Global assessment of nitrogen losses and trade-offs with yields from major crop cultivations

Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Exploring the benefits of inoculated cowpeas under different climatic conditions in Namibia

Cowpeas (Vigna uniculata L. Walp) are grown by many smallholder farmers in sub-Saharan Africa for food and their ability to fix nitrogen even under stress. Their performance depends on the indigenous rhizobial strains that live in symbiotic association with the roots; it can be enhanced if the seeds are inoculated with more effective ones. Data of the effectiveness of the technique under a variety of climatic conditions is rare. Here, we thus use a model to upscale two field experiments conducted in Namibia to include different climate change scenarios. The simulations show that non-inoculated cowpeas have mean yields of 0.5 t/ha and inoculated cowpeas 1 t/ha. If climatic conditions are favorable (cool and wet), estimated yield differences increase to over 1 t/ha. In dry years (< 200 mm), the average yield difference is only 0.1 t/ha. In the far future (2080-2100), instances of dry and hot years will increase. Using inoculated cowpea seeds instead of non-inoculated ones thus does not benefit farmers as much then as in the near future (2030-2050). In conclusion, using cowpea seeds inoculated with an efficient rhizobial strain can significantly increase yields under varying climatic conditions, but yield advantages decrease markedly in very dry and hot years.

Effects of the Rainfall Intensity and Slope Gradient on Soil Erosion and Nitrogen Loss on the Sloping Fields of Miyun Reservoir

Environmental loss is primarily caused by soil, water, and nutrient loss, and runoff is associated with nutrient transport and sediment loss. Most existing studies have focused on one influencing factor, namely slope gradient or rainfall intensity, for slope erosion and nutrient loss, but the joint effects of the two factors have rarely been researched. In this context, the impact of slope gradients (0°, 5°, 10°, and 15°) and rainfall intensities (30, 40, 50, 60, 70, and 80 mm/h) on soil erosion and nutrient loss on the sloping fields of Miyun Reservoir were explored using the indoor artificial rainfall simulation testing system. Based on the results of the study, the variation of runoff coefficient with slope gradient was not noticeable for rainfall intensities <40 mm/h; however, for rainfall intensities >40 mm/h, the increased range of runoff coefficient doubled, and the increase was the fastest under 0° among the four slope gradients. The slope surface runoff depth and runoff rate showed positive correlations with the rainfall intensity (r = 0.875, p < 0.01) and a negative correlation with the slope gradient. In addition, the cumulative sediment yield was positively related to the slope gradient and rainfall intensity (r > 0.464, p < 0.05). Moreover, the slope surface runoff-associated and sediment-associated loss rates of total nitrogen (TN) rose as the rainfall intensity or slope gradient increased, and significant linear positive correlations were found between the runoff-associated TN loss rate (NLr) and the runoff intensity and between the sediment-associated NLr and the erosion intensity. In addition, there were positive linear correlations between slope runoff-associated or sediment-associated TN loss volumes and rainfall intensity, surface runoff, and sediment loss volumes, which were highly remarkable. The slope gradient had a significant positive correlation with the slope surface runoff-associated TN loss at 0.05 (r = 0.452) and a significant positive correlation with the sediment-associated TN loss at the level of 0.01 (r = 0.591). The rainfall intensity exhibited extremely positive correlations with the slope surface runoff-associated and sediment-associated TN loss at 0.01 (r = 0.717 and 0.629) Slope gradients have less effect on nitrogen loss on sloped fields than rainfall intensity, mainly because rainfall intensity affects runoff depth. Based on the findings of this study, Miyun Reservoir may be able to improve nitrogen loss prevention and control.

Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.

Model comparison and quantification of nitrous oxide emission and mitigation potential from maize and wheat fields at a global scale

Maize and wheat are major cereals that contribute two-thirds of the food energy intake globally. The two crops consume about 35% of the nitrogen (N) fertilizer used in agriculture and thereby contribute to fertilizer-induced nitrous oxide (N₂O) emissions. Thus, estimation of spatially disaggregated N₂O emissions from maize and wheat fields on a global scale could be useful for identifying emission and mitigation hotspots. It could also be needed for prioritizing mitigation options consistent with location-specific production and environmental goals. N₂O emission from four models (CCAFS-MOT, IPCC Tier-I, IPCC Tier-II and Tropical N₂O) using a standard gridded dataset from global maize and wheat fields were compared and their performance evaluated using measured N₂O emission data points (777 globally distributed datapoints). The models were used to quantify spatially disaggregated N₂O emission and mitigation potential from maize and wheat fields globally and the values were compared. Although the models differed in their performance of capturing the level of measured N₂O emissions, they produced similar spatial patterns of annual N₂O emissions from maize and wheat fields. Irrespective of the models, predicted N₂O emissions per hectare were higher in some countries in East and South Asia, North America, and Western Europe, driven mainly by higher N application rates. The study indicated a substantial N₂O abatement potential if application of excess N in the maize and wheat systems is reduced without compromising the yield of the crops through technological and crop management innovations. N₂O mitigation potential is higher in those countries and regions where N application rates and current N₂O emissions are already high. The estimated mitigation potentials are useful for hotspot countries to target fertilizer and crop management as one of the mitigation options in their Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change (UNFCCC).

