What the study found
A gridded annual dataset of aboveground net primary productivity (ANPP, the portion of plant growth above the ground) for global natural grasslands was developed for historical and future periods. The dataset covers 1958–2023 for the historical record and 2015–2100 for future projections.
Why the authors say this matters
The authors say a long-term ANPP dataset is essential for carbon dynamics modeling and sustainable land management. The study suggests the dataset can provide a spatially explicit baseline of climate-driven productivity and help evaluate human impacts on grasslands and inform adaptive management under climate change.
What the researchers tested
The researchers used machine learning to build a gridded annual ANPP dataset. Historical ANPP data were derived from TerraClimate at 1/24° spatial resolution, and future projections came from CMIP6 models under SSP245 and SSP585 scenarios at 1/2° resolution.
What worked and what didn't
The model performed robustly, with R² = 0.675 ± 0.009, and showed temporal and spatial reliability through cross-validation with published products. The study also reported systematic ANPP underestimation in high-productivity regions above 700 g m⁻², which the authors link to sparse field observations.
What to keep in mind
The underestimation in high-productivity regions means values there should be interpreted with caution. The abstract does not describe other limitations beyond the sparse field-observation issue.
Key points
- A gridded annual ANPP dataset was developed for global natural grasslands.
- The dataset spans historical years 1958–2023 and future years 2015–2100.
- Historical data came from TerraClimate; future projections came from CMIP6 under SSP245 and SSP585.
- The model performance was reported as R² = 0.675 ± 0.009.
- ANPP was systematically underestimated in regions above 700 g m⁻² because of sparse field observations.
Disclosure
- Research title:
- Global grassland productivity dataset spans 1958–2100
- Publication date:
- 2026-02-27
- OpenAlex record:
- View
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