPP, without considering grazing, would be overestimated by 44.4%, up to 5.51 × 105 tC. This indicates that the grazing parameters derived in this study could effectively improve the accuracy of ANPP simulation results. Therefore, it is feasible to combine time-series remote sensing data with the process model to simulate the grazing effects on grassland ANPP. However, some issues, such as selecting proper remote sensing data, improving the quality of model input parameters, collecting more field data, and exploring the data assimilation approaches, still should be considered in the future work.
Highlights
As one of the most important living environments for human beings [1], grasslands cover approximately 24% of the world’s land surface area [2]
Grassland above-ground net primary production (ANPP) could be estimated through the ecosystem models, which have always been regarded as essential and indispensable tools in simulating net primary production (NPP), owing to their ability to describe the response of the grassland ecosystem to changing environmental conditions and human disturbances [11]
In order to evaluate the accuracy of the model simulation results, using the ground measurements and the model simulation results, we calculated the coefficient of determination (R2 ), root mean square error (RMSE), and relative error (RE) to evaluate the performance of the denitrification and decomposition (DNDC) model
Summary
As one of the most important living environments for human beings [1], grasslands cover approximately 24% of the world’s land surface area [2]. Monitoring the ecological environment changes of grassland ecosystems is critical to understand the role of grasslands in the global carbon cycle and is desirable for the local government to manage the grasslands resource. Grassland ANPP could be estimated through the ecosystem models, which have always been regarded as essential and indispensable tools in simulating net primary production (NPP), owing to their ability to describe the response of the grassland ecosystem to changing environmental conditions and human disturbances [11]. Denitrification and decomposition (DNDC) is considered as one of the most widely used and successful biogeochemical models [13], and it has been applied to simulate the amount and dynamics of carbon for almost all terrestrial ecosystems [14]. The DNDC model provides a powerful tool to estimate ANPP, significant uncertainties still exist when it is used to simulate the regional ANPP for grasslands because of the human disturbances, especially the grazing impact
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