Estimation of nitrogen runoff loss from croplands in the Yangtze River Basin: A meta-analysis

Nitrogen (N) runoff loss from croplands due to excessive anthropogenic N additions is a principal cause of non-point source water pollution worldwide. Quantitative knowledge of regional-scale N runoff loss from croplands is essential for developing sustainable agricultural N management and efficient water N pollution control strategies. This meta-analysis quantifies N runoff loss rates and identifies the primary factors regulating N runoff loss from uplands (n = 570) and paddy (n = 434) fields in the Yangtze River Basin (YRB). Results indicated that total N (TN) runoff loss rates from uplands and paddy fields consistently increased from upstream to downstream regions. Runoff depth, soil N content and fertilizer addition rate (chemical fertilizer + manure) were the major factors regulating variability of TN runoff loss from uplands, while runoff depth and fertilizer addition rate were the main controls for paddy fields. Multiple regression models incorporating these influencing factors effectively predicted TN runoff loss rates from uplands (calibration: R² = 0.60, n = 242; validation: R² = 0.55, n = 104) and paddy fields (calibration: R² = 0.70, n = 189; validation: R² = 0.85, n = 82). Models estimated total cropland TN runoff loss load in YRB of 0.54 (95% Cl: 0.23-1.33) Tg, with 0.30 (95% Cl: 0.15-0.56) Tg from uplands and 0.24 (95% Cl: 0.08-0.77) Tg from paddy fields in 2017. Guangxi, Jiangxi, Fujian, Hunan and Henan provinces within the YRB were identified as cropland TN runoff loss hotspots. Models predicted that TN runoff loss loads from croplands in YRB would decrease by 0.8-13.7% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. Reducing upland TN runoff loss should focus primarily on soil N utilization and runoff management, while reducing N fertilizer addition and runoff provided the most sensitive strategies for paddy fields. Integrated management of water, soil and fertilizer is required to effectively reduce cropland N runoff loss.

An Integrated Modeling System for the Evaluation of Water Resources in Coastal Agricultural Watersheds: Application in Almyros Basin, Thessaly, Greece

This study presents an integrated modeling system for the evaluation of the quantity and quality of water resources of coastal agricultural watersheds. The modeling system consists of coupled and interrelated models, including (i) a surface hydrology model (UTHBAL), (ii) a groundwater hydrology model (MODFLOW), (iii) a crop growth/nitrate leaching model (REPIC, an R-ArcGIS-based EPIC model), (iv) a groundwater contaminant transport model (MT3DMS), and (v) a groundwater seawater intrusion model (SEAWAT). The efficacy of the modeling system to simulate the quantity and quality of water resources has been applied to the Almyros basin in Thessaly, Greece. It is a coastal agricultural basin with irrigated and intensified agriculture facing serious groundwater problems, such as groundwater depletion, nitrate pollution, and seawater intrusion. Irrigation demands were estimated for the main crops cultivated in the area, based on precipitation and temperature from regional weather stations. The models have been calibrated and validated against time-series of observed crop yields, groundwater table observations, and observed concentrations of nitrates and chlorides. The results indicate that the modeling system simulates the water resources quantity and quality with increased accuracy. The proposed modeling system could be used as a tool for the simulation of water resources management and climate change scenarios.

Global Phosphorus Losses from Croplands under Future Precipitation Scenarios

Phosphorus (P) losses from fertilized croplands to inland water bodies cause serious environmental problems. During wet years, high precipitation disproportionately contributes to P losses. We combine simulations of a gridded crop model and outputs from a number of hydrological and climate models to assess global impacts of changes in precipitation regimes on P losses during the 21st century. Under the baseline climate during 1991-2010, median P losses are 2.7 ± 0.5 kg P ha^-1 year^-1 over global croplands of four major crops, while during wet years, P losses are 3.6 ± 0.7 kg P ha^-1 year^-1. By the end of this century, P losses in wet years would reach 4.2 ± 1.0 (RCP2.6) and 4.7 ± 1.3 (RCP8.5) kg P ha^-1 year^-1 due to increases in high annual precipitation alone. The increases in P losses are the highest (up to 200%) in the arid regions of Middle East, Central Asia, and northern Africa. Consequently, in three quarters of the world's river basins, representing about 40% of total global runoff and home up to 7 billion people, P dilution capacity of freshwater could be exceeded due to P losses from croplands by the end of this century.

Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

Achieving High Crop Yields with Low Nitrogen Emissions in Global Agricultural Input Intensification

Increasing demand for food is driving a worldwide trend of agricultural input intensification. However, there is no comprehensive knowledge about the interrelations between potential yield gains and environmental trade-offs that would enable the identification of regions where input-driven intensification could achieve higher yields, yet with minimal environmental impacts. We explore ways of enhancing global yields, while avoiding significant nitrogen (N) emissions (N_e) by exploring a range of N and irrigation management scenarios. The simulated responses of yields and N_e to increased N inputs (N_in) and irrigation show high spatial variations due to differences in current agricultural inputs and agro-climatic conditions. Nitrogen use efficiency (NUE) of yield gains is negatively correlated with incremental N_e due to N_in additions. Avoiding further intensification in regions where high fractions of climatic yield potentials, ≥ 80%, are already achieved is key to maintain good NUE. Depending on the intensification scenarios, relative increases in N_e could be reduced by 0.3-29.6% of the baseline N_e with this intensification strategy as compared to indiscriminate further intensification, at the cost of a loss of yield increases by 0.2-16.7% of the baseline yields. In addition, irrigation water requirements and N_in would dramatically decrease by considering this intensification strategy.

Runoff nitrogen (N) losses and related metabolism enzyme activities in paddy field under different nitrogen fertilizer levels

Nitrogen (N), one of the most important nutrients for plants, also can be a pollutant in water environments. N metabolism is sensitive to N fertilization application and related to rice growth. Different levels of N fertilization treatment (N0, control without N fertilizer application; N100, chemical fertilizer of 100 kg N ha^-1; N200, chemical fertilizer of 200 kg N ha^-1; N300, chemical fertilizer of 300 kg N ha^-1) were tested to investigate N loss due to surface runoff and to explore the possible involvement of rice N metabolism responses to different N levels. The results indicated that N loss through runoff and rice yield was simultaneously increased in response to increasing N fertilizer levels. About 30% of total nitrogen (TN) was lost in the form of ammonium (NH₄⁺) in a rice growing season, while only 3% was lost in the form of nitrate (NO₃^-). Higher N application increased carbon (C) and N content and increased nitrate reductase (NR) and glutamine synthetase (GS) activities in rice leaves, while it decreased glutamate synthase (GOGAT) and glutamate dehydrogenase (GDH) activities. These results suggest that N caused the accumulation of assimilation products in flag leaves of rice and stimulated N metabolic processes, while some protective substances were also stimulated to resist low N stress. This study provides a theoretical basis for improving N fertilizer management to reduce N loss and increase rice yield.

Water resources conservation and nitrogen pollution reduction under global food trade and agricultural intensification

Global food trade entails virtual flows of agricultural resources and pollution across countries. Here we performed a global-scale assessment of impacts of international food trade on blue water use, total water use, and nitrogen (N) inputs and on N losses in maize, rice, and wheat production. We simulated baseline conditions for the year 2000 and explored the impacts of an agricultural intensification scenario, in which low-input countries increase N and irrigation inputs to a greater extent than high-input countries. We combined a crop model with the Global Trade Analysis Project model. Results show that food exports generally occurred from regions with lower water and N use intensities, defined here as water and N uses in relation to crop yields, to regions with higher resources use intensities. Globally, food trade thus conserved a large amount of water resources and N applications, and also substantially reduced N losses. The trade-related conservation in blue water use reached 85km³y^-1, accounting for more than half of total blue water use for producing the three crops. Food exported from the USA contributed the largest proportion of global water and N conservation as well as N loss reduction, but also led to substantial export-associated N losses in the country itself. Under the intensification scenario, the converging water and N use intensities across countries result in a more balanced world; crop trade will generally decrease, and global water resources conservation and N pollution reduction associated with the trade will reduce accordingly. The study provides useful information to understand the implications of agricultural intensification for international crop trade, crop water use and N pollution patterns in the world.

