Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland

Potential benefits of cropping pattern change in the climate-sensitive regions of rice production in China

Rice production is a primary contributor to global greenhouse gas emissions, with unclear pathways towards carbon neutrality. Here, through a comprehensive assessment of direct greenhouse gas (GHG) emission using DNDC model and indirect GHG emission using emission factor methods, we estimated the annual crop yield, GHG emission amount and intensity, and economic benefits of different cropping patterns in the climate-sensitive regions of rice production in China. Through the expansion of single-rice and cropping pattern change from the wheat-rice to wheat-rice-rice in the climate-sensitive regions of single and triple-cropping cultivations, the total grain yield increased by 4.4 % and 4.5 % compared with the current national grain production, the GHG emission would increase by 2.4 % and 5.4 % of the current national GHG emissions from rice and wheat production, the net economic benefits could increase 0.9 % and decrease 2.0 % of the national output value of rice and wheat production. The study takes the entire-life cycle of crop growth as the principal line, and could provide a valuable reference for the regulation of the cropping pattern and the formulation of carbon reduction policies in the climate-sensitive region.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Agricultural transformation towards delivering deep carbon cuts in China's arid inland areas

Since agriculture is a main source of global greenhouse gas (GHG) emissions, reducing agricultural GHG emissions is crucial for achieving global climate goals. Nevertheless, there has been a lack of thorough and systematic assessment of the spatiotemporal distribution of agricultural GHG emissions at the county level, considering many factors such as crop and livestock products, different processes and gases, and the impact of carbon fixation. Furthermore, the potential of comprehensive technical strategies to reduce GHG emissions remains uncertain. Considering the unique attributes of agricultural development in arid areas of northwest China, this study aimed to explore long-term changes in agricultural net GHG emissions by county, product group, process, and gas and quantify the future reduction potential based on the Agricultural System-induced GreenHouse Gases INVentory (ASGHG-INV) econometric model. The results showed increasing trends in carbon emissions (CE), carbon sequestration (CS), carbon footprint (CF), crop carbon footprint per unit area (CFCF), and crop carbon footprint per unit product (CPCF) in various regions from 1991 to 2019, while there was a decreasing trend in livestock carbon footprint per unit product (LPCF). Focus on reducing GHG emissions in the crop-sector should be in Shihezi, Alaer, and Liangzhou; those of the livestock-sector should be in Xinyuan, Yecheng, Liangzhou, and Gaotai. Scenario analysis indicated that agricultural transformation could substantially reduce GHG emissions in all regions. Reducing the loss of reactive nitrogen was shown to be the most effective single strategy for reducing crop emissions. A comprehensive scheme further integrating the optimization of nitrogen fertilizer management, increasing water-saving, manure application, and straw returning measures, and using biochar and inhibitors can decrease CE, CF, CFCF, and CPCF by 22.62-43.45%, 40.55-111.60%, 41.38-111.78%, and 43.33-111.32%, respectively, increase CS by 9.07-39.97%. Optimizing forage composition was the most influential strategy for reducing livestock GHG emissions. The integrated strategy of further using forage additives, breeding low-emission varieties, and optimizing fecal management can reduce CF and LPCF by 37.32-76.42% and 40.51-78.70%, respectively. This study's results can be a reference for developing more effective GHG emissions reduction and green transformation pathways for global dryland agriculture.

Quantifying nitrous oxide production rates from nitrification and denitrification under various moisture conditions in agricultural soils: Laboratory study and literature synthesis

Biogenic nitrous oxide (N2O) from nitrification and denitrification in agricultural soils is a major source of N2O in the atmosphere, and its flux changes significantly with soil moisture condition. However, the quantitative relationship between N2O production from different pathways (i.e., nitrification vs. denitrification) and soil moisture content remains elusive, limiting our ability of predicting future agricultural N2O emissions under changing environment. This study quantified N2O production rates from nitrification and denitrification under various soil moisture conditions using laboratory incubation combined with literature synthesis. 15N labeling approach was used to differentiate the N2O production from nitrification and denitrification under eight different soil moisture contents ranging from 40 to 120% water-filled pore space (WFPS) in the laboratory study, while 80 groups of data from 17 studies across global agricultural soils were collected in the literature synthesis. Results showed that as soil moisture increased, N2O production rates of nitrification and denitrification first increased and then decreased, with the peak rates occurring between 80 and 95% WFPS. By contrast, the dominant N2O production pathway switched from nitrification to denitrification between 60 and 70% WFPS. Furthermore, the synthetic data elucidated that moisture content was the major driver controlling the relative contributions of nitrification and denitrification to N2O production, while NH4 + and NO3 - concentrations mainly determined the N2O production rates from each pathway. The moisture treatments with broad contents and narrow gradient were required to capture the comprehensive response of soil N2O production rate to moisture change, and the response is essential for accurately predicting N2O emission from agricultural soils under climate change scenarios.

