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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Publications at this Location » Publication #364290

Research Project: Managing Energy and Carbon Fluxes to Optimize Agroecosystem Productivity and Resilience

Location: Soil, Water & Air Resources Research

Title: Upscaling Gross Primary Production in corn-soybean rotation systems in the Midwest

Author
item DOLD, CHRISTIAN - Orise Fellow
item Hatfield, Jerry
item Prueger, John
item Moorman, Thomas - Tom
item Sauer, Thomas - Tom
item Cosh, Michael
item DREWRY, DARREN - The Ohio State University
item WACHA, KENNETH - Orise Fellow

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/13/2019
Publication Date: 7/17/2019
Citation: Dold, C., Hatfield, J.L., Prueger, J.H., Moorman, T.B., Sauer, T.J., Cosh, M.H., Drewry, D.T., Wacha, K.M. 2019. Upscaling Gross Primary Production in corn-soybean rotation systems in the Midwest. Remote Sensing. 11(14):1688. https://doi.org/10.3390/rs11141688.
DOI: https://doi.org/10.3390/rs11141688

Interpretive Summary: The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and carbon dynamics are impacted by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP in four counties in Central Iowa in the 2016 growing season (DOY 145 – 269). Eight eddy-covariance stations recorded carbon fluxes of corn (n=4) and soybean (n=4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPP was upscaled using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE (x¯ ± SE) were 678 ± 63, 1483 ± 100, and -805 ± 40 g C m-2 for corn, and 263 ± 40, 811 ± 53, and -548 ± 14 g C m-2 for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ-1 for corn and soybean, respectively. The upscaled versus EC-derived GPP had a RMSE of 2.24 and 2.81 g C m-2, for corn and soybean, respectively, which is an improvement to the MODIS GPP product. Corn yield, calculated from upscaled GPP, corresponded well to official yield data, while soybean yield was overestimated. The presented approach can increase the accuracy of regional corn and soybean GPP and grain yield estimates.

Technical Abstract: The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and carbon dynamics are impacted by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP in four counties in Central Iowa in the 2016 growing season (DOY 145 – 269). Eight eddy-covariance stations recorded carbon fluxes of corn (n=4) and soybeans (n=4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPP was upscaled using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE (x¯ ± SE) were 678 ± 63, 1483 ± 100, and -805 ± 40 g C m-2 for corn, and 263 ± 40, 811 ± 53, and -548 ± 14 g C m-2 for soybeans, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ-1 for corn and soybeans, respectively. The upscaled versus EC-derived GPP had a RMSE of 2.24 and 2.81 g C m-2, for corn and soybeans, respectively, which is an improvement to the MODIS GPP product. Corn yield, calculated from upscaled GPP, corresponded well to official yield data, while soybean yield was overestimated. The presented approach can increase the accuracy of regional corn and soybean GPP and grain yield estimates.