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In addition to enhancing agricultural productivity, synthetic nitrogen (N) and phosphorous (P) fertilizer application in croplands dramatically alters global nutrient budget, water quality, greenhouse gas balance, and their feedback to the climate system. However, due to the lack of geospatial fertilizer input data, current Earth system and land surface modeling studies have to ignore or use oversimplified data (e.g., static, spatially uniform fertilizer use) to characterize agricultural N and P input over decadal or century-long periods. In this study, we therefore develop global time series gridded data of annual synthetic N and P fertilizer use rate in agricultural lands, matched with HYDE 3.2 historical land use maps, at a resolution of 0.5°  ×  0.5° latitude–longitude during 1961–2013. Our data indicate N and P fertilizer use rates on per unit cropland area increased by approximately 8 times and 3 times, respectively, since the year 1961 when IFA (International Fertilizer Industry Association) and FAO (Food and Agricultural Organization) surveys of country-level fertilizer input became available. Considering cropland expansion, the increase in total fertilizer consumption is even larger. Hotspots of agricultural N fertilizer application shifted from the US and western Europe in the 1960s to eastern Asia in the early 21st century. P fertilizer input shows a similar pattern with an additional current hotspot in Brazil. We found a global increase in fertilizer N ∕ P ratio by 0.8 g N g−1 P per decade (p < 0.05) during 1961–2013, which may have an important global implication for human impacts on agroecosystem functions in the long run. Our data can serve as one of critical input drivers for regional and global models to assess the impacts of nutrient enrichment on climate system, water resources, food security, etc. Datasets available at doi:10.1594/PANGAEA.863323.

Grasslands absorb and release carbon dioxide (CO 2 ), emit methane (CH 4 ) from grazing livestock, and emit nitrous oxide (N 2 O) from soils. Little is known about how the fluxes of these three greenhouse gases, from managed and natural grasslands worldwide, have contributed to past climate change, or the roles of managed pastures versus natural grasslands. Here, global trends and regional patterns of the full greenhouse gas balance of grasslands are estimated for the period 1750 to 2012. A new spatially explicit land surface model is applied, to separate the direct effects of human activities from land management and the indirect effects from climate change, increasing CO 2 and regional changes in nitrogen deposition. Direct human management activities are simulated to have caused grasslands to switch from a sink to a source of greenhouse gas, because of increased livestock numbers and accelerated conversion of natural lands to pasture. However, climate change drivers contributed a net carbon sink in soil organic matter, mainly from the increased productivity of grasslands due to increased CO 2 and nitrogen deposition. The net radiative forcing of all grasslands is currently close to neutral, but has been increasing since the 1960s. Here, we show that the net global climate warming caused by managed grassland cancels the net climate cooling from carbon sinks in sparsely grazed and natural grasslands. In the face of future climate change and increased demand for livestock products, these findings highlight the need to use sustainable management to preserve and enhance soil carbon storage in grasslands and to reduce greenhouse gas emissions from managed grasslands. Grasslands, and the livestock that live there, are dynamic sources and sinks of greenhouse gases, but what controls these fluxes remains poorly characterized. Here the authors show that on the global level, grasslands are climate neutral owing to the cancelling effects of managed vs. natural systems.

Emissions of nitrous oxide (N2O) from the world’s river networks constitute a poorly constrained term in the global N2O budget1,2. This N2O component was previously estimated as indirect emissions from agricultural soils3 with large uncertainties4–10. Here, we present an improved model representation of nitrogen and N2O processes of the land–ocean aquatic continuum11 constrained with an ensemble of 11 data products. The model–data framework provides a quantification for how changes in nitrogen inputs (fertilizer, deposition and manure), climate and atmospheric CO2 concentration, and terrestrial processes have affected the N2O emissions from the world’s streams and rivers during 1900–2016. The results show a fourfold increase of global riverine N2O emissions from 70.4 ± 15.4 Gg N2O-N yr−1 in 1900 to 291.3 ± 58.6 Gg N2O-N yr−1 in 2016, although the N2O emissions started to decline after the early 2000s. The small rivers in headwater zones (lower than fourth-order streams) contributed up to 85% of global riverine N2O emissions. Nitrogen loads on headwater streams and groundwater from human activities, primarily agricultural nitrogen applications, play an important role in the increase of global riverine N2O emissions.N2O emissions from rivers have increased globally by a factor of four between 1900 and 2016, with emissions starting to decline since the early 2000s. Most riverine N2O emissions come from smaller streams, driven primarily by the use of nitrogen fertilizers in agriculture.

One of the major limitations in assessing the impacts of human activities on global biogeochemical cycles and climate is a shortage of reliable data on historical land cover and land use change (LCLUC). China had extreme discrepancies in estimating contemporary and historical patterns of LCLUC over the last 3 centuries because of its geographical complexity, long history of land use, and limited national surveys. This study aims to characterize the spatial and temporal patterns of China's LCLUC during 1700–2005 by reconstructing historical gridded data sets from high‐resolution satellite data and long‐term historical survey data. During this 300 year period, the major characteristics of LCLUC in China have been shrinking forest (decreased by 22%) and expanding cropland (increased by 42%) and urban areas (including urban and rural settlements, factories, quarries, mining, and other built‐up land). New cropland areas have come almost equally from both forested and nonforested land. This study also revealed that substantial conversion between forest and woodland can be attributed to forest harvest, forest regeneration, and land degradation. During 1980–2005, LCLUC was characterized by shrinking cropland, expanding urban and forest areas, and large decadal variations on a national level. LCLUC in China showed significant spatial variations during different time periods, which were caused by spatial heterogeneity in vegetation, soils, and climate and regional imbalance in economy development. During 1700–2005, forests shrunk rapidly while croplands expanded in the northeast and southwest of China. During 1980–2005, we found a serious loss of cropland and urban sprawl in the eastern plain, north, and southeast regions of China and a large increase in forested area in the southeast and southwest regions. The reconstructed LCLUC data sets from this study could be used to assess the impacts of land use change on biogeochemical cycles, the water cycle, and the regional climate in China. To further eliminate uncertainties in this data set and make reliable projections of LCLUC for the future, we need to improve our understanding of the drivers of LCLUC and work toward developing an advanced, spatially explicit land use model.

