Above- and belowground net-primary productivity: A field-based global database of grasslands

Abstract

Net primary productivity (NPP) over global grasslands is crucial for understanding the terrestrial carbon cycling and for the assessments of wild herbivores food security. During the past few decades, numerous field investigations have been conducted to estimate grassland NPP since the measuring criterion released by the International Biological Program. However, a comprehensive NPP database, particularly for belowground NPP (BNPP), in global grasslands is rare to date. Here, field NPP measurements from 438 publications (1957–2018) in global grasslands were collected, critically filtered, and incorporated in a comprehensive global database with observations for aboveground NPP (ANPP), BNPP, total NPP (TNPP), and BNPP fraction (fBNPP). Associated information on geographical locations, climatic records, grassland types, land use patterns, manipulations subjected to manipulative experiments, sampling year of study sites, as well as NPP measurement methods are also documented. This database included 2985 entries from 1785 study sites. Among them, 806 entries contained paired data of ANPP and BNPP, resulting in the 806 fBNPP data. The study sites encompassed global grasslands with latitudinal range of 54.5° S~78.9° N, longitudinal range of 157.4° W~175.8° E, and altitudes from 0 to 5168 m above sea level, covering broad climatic gradients (−17.6 to 28.8°C in mean annual temperature and 63–2052 mm in mean annual precipitation). This global database is the world's largest paired data of ANPP and BNPP field measurements in grasslands. It can be used to study the spatio-temporal patterns of NPP and its allocation, evaluate the responses of above- and below-ground carbon components to future global changes, and validate the NPP estimation by empirical or process-based models in global grasslands. The database can be freely used for noncommercial applications. We kindly request users cite this data paper when using the database, respecting all the hard work during data compilation.