Tallgrass-prairie productivity models worked well—until management got intensive
WHAT THE EVIDENCE SAYS
Researchers compared eddy-covariance-derived gross primary production with two remote-sensing/light-use-efficiency models across six native tallgrass prairie pastures of roughly 32–64 ha. The sites differed in burn interval, grazing intensity and haying. Enhanced Vegetation Index was the strongest predictor of measured GPP, and modeled GPP tracked measured dynamics well overall (R² about 0.72–0.85). But annual cumulative GPP was overestimated by roughly 15–30% in intensively grazed and hayed pastures, while a lightly grazed pasture showed only 0–2% overestimation.
WHY IT MATTERS / OUR READ
For managers using satellite productivity or carbon products, management intensity is part of the calibration problem. A model can perform well across a region yet still bias the sites where grazing, haying or species composition most strongly alter green biomass. Ground-truthing the management extremes may be more useful than assuming a single regional correction.
CAVEATS / UNCERTAINTY
The six pastures are co-located in one central Oklahoma research landscape, so management, soils and weather do not span the full tallgrass-prairie domain. GPP is carbon uptake, not net ecosystem carbon storage, forage quality or biodiversity. Management treatments and C3:C4 composition are not fully randomized experimental factors.
SOURCE
Science of the Total Environment / USDA-ARS — primary study
View original evidence ↗
Across six Oklahoma prairie pastures, remote-sensing models tracked eddy-covariance GPP well overall but overpredicted annual production by 15–30% under intensive grazing and haying.
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