###### Abstract  
The rising frequency of extreme weather events and global warming are greatly challenging pastoral ecosystem productivity, particularly in the temperate climate-transition regions. While this could cause greater gross primary production (GPP) mainly contributed by the warm-season vegetation, the consequences for the dynamics of net ecosystem exchange (NEE) and hydrological responses (e.g., evapotranspiration, ET) on an ecosystem level are poorly known. Here, we investigated the evolution of plant phenology, nutritive value, energy balance, and carbon/water budgets of a cool-season dominated pastoral ecosystem in the temperate zone; integrating both eddy covariance (EC) flux measurement and simulation modeling-based uncertainty analysis. Throughout the two-year duration (2017-2018) of this study, the entire pasture ecosystem remained a strong carbon sink (NEE = \(-\)1.23 and \(-\)1.95 kg C m\({}^{-2}\), respectively) with 74% and 62% of available energy loss explained by EC fluxes, respectively. The cumulative ET was 735.8 and 796.8 mm, respectively; and the overall ecosystem water use efficiency (EWUE) were calculated as 65 g C kg\({}^{-1}\) water across both growing seasons. The above-ground biomass yield agreed with the cumulative GPP and was inversely correlated with grass nutritive value. The uncertainty analysis indicated that accurate EC flux gap-filling models could be constructed using support vector machine trained time-series models (NEE, R\({}^{2}\) = 0.77, RMSE = 11.8; ET, R\({}^{2}\) = 0.90, RMSE = 73.8). The performance benchmarking tests indicated that REddyPro-based gap-filling performance was very limiting and highly variable (NEE, R\({}^{2}\) = 0.21-0.64; ET, R\({}^{2}\) = 0.79-0.87), particularly for estimating NEE. Overall, the warm-season vegetation encroachment greatly filled the production gap of cool-season grasses, leading to greater cumulative NEE and EWUE on a system level, compared with those from many other reported field-crop or grassland studies using EC approaches. The complex and dynamic nature of grassland ecosystems greatly challenged the conventional REddyPro-based EC flux gap-filling performance. However, accurate machine learning models could be constructed for error/uncertainty control purposes and, thus, should be encouraged in future studies. eddy covariance (EC); gross primary production (GPP); net ecosystem exchange (NEE); uncertainty analysis; machine learning +
Footnote †: journal: Applicand  
**Volumes:** Number of papers (NEEE),  
and computational approaches could greatly enhance our understanding of ecosystem functioning and behavior under the changing climate conditions. Located in the middle of a climate transition zone, the middle Tennessee region has excellent weather conditions for plant production. In particular, several cool-season grass species such as tall-fescue (_Lolium arundinaceum_ (Schreb.) [PERSON]), orchardgrass (_Dactylis glomerta_ L.), and kentucky bluegrass (_Poa pratensis_ L.) are well adapted to the soil and climate conditions in this interfacing environment between the northeast and southeast vegetation zones [4; 5]. These plant species, together with many introduced and/or native warm-season grasses, make this temperate transition zone one of the most productive and diverse plant production regions in the world. Most importantly, these cool-season dominated pasture ecosystems are very sensitive to rising temperatures and drought conditions, evidenced by increasing encroachment of warm-season species in the summer, and rising frequency and intensity of insect and weed outbreaks. These characteristics provide us with excellent candidate ecosystems to investigate the dynamics and behaviors of carbon/water vapor footprints along with their interactions with various environmental/ecological factors and legacy effects. Numerous studies were conducted previously to explore how carbon fluxes and/or ecosystem productivity respond to different managerial factors or species composition within pasture ecosystems [6; 7; 8; 9; 10].