Carbon budget accounting relies heavily on Food and Agriculture Organization land-use data reported by governments. Here we develop a new land-use and cover-change database for China, finding that differing historical survey methods biased China’s reported data causing large errors in Food and Agriculture Organization databases. Land ecosystem model simulations driven with the new data reveal a strong carbon sink of 8.9 ± 0.8 Pg carbon from 1980 to 2019 in China, which was not captured in Food and Agriculture Organization data-based estimations due to biased land-use and cover-change signals. The land-use and cover-change in China, characterized by a rapid forest expansion from 1980 to 2019, contributed to nearly 44% of the national terrestrial carbon sink. In contrast, climate changes (22.3%), increasing nitrogen deposition (12.9%), and rising carbon dioxide (8.1%) are less important contributors. This indicates that previous studies have greatly underestimated the impact of land-use and cover-change on the terrestrial carbon balance of China. This study underlines the importance of reliable land-use and cover-change databases in global carbon budget accounting. The impact of land-use and cover-change (LUCC) on ecosystem carbon stock in China is poorly known due to large biases in existing databases. Here the authors develop a new LUCC database with corrected false signals and reveal that forest expansion is the dominant driver of China’s recent carbon sink.

Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N 2 O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N 2 O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N 2 O-N yr −1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N 2 O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N 2 O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr −1 . This suggests a global emission factor of 0.051% for inland water N 2 O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories. Modeling shows that N 2 O emissions from global lakes and reservoirs have doubled since the pre-industrial era, this was mainly caused by widespread agricultural nitrogen application.

Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.5 Pg C from 2020 to 2100, with additional sink through augmentation of wood product pool (0.6-2.0 Pg C) and spatiotemporal optimization of forest management (2.3 ± 0.03 Pg C). We find that statistical model might cause large bias in long-term projection due to underrepresentation or neglect of wood harvest and forest demographic changes. Remarkably, disregarding the repercussions of harvesting on forest age can result in a premature shift in the timing of the carbon sink peak by 1–3 decades. Our findings emphasize the pressing necessity for the swift implementation of optimal forest management strategies for carbon sequestration enhancement. The authors show China’s forests can sequester 172.3 million tons of carbon per year in biomass by 2100, with an additional 28.1 million tons from improved management practices, but neglecting wood harvest impacts will distort long-term future projections.

The arid and semiarid region in central Asia is sensitive and vulnerable to climate variations. However, the sparse and highly unevenly distributed meteorological stations in the region provide limited data for understanding of the region’s climate variations. In this study, the near-surface air temperature change in central Asia from 1979 to 2011 was examined using observations from 81 meteorological stations, three local observation validated reanalysis datasets of relatively high spatial resolutions, and the Climate Research Unit (CRU) dataset. Major results suggested that the three reanalysis datasets match well with most of the local climate records, especially in the low-lying plain areas. The consensus of the multiple datasets showed significant regional surface air temperature increases of 0.36°–0.42°C decade−1in the past 33 years. No significant contributions from declining irrigation and urbanization to temperature change were found. The rate is larger in recent years than in the early years in the study period. Additionally, unlike in many regions in the world, the temperature in winter showed no increase in central Asia in the last three decades, a noticeable departure from the global trend in the twentieth century. The largest increase in surface temperature was occurring in the spring season. Analyses further showed a warming center in the middle of the central Asian states and weakened temperature variability along the northwest–southeast temperature gradient from the northern Kazakhstan to southern Xinjiang. The reanalysis datasets also showed significant negative correlations between temperature increase rate and elevation in this complex terrain region.

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Forests are invaluable natural resources that provide essential services to humanity. However, the effects of global warming on forest carbon and nitrogen cycling remain uncertain. Here we project a decrease in total nitrogen input and accumulation by 7 ± 2 and 28 ± 9 million tonnes (Tg), respectively, and an increase in reactive nitrogen losses to the environment by 9 ± 3 Tg for 2100 due to warming in a fossil-fueled society. This would compromise the global carbon sink capacity by 0.45 ± 0.14 billion tonnes annually. Furthermore, warming-induced inequality in forest carbon and nitrogen cycles could widen the economic gap between the Global South and Global North. High-income countries are estimated to gain US$179 billion in benefits from forest assets under warming, while other regions could face net damages of US$31 billion. Implementing climate-smart forest management, such as comprehensive restoration and optimizing tree species composition, is imperative in the face of future climate change. Global warming may widen the development gap between the Global South and North by increasing inequalities in forest carbon and nitrogen cycling. High-income countries are expected to benefit from forest assets, while others face net losses.