A long-term gridded dataset of aboveground net primary productivity for global natural grasslands.

Ziwei Chen, Dongsheng Zhao, Zhiyuan Zhang, Liming Zhang, Du Zheng

Abstract readDataset

In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ziwei ChenUniversity Key Lab of Soil Ecosystem Health and Regulation in Fujian, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Dongsheng ZhaoKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. zhaods@igsnrr.ac.cn.

Zhiyuan ZhangUniversity Key Lab of Soil Ecosystem Health and Regulation in Fujian, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. zhangzhiyuancn@foxmail.com.

Liming ZhangUniversity Key Lab of Soil Ecosystem Health and Regulation in Fujian, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Du ZhengKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42207477National Natural Science Foundation of China (National Science Foundation of China) 42271489

6 · The paper itself

Abstract

A long-term dataset of aboveground net primary productivity (ANPP) for global natural grasslands is essential for carbon dynamics modeling and sustainable land management. However, existing datasets are limited: they often fail to separate above- and below-ground productivity or reflect only post-disturbance conditions. To address these gaps, we developed a gridded annual ANPP dataset using machine learning, spanning historical (1958-2023) and future (2015-2100) periods. Historical ANPP data were derived from TerraClimate at 1/24° spatial resolution, while future projections came from CMIP6 models under SSP245 and SSP585 scenarios at 1/2° resolution. Our model performed robustly (R

Indexed as

GrasslandClimate ChangeMachine Learning

Identifiers

PMID41748605

DOI10.1038/s41597-026-06944-7

PMCPMC13066554

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.