History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019

Abstract

Abstract. Excessive anthropogenic nitrogen (N) inputs to the biosphere have disrupted the global nitrogen cycle. To better quantify the spatial and temporal patterns of anthropogenic N inputs, assess their impacts on the biogeochemical cycles of the planet and the living organisms, and improve nitrogen use efficiency (NUE) for sustainable development, we have developed a comprehensive and synthetic dataset for reconstructing the History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere. The HaNi dataset takes advantage of different data sources in a spatiotemporally consistent way to generate a set of high-resolution gridded N input products from the preindustrial period to the present (1860–2019). The HaNi dataset includes annual rates of synthetic N fertilizer, manure application/deposition, and atmospheric N deposition on cropland, pasture, and rangeland at a spatial resolution of 5 arcmin × 5 arcmin. Specifically, the N inputs are categorized, according to the N forms and land uses, into 10 types: (1) NH4+-N fertilizer applied to cropland, (2) NO3--N fertilizer applied to cropland, (3) NH4+-N fertilizer applied to pasture, (4) NO3--N fertilizer applied to pasture, (5) manure N application on cropland, (6) manure N application on pasture, (7) manure N deposition on pasture, (8) manure N deposition on rangeland, (9) NHx-N deposition, and (10) NOy-N deposition. The total anthropogenic N (TN) inputs to global terrestrial ecosystems increased from 29.05 Tg N yr−1 in the 1860s to 267.23 Tg N yr−1 in the 2010s, with the dominant N source changing from atmospheric N deposition (before the 1900s) to manure N (in the 1910s–2000s) and then to synthetic fertilizer in the 2010s. The proportion of synthetic NH4+-N in fertilizer input increased from 64 % in the 1960s to 90 % in the 2010s, while synthetic NO3--N fertilizer decreased from 36 % in the 1960s to 10 % in the 2010s. Hotspots of TN inputs shifted from Europe and North America to East and South Asia during the 1960s–2010s. Such spatial and temporal dynamics captured by the HaNi dataset are expected to facilitate a comprehensive assessment of the coupled human–Earth system and address a variety of social welfare issues, such as the climate–biosphere feedback, air pollution, water quality, and biodiversity. The data are available at https://doi.org/10.1594/PANGAEA.942069 (Tian et al., 2022).

Data‐driven estimation of nitric oxide emissions from global soils based on dominant vegetation covers

“…However, this information was difficult to obtain with global spatial coverage. Second, our analysis did not consider N fertilizer inputs to grasslands due to limited observations after N fertilization, which may lead to underestimations of grassland emissions, as managed pastures and forests receive some N fertilization (Bian et al, 2021; Tian, Bian, et al, 2022). Third, although we used an ensemble of three methods (i.e., RF, GBM, and RBF) to estimate soil NO emissions and compared their performances using a DNDC model, our results may not reflect seasonal variations, especially for rainfed regions.…”

Central Role of Nitrogen Fertilizer Relative to Water Management in Determining Direct Nitrous Oxide Emissions From Global Rice‐Based Ecosystems

“…First and foremost, better global gridded data sets on crop‐specific N input are crucial to accurately estimate the spatiotemporal heterogeneity of rice‐based N 2 O emissions, particularly regarding the key driver, anthropogenic N inputs (R. Xu et al., 2020). The current study used global N fertilizer and manure application data representing the crop‐area‐weighted average N fertilizer and manure rates in each grid cell (0.5°) (Tian et al., 2022) rather than crop‐specific N input rates (e.g., Q. Wang et al., 2020). This discrepancy could cause significant uncertainties in the estimation of N 2 O emissions because different crop species have diverse N demands, influencing soil N availability for producing N 2 O (Shcherbak et al., 2014).…”

“…All agricultural management information was based on published data sets (from 1910 to 2020), which were successfully applied in global modeling studies (Ito et al., 2018; Tian et al., 2019). The amount (kgN ha −1 ) and properties ($${\text{NH}}_{4}^{+}$$:$${\text{NO}}_{3}^{-}$$) of the chemical N fertilizer application data were obtained from the History of anthropogenic Nitrogen inputs (HaNi) data set (Tian et al., 2022), which incorporates the information provided by the International Fertilizer Association country‐level inventory, crop‐specific N fertilizer use rates, major crop calendar, and FAOSTAT fertilizer types. The manure application data were also derived from the HaNi data set, while the manure chemical properties (e.g., C:N ratios, inorganic N proportion etc.)…”

“…Recently developed spatially global rice crop calendar data sets, agricultural management information, and a growing number of long‐term continuous measurements would provide good opportunities to better quantify the spatiotemporal variations in N 2 O emissions from rice‐based ecosystems (Laborte et al., 2017; Tian et al., 2022). The TRIPLEX‐GHG model v2.0 is a process‐based biogeochemical model that can simulate N 2 O emissions from upland soils on a global scale; however, its descriptions of the dynamics of rice‐based N 2 O fluxes are lacking (Song et al., 2022; K. Zhang et al., 2017).…”

Identification of driving factors for chlorophyll‐a in multi‐stable shallow lakes of China employing machine learning methods

“…When the lake is already in a state of eutrophication, the nutrient concentration no longer limits the growth of algae and the formation of algal blooms (Huang et al, 2020). Although, meteorological conditions may become the limiting factor for the occurrence of algal blooms (Tian et al, 2022). Taihu Lake is a typical large‐scale eutrophic shallow lake in China.…”