Estimation of greenhouse gas emission flux from agricultural lands of Khuzestan province in Iran

Greenhouse gas emissions and their effects on global warming are one of the serious challenges of developed and developing countries. Investigating the amount of greenhouse gas emissions of different countries makes it possible to determine the share of countries in the production of greenhouse gases. The purpose of this study is to use DAYCENT and DNDC models to estimate the emission rate of methane, nitrous oxide, and carbon dioxide greenhouse gases as well as to estimate the global warming potential in Khuzestan agricultural lands in Iran. For this purpose, the gas sampling was done in rice, wheat, and sugarcane fields using a static chamber, and then the concentration of methane, nitrous oxide, and carbon dioxide was determined by using gas chromatography. In the following, DAYCENT and DNDC models were used to estimate gas emissions and the global warming potential of these gases was estimated. Finally, TOPSIS method was used to prioritize gas emissions. In order to evaluate the modeling accuracy, the statistical indicators of maximum error, root mean square error, determination coefficient, model efficiency, and residual mass coefficient were used. According to the results, the highest measured gas flux was obtained for rice fields at Baghmalek and the lowest for sugarcane in Abadan. The results of DAYCENT model estimation showed that the highest emissions were obtained for methane gas and rice cultivation, and lowest gas emissions were obtained for sugarcane cultivation. The results of DNDC model estimation also showed that the highest flux was determined for nitrous oxide gas in rice cultivation. The results of the estimation of global warming potential also showed that it was the highest in sugarcane cultivation (Shushtar station) and the DAYCENT model, and the lowest was also in wheat cultivation and the DNDC model. The statistical results of the estimation of DAYCENT and DNDC models showed that the DAYCENT model in sugarcane cultivation (Shushtar station) was the most accurate in estimating carbon dioxide gas, and the lowest accuracy was related to the DNDC model and sugarcane cultivation (Shushtar station) in estimating nitrous oxide gas. According to the results of agricultural activities in Khuzestan province, they have made a major contribution to the production of greenhouse gases, which, or the lack of attention to this issue, will have an effect on the future climate of this region.

Estimation of nitrous oxide emissions from rice paddy fields using the DNDC model: a case study of South Korea

The Denitrification-Decomposition (DNDC)-Rice is a mechanistic model which is widely used for the simulation and estimation of greenhouse gas emissions [nitrous oxide (N₂O)] from soils under rice cultivation. N₂O emissions from paddy fields in South Korea are of high importance for their cumulative effect on climate. The objective of this study was to estimate the N₂O emissions and biogeochemical factors involved in N₂O emissions such as ammonium (NH₄⁺) and nitrate (NO₃^-) using the DNDC model in the rice-growing regions of South Korea. N₂O emission was observed at every application of fertilizer and during end-season drainage at different rice-growing regions in South Korea. Maximum NH₄⁺ and NO₃^- were observed at 0-10 cm depth of soil. NH₄⁺ increased at each fertilizer application and no change in NO₃^- was observed during flooding. NH₄⁺ decreased and NO₃^- increased simultaneously at end-season drainage. Minimum and maximum cumulative N₂O emissions were observed at Chungcheongbuk-do and Jeju-do regions of South Korea, respectively. The simulated average cumulative N₂O emission in rice paddies of South Korea was 1.37 kg N₂O-N ha^-1 season^-1. This study will help in calculating the total nitrogen emissions from agriculture land of South Korea and the World.

