## 1 Introduction
(2018) [12] used Landsat-MODIS fusion data (30 m) to estimate daily APAR from NDVI and then fit an empirical model to predict ground-clipped ANHP using integrated (cumulative) growing season APAR (iAPAR) in the shortgrass prairies of Colorado, USA. Theoretically this approach could estimate daily production at 30 m resolution, although it was only validated at peak production. When sufficient ground data are available, empirical approaches such as that used by [PERSON] et al. (2018) [12] are appealing for developing more locally specific estimates of ANHP, for example, at the ranch scale. By contrast, process-based models such as RAP require a number of assumptions about biophysical parameters (e.g., light use efficiency, respiration rates) and meteorological forcing (e.g., responses to climatic stressors). These parameters can vary by plant community or even individual species, making it challenging to produce them at locally relevant scales, both because the parameters are not always known and because information on dominant plant types is not always available in a spatially explicit form. Empirical models can be a simple way to directly use the observed relationship between iAPAR and ANHP, allowing for separate models or coefficients for different dominant plant types [3; 12]. In either case, little is known about the effects of grazing by large herbivores on the relationship between satellite-derived APAR and ANHP. Herbivory can reduce, enhance, or fail to affect ANPP and ANHP, depending on plant community characteristics and the evolutionary history of herbivory [18; 19; 20]. For process-based models, herbivory likely changes some of the underlying biophysical assumptions used due to its effects on plant regrowth. However, it is also possible that grazing may change the assumed relationships between NDVI and LAI or NDVI and fPAR, which would affect both process-based and empirical models. For example, if grazing removes the upper canopy layers of vegetation and exposes lower layers with different reflectance properties, it may alter satellite-observed NDVI regardless of actual changes in production on the ground. Furthermore, while GPP estimates can be validated on the ground using flux towers which can capture the effects of grazing, it is more challenging to measure ANPP and ANHP in a grazed setting [21]. In grazed grasslands, ANHP is typically estimated from vegetation clipped in cages that exclude grazing during the current growing season. Therefore, production at the scale seen by satellites (i.e., grazed vegetation at the 30 m pixel scale) may be different from the production estimates used to calibrate and validate models (i.e., ungrazed biomass in a \(<\)1 m\({}^{2}\) cage), either as a result of under-compensation (reduced production) or over-compensation (increased production) in response to grazing [20]. In central North American grasslands, ground-based estimates of ANHP also typically exclude primary production by species such as cacti (_Opuntia polyacantlu_) and low-statured woody plants (e.g., _Guterizia sarothrae_), which comprise only a small fraction of ANPP and are not grazed by livestock. Considering the potential disparities when calibrating and developing models, we aim to expand on previous research conducted by [PERSON] et al. (2018) [12] at the Central Plains Experimental Range (CPER) on the shortgrass step of northeast Colorado, USA. Here, our main objective was to determine whether grazing management affects the relationship between ANHP and satellite-derived iAPAR while accounting for differences in plant community type. For rangelands of the western United States, plant community types are strongly driven by soil properties and processes in combination with climate, which have been used to delineate \"ecological sites\", or groups of mapped soil types that support similar plant communities and respond similarly to management and disturbance [22].