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Abstract U.S. rice paddies, critical for food security, are increasingly contributing to non‐CO₂greenhouse gas (GHG) emissions like methane (CH₄) and nitrous oxide (N₂O). Yet, the full assessment of GHG balance, considering trade‐offs between soil organic carbon (SOC) change and non‐CO₂GHG emissions, is lacking. Integrating an improved agroecosystem model with a meta‐analysis of multiple field studies, we found that U.S. rice paddies were the rapidly growing net GHG emission sources, increased 138% from 3.7 ± 1.2 Tg CO₂eq yr⁻¹in the 1960s to 8.9 ± 2.7 Tg CO₂eq yr⁻¹in the 2010s. CH₄, as the primary contributor, accounted for 10.1 ± 2.3 Tg CO₂eq yr⁻¹in the 2010s, alongside a notable rise in N₂O emissions by 0.21 ± 0.03 Tg CO₂eq yr⁻¹. SOC change could offset 14.0% (1.45 ± 0.46 Tg CO₂eq yr⁻¹) of the climate‐warming effects of soil non‐CO₂GHG emissions in the 2010s. This escalation in net GHG emissions is linked to intensified land use, increased atmospheric CO₂, higher synthetic nitrogen fertilizer and manure application, and climate change. However, no/reduced tillage and non‐continuous irrigation could reduce net soil GHG emissions by approximately 10% and non‐CO₂GHG emissions by about 39%, respectively. Despite the rise in net GHG emissions, the cost of achieving higher rice yields has decreased over time, with an average of 0.84 ± 0.18 kg CO₂eq ha⁻¹emitted per kilogram of rice produced in the 2010s. The study suggests the potential for significant GHG emission reductions to achieve climate‐friendly rice production in the U.S. through optimizing the ratio of synthetic N to manure fertilizer, reducing tillage, and implementing intermittent irrigation. 

Abstract Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N₂O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N₂O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process‐based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low‐ambition nitrogen regulation policies, EF would increase from 1.18%–1.22% in 2010 to 1.27%–1.34% by 2050, representing a relative increase of 4.4%–11.4% and exceeding the IPCC tier‐1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%–0.35% (relative increase of 11.9%–17%). In contrast, high‐ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying–wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N₂O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N₂O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio‐economic assessments and policy‐making efforts. 

This dataset contains yearly projections of emission factors (EFs) for fertilizer-induced direct nitrous oxide (N2O) emissions across the global agricultural lands with a spatial resolution of 0.5° × 0.5° from 1990 to 2050. Emission factor (EF) is defined as the amount of N2O emitted per unit of nitrogen (N) fertilizer applied, expressed in percentage (%). They are developed from a hybrid modeling framework, Dym-EF (more details can be found in Li et al., 2024). The framework integrates machine learning approaches with an ensemble of eight process-based models from The Global N2O Model Intercomparison Project phase 2 (NMIP2) to learn the relationship between EF dynamics and multiple environmental factors, such as climate, soil properties, nitrogen fertilizer input, and other agricultural management practices. After the hybrid modeling framework was extensively validated, we applied it to develop EF projections under different nitrogen management policies and climate change scenarios, including future climate data from 37 Global Climate Models (GCMs). The annual median and standard deviation (SD) of EF under each scenario represent the projection median and variability derived from climate input data using the 37 GCMs.The dataset filenames follow the structure: 'Scenario'_'N regulation'_'Median/SD', where 'Scenario' corresponds to the different nitrogen management and climate scenarios (e.g., INMS1, INMS2, and INMS3), 'N regulation' corresponds to the different nitrogen management levels (e.g., BAU, LowNRegul, and MedNRegul), and 'Median/SD' indicates whether the file contains the median (Median) or standard deviation (SD) of the projections. All relevant data and further details can be found in the supplementary materials and the cited references.INMS1: Business-as-usual, Land use regulation: Medium, Diet: Meat & dairy-rich, Ambition level: LowINMS2: Low-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: LowINMS3: Medium-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: ModerateINMS4: High-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: HighINMS5: Best-case, Land use regulation: Strong, Diet: Low meat & dairy, Ambition level: HighINMS6: Best-case “Plus”, Land use regulation: Strong, Diet: Ambitious diet shift and food-loss/waste reductions, Ambition level: HighINMS7: Bioenergy, Land use regulation: Strong, Diet: Low meat & dairy, Ambition level: HighWe developed this data using the “ranger” package in R 4.1.1, which is accessible at https://cran.r-project.org/web/packages/ranger/. The optimization of the two hyperparameters (ntree and mtry) was performed using the ‘caret’ package, available at https://topepo.github.io/caret/.This database is developed by Li, L., C. Lu, W. Winiwarter, H. Tian, J. Canadell, A. Ito, A.K. Jain, S. Kou-Giesbrecht, S. Pan, N. Pan, H. Shi, Q. Sun, N. Vuichard, S. Ye., S. Zaehle, Q. Zhu. Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector Global Change Biology (In Press) 