Higher than expected N2O emissions from soybean crops in the Pampas Region of Argentina: Estimates from DayCent simulations and field measurements

In developing countries, agriculture generally represents a large fraction of GHG emissions reported in National Inventories, and emissions are typically estimated using Tier 1 IPCC guidelines. However, field data and locally adapted simulation models can improve the accuracy of IPCC estimations. In this study we aimed to quantify anthropogenic N2O emissions from croplands of Argentina through field measurements, model simulations and IPCC guidelines. We measured N2O emissions and their controlling factors in 62 plots of the Pampas Region with corn, soybean and wheat/soybean crops and in unmanaged grasslands. We accounted for gross emissions from crops and background emissions from unmanaged grasslands to calculate net anthropogenic emissions from crops as the difference between them. We calibrated and evaluated the DayCent model and then simulated different weather and management scenarios. Finally, we applied IPCC guidelines to estimate anthropogenic N2O emissions at the same plots. The DayCent model accurately simulated annual N2O emission for all crops as compared to measured data (RMSE = 1.4 g N ha-1 day-1). Measured and simulated emissions in soybean crops were higher than in corn and wheat/soybean crops. Gross N2O emissions ranged from 1.4 to 5.1 kg N ha-1 yr-1 for current environmental (soil and weather) and management (crops and fertilizer doses) conditions. Background emissions ranged between 1.1 and 1.3 kg N ha-1 yr-1, and therefore net anthropogenic emissions ranged from 0.3 to 4.0 kg N ha-1 yr-1. IPCC Tier 1 emission factors underestimated N2O releases from soybean, that were on average 4.87 times greater when estimated with DayCent and observations (0.53 vs 2.47 and 2.69 kg N ha-1 yr-1, respectively). On the contrary, IPCC estimates for corn and wheat/soybean crops were similar to modeled and measured values. Our results suggest that N2O emissions from the vast 15 million ha of soybean croplands in the Pampas Region may be substantially underestimated.

A gap in nitrous oxide emission reporting complicates long-term climate mitigation

Nitrous oxide (N2O) is an important greenhouse gas (GHG) that also contributes to depletion of ozone in the stratosphere. Agricultural soils account for about 60% of anthropogenic N2O emissions. Most national GHG reporting to the United Nations Framework Convention on Climate Change assumes nitrogen (N) additions drive emissions during the growing season, but soil freezing and thawing during spring is also an important driver in cold climates. We show that both atmospheric inversions and newly implemented bottom-up modeling approaches exhibit large N2O pulses in the northcentral region of the United States during early spring and this increases annual N2O emissions from croplands and grasslands reported in the national GHG inventory by 6 to 16%. Considering this, emission accounting in cold climate regions is very likely underestimated in most national reporting frameworks. Current commitments related to the Paris Agreement and COP26 emphasize reductions of carbon compounds. Assuming these targets are met, the importance of accurately accounting and mitigating N2O increases once CO2 and CH4 are phased out. Hence, the N2O emission underestimate introduces additional risks into meeting long-term climate goals.

Deceleration of Cropland-N2O Emissions in China and Future Mitigation Potentials

Agricultural soils are the largest anthropogenic emission source of nitrous oxide (N2O). National agricultural policies have been implemented to increase crop yield and reduce nitrogen (N) losses to the environment. However, it is difficult to effectively quantify crop-specific and regional N2O mitigation priorities driven by policies, due to lack of long-term, high-resolution crop-specific activity data, and oversimplified models. Here, we quantify the spatiotemporal changes and key drivers of crop-specific cropland-N2O emissions from China between 1980 and 2017, and future N2O mitigation potentials, using a linear mixed-effect model and survey-based data set of agricultural management measures. Cropland-N2O emissions from China tripled from 102.5 to 315.0 Gg N yr-1 between 1980 and 2017, and decelerated since 1998 mainly driven by country-wide deceleration and decrease in N rate and the changes in sowing structure. About 63% of N2O emissions could be reduced in 2050, primarily in the North China Plain and Northeast China Plain; 83% of which is from the production of maize (33%), vegetables (27%), and fruits (23%). The deceleration of N2O emissions highlights that policy interventions and agronomy practices (i.e., optimizing N rate and sowing structure) are potential pathways for further ambitious N2O mitigation in China and other developing countries.

