Field‐Based Estimation of Net Primary Productivity and Its Above‐ and Belowground Partitioning in Global Grasslands. Issue 11 (18th November 2021)

• Record Type: Journal Article
• Authors: Sun, Yuanfeng; Feng, Yuhao; Wang, Yupin; Zhao, Xia; Yang, Yuanhe; Tang, Zhiyao; Wang, Shaopeng; Su, Haojie; Zhu, Jiangling; Chang, Jinfeng; Fang, Jingyun
• Is Part Of: Journal of geophysical research. Volume 126:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-18
• Subjects: net primary productivity -- field data -- global grasslands -- random forest model -- spatial product -- driving factors; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2021JG006472
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Net primary productivity (NPP) in global grasslands is a critical component of terrestrial carbon cycling and the primary source of food for herbivores. However, the size and spatial distribution of NPP across global grasslands remain unclear, especially for belowground NPP (BNPP), which limits our understanding of above‐ and belowground carbon cycling and the assessments of herbivore food security. Here, we compiled a comprehensive grassland NPP database with 1, 467 field measurements to estimate the spatial distributions of aboveground NPP (ANPP), BNPP, total NPP (TNPP), and the fraction of BNPP ( f BNPP ) using the random forest (RF) model. The global mean grassland ANPP, BNPP, TNPP, and f BNPP were 433 ± 31 g m −2 yr −1, 593 ± 47 g m −2 yr −1, 979 ± 78 g m −2 yr −1, and 0.54 ± 0.02, respectively. The total ANPP, BNPP, and TNPP over global grasslands were 6.84 ± 0.49, 9.36 ± 0.74, and 15.46 ± 1.23 Pg C yr −1, respectively. ANPP, BNPP, and TNPP exhibited decreasing trends from low latitudes toward the poles. The spatial pattern of f BNPP was almost opposite to that of ANPP. Climate was a major determinant in shaping the spatial distribution of ANPP and TNPP, while soil and vegetation had significant impacts on that of BNPP and f BNPP . Our findings suggest that field data‐driven estimation of NPP using the RF model could be a useful approach for obtaining spatially explicit NPP of grasslands, particularly BNPP products, and that more attention should be paid on belowground and non‐climatic factors to better assess the carbon cycle in global grasslands. Key Points: A 1 km resolution net primary productivity product of global grasslands was created using field‐data and machine learning method Climate, soil, topography, and vegetation jointly influenced the spatial distribution of belowground net primary productivity More attention should be paid on belowground and non‐climatic factors to better assess the carbon cycle in global grasslands