Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Abstract

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 (R 2 ) 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 R 2 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 3.815ˆ10 5 tC, while the total regional ANPP, without considering grazing, would be overestimated by 44.4%, up to 5.51ˆ10 5 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.

Grazing exclusion—An effective approach for naturally restoring degraded grasslands in Northern China

“…Grassland restoration is a vital process that includes the restructuring of belowground properties (J. Wang, Li, & Bian, ) as root traits are often altered with grazing practices (Chen et al, ; Zhang et al, ). The root:shoot ratio, a key variable for assessing the effectiveness of grassland restoration, often differs with grazing practices (J. Wang, Li, & Bian, ), grassland type (Wang, Niu, Yang, & Zhou, ), and the characteristics of biomass partitioning between aboveground parts and roots at the community level (Yang, Fang, Ma, Guo, & Mohammat, ).…”

“…Grassland restoration is a vital process that includes the restructuring of belowground properties (J. Wang, Li, & Bian, 2016) as root traits are often altered with grazing practices (Chen et al, 2017a;Zhang et al, 2017). The root:shoot ratio, a key variable for assessing the effectiveness of grassland restoration, often differs with grazing practices (J.…”

“…The root:shoot ratio, a key variable for assessing the effectiveness of grassland restoration, often differs with grazing practices (J. Wang, Li, & Bian, 2016), grassland type (Wang, Niu, Yang, & Zhou, 2010), and the characteristics of biomass partitioning between aboveground parts and roots at the community level (Yang, Fang, Ma, Guo, & Mohammat, 2010). Figure 1b).…”

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

“…The average elevation of the study area is 3400 m above sea level. The study area is characterized by low temperature (with an annual mean temperature of 1.21 • C) and high humidity (with annual precipitation ranging from 464.8 to 862.9 mm) [30]. The objective of this study is to propose a physically-based method to estimate grassland AGB based on inverting the PROSAIL model from MODIS data.…”

“…Note that the AGB in dormancy was not zero. This is because the grassland in our study area is divided into summer pastures and winter pastures by local people [30], and the winter pastures are adhered to much withered grass to feed the livestock during winter.…”