Exploring wheat-based management strategies to balance agricultural production and environmental sustainability in a wheat-maize cropping system using the DNDC model

Faced with the great challenge of food demand and environmental pollution, optimizing agricultural practices can potentially balance food security and environmental protection. In this study, the DeNitrification-DeComposition (DNDC) model was applied to explore the effect of wheat-based management strategies on crop productivity and greenhouse gas emissions in the wheat-maize system. The DNDC model was tested against crop yield, daily nitrous oxide (N₂O) fluxes, and cumulative N₂O emissions determined from field measurements in a typical winter wheat-summer maize cropping system. Model evaluations demonstrated a good agreement between the observations and simulated crop yield (4.4%≤NRMSE≤8.0%), daily N₂O fluxes (0.68 ≤ d ≤ 0.88), and cumulative N₂O emissions (4.9%≤NRMSE≤11.9%). By adopting sensitivity analysis, the DNDC model then assessed the impacts on crop yield and cumulative N₂O emissions of multiple management practices from the winter wheat season. Delaying the sowing date from October 7 to November 4 reduced annual yield by 1.9%, while cumulative N₂O emissions were increased by 10.4%. Furthermore, postponing the supplementary irrigation date from April 1 to May 20 decreased annual yield by 2.4% without affecting cumulative N₂O emissions. An N fertilizer rate of 120-150 kg N ha^-1 was able to reduce N usage and cumulative N₂O emissions without sacrificing annual yield. Despite an improvement in the annual yield at the 0-30 cm tillage depth by 2.9%, cumulative N₂O emissions increased by 11.6%. The results suggest that sowing in early October, applying supplementary irrigation in early April, an N fertilizer rate of 120-150 kg N ha^-1, and no-tillage from the winter wheat season can improve crop yield and mitigate N₂O emissions. This is conducive to the synergism of agricultural production and environmental sustainability.

Uncertainties in direct N2O emissions from grazing ruminant excreta (EF3PRP) in national greenhouse gas inventories

Excreta deposition onto pasture, range and paddocks (PRP) by grazing ruminant constitute a source of nitrous oxide (N₂O), a potent greenhouse gas (GHG). These emissions must be reported in national GHG inventories, and their estimation is based on the application of an emission factor, EF_3PRP (proportion of nitrogen (N) deposited to the soil through ruminant excreta, which is emitted as N₂O)_. Depending on local data available, countries use various EF_3PRPs and approaches to estimate N₂O emissions from grazing ruminant excreta. Based on ten case study countries, this review aims to highlight the uncertainties around the methods used to account for these emissions in their national GHG inventories, and to discuss the efforts undertaken for considering factors of variation in the calculation of emissions. Without any local experimental data, 2006 the IPCC default (Tier 1) EF_3PRPs are still widely applied although the default values were revised in 2019. Some countries have developed country-specific (Tier 2) EF_3PRP based on local field studies. The accuracy of estimation can be improved through the disaggregation of EF_3PRP or the application of models; two approaches including factors of variation. While a disaggregation of EF_3PRP by excreta type is already well adopted, a disaggregation by other factors such as season of excreta deposition is more difficult to implement. Empirical models are a potential method of considering factors of variation in the establishment of EF_3PRP. Disaggregation and modelling requires availability of sufficient experimental and activity data, hence why only few countries have currently adopted such approaches. Replication of field studies under various conditions, combined with meta-analysis of experimental data, can help in the exploration of influencing factors, as long as appropriate metadata is recorded. Overall, despite standard IPCC methodologies for calculating GHG emissions, large uncertainties and differences between individual countries' accounting remain to be addressed.

