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Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass "yield"). We evaluated the NDVI-yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l'Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI-biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R 2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program.

Effective agronomic nitrogen management strategies ensure optimum productivity, reduce nitrogen losses, and enhance economic profitability and environmental quality. Farmers in western Canada make key decisions on formulation, rate, timing, and placement of fertilizer nitrogen that are suitable for soils, weather, and farming operations within which they operate. Suitability of agronomic nitrogen management options are assessed by estimates from linear interpolations and extrapolations of temporally and spatially discrete field-plot measurements of nitrogen responses. Such estimates do not account for non-linear and offsetting biogeochemical feedbacks of nitrogen cycles and cannot provide comprehensive nitrogen budgets for alternative nitrogen management options. These limitations can be overcome by using process-based agro-ecosystem models that adequately simulate basic processes of nitrogen biogeochemical cycles and are rigorously tested against site observations. Ecosys is a process-based ecosystem model that successfully simulated the biogeochemical feedbacks among nitrogen, carbon, and phosphorus cycles across different agro-ecosystems. This study deployed ecosys to generate spatially and temporally continuous estimates to assess crop nitrogen use and agronomic nitrogen losses from the crop fields across Alberta for alternative nitrogen fertilizer management scenarios. The study simulated effects of four nitrogen management scenarios: fall banded urea, fall banded ESN (Environmentally Smart Nitrogen), spring banded urea, and spring banded ESN on nitrogen recovery and losses from barley fields on mid-slope landforms. These simulations were done at township grids of ∼10 km × 10 km over 2011-2015 utilizing provincial soil and climate datasets. Modeled annual N 2 O, N 2 , and NH 3 emissions, and nitrogen losses in surface runoff and sub-surface discharge were lower by about 25, 30, 70, and 40%, respectively, with spring banding than in fall banding across Alberta. Modeled barley yields and grain nitrogen uptake were similar in spring and fall banding, indicating agro-economic and environmental sustainability advantage of spring banding in Alberta. These modeled estimates were consistent with estimates based on plot and laboratory research for Mezbahuddin et al. Modeling Prairie Agronomic N Management Alberta and similar prairie conditions. This study pioneered a methodology of processbased agroecosystem modeling, which is replicable and scalable to assess cumulative impacts of alternative agronomic nitrogen management options on crop production and the environment on provincial, regional, federal, continental, and global scales.

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