Abstract Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world. 

Abstract Increasing food and biofuel demands have led to the cascading effects from cropland expansions, raised fertilizer use, to increased riverine nitrogen (N) loads. However, little is known about the current trade-off between riverine N pollution and crop production due to the lack of predictive understanding of ecological processes across the land-aquatic continuum. Here, we propose a riverine N footprint (RNF) concept to quantify how N loads change along with per unit crop production gain. Using data synthesis and a well-calibrated hydro-ecological model, we find that the RNF within the Mississippi–Atchafalaya River Basin peaked at 1.95 g N kg⁻¹grain during the 1990s, and then shifted from an increasing to a decreasing trend, reaching 0.65 g N kg⁻¹grain in the 2010s. This implies decoupled responses of crop production and N loads to key agricultural activities approximately after 2000, but this pattern varies considerably among sub-basins. Our study highlights the importance of developing a food–energy–water nexus indicator to examine the region-specific trade-offs between crop production and land-to-aquatic N loads for achieving nutrient mitigation goals while sustaining economic gains. 

These data support the findings of a manuscript by Lu et al. under review in Environmental Research Letters. We used data synthesis and a well-calibrated hydro-ecological model to quantify the dynamics and controls of the riverine N footprint (RNF) within the Mississippi-Atchafalaya River Basin (MARB) from 1970 to 2019. These supportive data include (1) Annual synthetic N fertilizer and manure N input from 1970 to 2019 in sub-basins in the MARB; (2) Annual N inputs, outputs, and N balance from 1970 to 2017 in the MARB; (3) Changes in crop production, N load and riverine N footprint in response to key agricultural activities in MARB; (4) Changes in crop production, N load, and riverine N footprint under key agricultural activities at sub-basin level; (5) Annual acreage of major grain crops and total cropland areas in sub-basins of the MARB. 

Abstract Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N₂O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N₂O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N₂O-N yr⁻¹in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N₂O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N₂O 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 N₂O 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. 

Abstract. The land of the conterminous United States (CONUS) hasbeen transformed dramatically by humans over the last four centuries throughland clearing, agricultural expansion and intensification, and urban sprawl.High-resolution geospatial data on long-term historical changes in land useand land cover (LULC) across the CONUS are essential for predictiveunderstanding of natural–human interactions and land-based climatesolutions for the United States. A few efforts have reconstructed historicalchanges in cropland and urban extent in the United States since themid-19th century. However, the long-term trajectories of multiple LULCtypes with high spatial and temporal resolutions since the colonial era(early 17th century) in the United States are not available yet. Byintegrating multi-source data, such as high-resolution remote sensingimage-based LULC data, model-based LULC products, and historical censusdata, we reconstructed the history of land use and land cover for theconterminous United States (HISLAND-US) at an annual timescale and 1 km × 1 km spatial resolution in the past 390 years (1630–2020). The results showwidespread expansion of cropland and urban land associated with rapid lossof natural vegetation. Croplands are mainly converted from forest, shrub,and grassland, especially in the Great Plains and North Central regions.Forest planting and regeneration accelerated the forest recovery in theNortheast and Southeast since the 1920s. The geospatial and long-termhistorical LULC data from this study provide critical information forassessing the LULC impacts on regional climate, hydrology, andbiogeochemical cycles as well as achieving sustainable use of land in thenation. The datasets are available at https://doi.org/10.5281/zenodo.7055086 (Li et al., 2022).