Parameterization, calibration and validation of the DNDC model for carbon dioxide, nitrous oxide and maize crop performance estimation in East Africa

Dynamic biogeochemical models are crucial tools for simulating the complex interaction between soils, climate and plants; thus the need for improving understanding of nutrient cycling and reduction of greenhouse gases (GHG) from the environment. This study aimed to calibrate and validate the DeNitrification-DeComposition (DNDC) model for soil moisture, temperature, respiration, nitrous oxide and maize crop growth simulation in drier sub-humid parts of the central highlands of Kenya. We measured soil GHG fluxes from a maize field under four different soil fertility management practices for one year using static chambers and gas chromatography. Using experimental data collected from four management practices during GHG sampling period, we parameterized the DNDC model. The results indicate that the DNDC model simulates daily and annual soil moisture, soil temperature, soil respiration (CO₂), nitrous oxide (N₂O), N₂O yield-scaled emissions (YSE), N₂O emission factors (EFs) and maize crop growth with a high degree of fitness. However, the DNDC simulations slightly underestimated soil temperature (2–6%), crop growth (2–45%) and N₂O emissions (5–23%). The simulation overestimated soil moisture (9–17%) and CO₂ emissions (3–10%). It however, perfectly simulated YSE and EFs. Compared to the observed/measured annual GHG trends, the simulation results were relatively good, with an almost perfect fitting of emission peaks during soil rewetting at the onset of rains, coinciding with soil fertilisation. These findings provide reliable information in selecting best farm management practice, which simultaneously improves agricultural productivity and reduces GHG emissions, thus permitting climate-smart agriculture. The good DNDC simulated YSE and EFs values (Tier III) provide cheaper and reliable ways of filling the huge GHG data gap, reducing uncertainties in national GHG inventories and result to efficient targeting of mitigation measures in sub-Saharan Africa.

Prediction of future carbon footprint and ecosystem service value of carbon sequestration response to nitrogen fertilizer rates in rice production

There is concern for variations of the carbon footprint (CF) and ecosystem service value of carbon sequestration (ESVCS) related to nitrogen (N) fertilizer rate in rice production under future climate change. To explore possible future ecological effects of N fertilizer rate, a DeNitrification-DeComposition (DNDC) model combined with Representative Concentration Pathway (RCP) projections (RCP 4.5 and RCP 8.5) were used to predict the CF and ESVCS of rice production. The model was validated by a two-year field experiment, and then seven N fertilizer levels (0, 75, 150, 190, 225, 300, and 375 kg N/ha) were set for prediction from 2015 to 2050. The validation results indicated a good fit between the DNDC-simulated and observed data of GHG emission and rice yield. Under RCP 8.5, the mean CF was 4.5-8.7% higher and the average ESVCS was 3.6-7.4% lower than those under RCP 4.5. The effects of N fertilizer rate on CF and ESVCS were consistent between the two climate change scenarios. In both RCPs, it was found that CF and ESVCS were mainly influenced by N fertilizer rate due to the latter's effect on CH₄ emissions and crop carbon fixation. CH₄ was the main contributor to CF during 2015-2050, accounting for 43.9-58.3% of the total CF. Agricultural inputs were also large contributors to CF, and N fertilizer increased the indirect GHG emissions by 24.6-122.2% compared with no N fertilization treatment. Among the studied N fertilizer rates, 190 kg N/ha was the optimal rate, obtaining the lowest CF and highest ESVCS. These results indicate that, under future climate change, an N fertilizer rate of 190 kg N/ha could achieve a trade-off between high yield, reduction of CF, and improvement of ESVCS in rice production.

Calibration and validation of the DNDC model to estimate nitrous oxide emissions and crop productivity for a summer maize-winter wheat double cropping system in Hebei, China

The main aim of this paper was to calibrate and evaluate the DeNitrification-DeComposition (DNDC) model for estimating N₂O emissions and crop productivity for a summer maize-winter wheat double cropping system with different N fertilizer rates in Hebei, China. The model's performance was assessed before and after calibration and model sensitivity was investigated. The calibrated and validated DNDC performed effectively in estimating cumulative N₂O emissions (coefficient of determination (1:1 relationship; r²) = 0.91; relative deviation (RD) = -13 to 16%) and grain yields for both crops (r² = 0.91; RD = -21 to 7%) from all fertilized treatments, but poorly estimated daily N₂O patterns. Observed and simulated results showed that optimal N fertilizer treatment decreased cumulative N₂O flux, compared to conventional N fertilizer, without a significant impact on grain yields of the summer maize-winter wheat double cropping system. The high sensitivity of the DNDC model to rainfall, soil organic carbon and temperature resulted in significant overestimation of N₂O peaks during the warm wet season. The model also satisfactorily estimated daily patterns/average soil temperature (^o C; 0-5 cm depth) (r² = 0.88 to 0.89; root mean square error (RMSE) = 4 °C; normalized RMSE (nRMSE) = 25% and index of agreement (d) = 0.89-0.97) but under-predicted water filled pore space (WFPS; %; 0-20 cm depth) (r² = 0.3 to 0.4) and soil ammonium and nitrate (exchangeable NH₄⁺ & NO₃^-; kg N ha^-1; r² = 0.97). With reference to the control treatment (no N fertilizer), DNDC was weak in simulating both N₂O emissions and crop productivity. To be further improved for use under pedo-climatic conditions of the summer maize-winter wheat double cropping system we suggest future studies to identify and resolve the existing problems with the DNDC, especially with the control treatment.

