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The southern Great Plains of the USA has great potential to produce biofuel feedstock while minimizing the dual stresses of woody plant encroachment and climate change. Switchgrass (Panicum virgatum) cultivation, woody biomass captured during removal of the encroaching eastern redcedar (Juniperus virginiana) to restore grasslands and thinning of the native oak forest can provide an integrated source of feedstock and improve ecosystem services. In north‐central Oklahoma, we quantified productivity and ecosystem water use of switchgrass stands and degraded ecosystems encroached by eastern redcedar and compared these to native oak forest and tallgrass prairie ecosystems. We measured aboveground net primary productivity (ANPP) using allometric equations (trees) and clip plots (herbaceous), and evapotranspiration (ET) using a water balance approach from gauged watersheds, and calculated water use efficiency (WUE = ANPP/ET) from 2016 to 2019. Among vegetation cover types, ANPP averaged 5.1, 5.4, 6.0, and 7.8 Mg ha⁻¹ year⁻¹for the prairie, oak, eastern redcedar, and switchgrass watersheds and was significantly greater for switchgrass in 2018 and 2019 (2 and 3 years post establishment) when it reached 8.6 Mg ha⁻¹ year⁻¹. Averaged across 2017–2019, ET was significantly greater in the forested watersheds than the grassland watersheds (1022 mm year⁻¹for eastern redcedar, 1025 mm year⁻¹for oak, 874 mm year⁻¹for prairie, and 828 mm year⁻¹for switchgrass). The mean WUE was significantly greater (9.47 kg ha⁻¹ mm⁻¹) for switchgrass than for the prairie, eastern redcedar, and oak cover types (6.03, 6.02, and 5.31 kg ha⁻¹ mm⁻¹). Switchgrass offered benefits of greater ANPP, less ET, and greater WUE. Our findings indicate that an integrated biofuel feedstock system that includes converting eastern redcedar encroached areas to switchgrass and thinning the oak forest can increase productivity, increase runoff to streams, and improve ecosystem services.

Regional, automated meteorological networks, such as the Oklahoma Mesonet can potentially provide high quality forcing data for generating gridded surfaces, but proven methods of interpolating weather variables between the station locations are needed. We compared two interpolation methods, ordinary kriging (OK) and empirical Bayesian kriging (EBK), with and without using long‐term climate imprints (CI), for creating spatially continuous, daily weather datasets. Daily meteorological variables (maximum and minimum temperature, solar radiation, and precipitation) from the Oklahoma Mesonet for the period 1997–2014 were interpolated using geoprocessing tools in ArcGIS. Cross‐validation was used for evaluation of interpolation methods, with 90% of sites chosen randomly for the training set and the remaining 10% left for validation. For all interpolation approaches, cross‐validation showed coefficient of determination (R²) values of .99 and .98 for daily maximum and minimum air temperatures, with mean absolute error (MAE) ranging from ±0.45–0.50 °C for maximum temperature and ±0.77–0.80 °C for minimum temperature. Likewise, for daily solar radiation,R²values of .94 and .93 showed overall good prediction accuracy with MAE values 1.00 and 1.01 MJ m^–2 d^–1for EBK and OK, respectively. However, for rainfall, all methods yieldedR²values ≤.67, suggesting a need for more effective interpolation method. Based on its lower computational time and lower input data requirement, OK appears preferable to the other approaches tested here to provide the daily weather data for gridded models in Oklahoma and other regions with similar monitoring networks.