Greenhouse gas emissions from synthetic nitrogen manufacture and fertilization for main upland crops in China

BACKGROUND

A significant source of greenhouse gas (GHG) emissions comes from the manufacture of synthetic nitrogen (N) fertilizers consumed in crop production processes. And the application of synthetic N fertilizers is recognized as the most important factor contributing to direct N₂O emissions from agricultural soils. Based on statistical data and relevant literature, the GHG emissions associated with synthetic N manufacture and fertilization for wheat and maize in different provinces and agricultural regions of China were quantitatively evaluated in the present study.

RESULTS

During the 2015-2017 period, the average application rates of synthetic N for wheat and maize in upland fields of China were 222 and 197 kg ha^-1, respectively. The total consumption of synthetic N on wheat and maize was 12.63 Mt year^-1. At the national scale, the GHG emissions associated with the manufacture of synthetic N fertilizers were estimated to be 41.44 and 59.71 Mt CO₂-eq year^-1 for wheat and maize in China, respectively. And the direct N₂O emissions derived from synthetic N fertilization were estimated to be 35.82 and 69.44 Gg N₂O year^-1 for wheat and maize, respectively. In the main wheat-cultivating regions of China, area-scaled GHG emissions were higher for Inner Mongolia, Jiangsu and Xinjiang provinces. And for maize, Gansu, Xinjiang, Yunnan, Shannxi and Jiangsu provinces had higher area-scaled GHG emissions. Higher yield-scaled GHG emissions for wheat and maize mainly occured in Yunnan and Gansu provinces.

CONCLUSIONS

The manufacture and application of synthetic N fertilizers for wheat and maize in Chinese croplands is an important source of agricultural GHG emissions. The current study could provide a scientific basis for establishing an inventory of upland GHG emissions in China and developing appropriate mitigation strategies.

Weakened growth of cropland-N2 O emissions in China associated with nationwide policy interventions

China has experienced rapid agricultural development over recent decades, accompanied by increased fertilizer consumption in croplands; yet, the trend and drivers of the associated nitrous oxide (N₂ O) emissions remain uncertain. The primary sources of this uncertainty are the coarse spatial variation of activity data and the incomplete model representation of N₂ O emissions in response to agricultural management. Here, we provide new data-driven estimates of cropland-N₂ O emissions across China in 1990-2014, compiled using a global cropland-N₂ O flux observation dataset, nationwide survey-based reconstruction of N-fertilization and irrigation, and an updated nonlinear model. In addition, we have evaluated the drivers behind changing cropland-N₂ O patterns using an index decomposition analysis approach. We find that China's annual cropland-N₂ O emissions increased on average by 11.2 Gg N/year² (p < .001) from 1990 to 2003, after which emissions plateaued until 2014 (2.8 Gg N/year² , p = .02), consistent with the output from an ensemble of process-based terrestrial biosphere models. The slowdown of the increase in cropland-N₂ O emissions after 2003 was pervasive across two thirds of China's sowing areas. This change was mainly driven by the nationwide reduction in N-fertilizer applied per area, partially due to the prevalence of nationwide technological adoptions. This reduction has almost offset the N₂ O emissions induced by policy-driven expansion of sowing areas, particularly in the Northeast Plain and the lower Yangtze River Basin. Our results underline the importance of high-resolution activity data and adoption of nonlinear model of N₂ O emission for capturing cropland-N₂ O emission changes. Improving the representation of policy interventions is also recommended for future projections.