Global patterns of crop yield stability under additional nutrient and water inputs

Agricultural production must increase to feed a growing and wealthier population, as well as to satisfy increasing demands for biomaterials and biomass-based energy. At the same time, deforestation and land-use change need to be minimized in order to preserve biodiversity and maintain carbon stores in vegetation and soils. Consequently, agricultural land use needs to be intensified in order to increase food production per unit area of land. Here we use simulations of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 to assess implications of input-driven intensification (water, nutrients) on crop yield and yield stability, which is an important aspect in food security. We find region- and crop-specific responses for the simulated period 1980-2009 with broadly increasing yield variability under additional nitrogen inputs and stabilizing yields under additional water inputs (irrigation), reflecting current patterns of water and nutrient limitation. The different models of the GGCMI ensemble show similar response patterns, but model differences warrant further research on management assumptions, such as variety selection and soil management, and inputs as well as on model implementation of different soil and plant processes, such as on heat stress, and parameters. Higher variability in crop productivity under higher fertilizer input will require adequate buffer mechanisms in trade and distribution/storage networks to avoid food price volatility.

Analyzing and modelling the effect of long-term fertilizer management on crop yield and soil organic carbon in China

This study analyzes the influence of various fertilizer management practices on crop yield and soil organic carbon (SOC) based on the long-term field observations and modelling. Data covering 11 years from 8 long-term field trials were included, representing a range of typical soil, climate, and agro-ecosystems in China. The process-based model EPIC (Environmental Policy Integrated Climate model) was used to simulate the response of crop yield and SOC to various fertilization regimes. The results showed that the yield and SOC under additional manure application treatment were the highest while the yield under control treatment was the lowest (30%-50% of NPK yield) at all sites. The SOC in northern sites appeared more dynamic than that in southern sites. The variance partitioning analysis (VPA) showed more variance of crop yield could be explained by the fertilization factor (42%), including synthetic nitrogen (N), phosphorus (P), potassium (K) fertilizers, and fertilizer NPK combined with manure. The interactive influence of soil (total N, P, K, and available N, P, K) and climate factors (mean annual temperature and precipitation) determine the largest part of the SOC variance (32%). EPIC performs well in simulating both the dynamics of crop yield (NRMSE = 32% and 31% for yield calibration and validation) and SOC (NRMSE = 13% and 19% for SOC calibration and validation) under diverse fertilization practices in China. EPIC can assist in predicting the impacts of different fertilization regimes on crop growth and soil carbon dynamics, and contribute to the optimization of fertilizer management for different areas in China.

Detection and attribution of nitrogen runoff trend in China's croplands

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990-2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China.

Modeling future water footprint of barley production in Alberta, Canada: Implications for water use and yields to 2064

Despite the perception of being one of the most agriculturally productive regions globally, crop production in Alberta, a western province of Canada, is strongly dependent on highly variable climate and water resources. We developed agro-hydrological models to assess the water footprint (WF) of barley by simulating future crop yield (Y) and consumptive water use (CWU) within the agricultural region of Alberta. The Soil and Water Assessment Tool (SWAT) was used to develop rainfed and irrigated barley Y simulation models adapted to sixty-seven and eleven counties, respectively through extensive calibration, validation, sensitivity, and uncertainty analysis. Eighteen downscaled climate projections from nine General Circulation Models (GCMs) under the Representative Concentration Pathways 2.6 and 8.5 for the 2040-2064 period were incorporated into the calibrated SWAT model. Based on the ensemble of GCMs, rainfed barley yield is projected to increase while irrigated barley is projected to remain unchanged in Alberta. Results revealed a considerable decrease (maximum 60%) in WF to 2064 relative to the simulated baseline 1985-2009 WF. Less water will also be required to produce barley in northern Alberta (rainfed barley) than southern Alberta (irrigated barley) due to reduced water consumption. The modeled WF data adjusted for water stress conditions and found a remarkable change (increase/decrease) in the irrigated counties. Overall, the research framework and the locally adapted regional model results will facilitate the development of future water policies in support of better climate adaptation strategies by providing improved WF projections.

Nitrification inhibitors mitigated reactive gaseous nitrogen intensity in intensive vegetable soils from China

Nitrification inhibitors, a promising tool for reducing nitrous oxide (N₂O) losses and promoting nitrogen use efficiency by slowing nitrification, have gained extensive attention worldwide. However, there have been few attempts to explore the broad responses of multiple reactive gaseous nitrogen emissions of N₂O, nitric oxide (NO) and ammonia (NH₃) and vegetable yield to nitrification inhibitor applications across intensive vegetable soils in China. A greenhouse pot experiment with five consecutive vegetable crops was performed to assess the efficacies of two nitrification inhibitors, namely, nitrapyrin and dicyandiamide on reactive gaseous nitrogen emissions, vegetable yield and reactive gaseous nitrogen intensity in four typical vegetable soils representing the intensive vegetable cropping systems across mainland China: an Acrisol from Hunan Province, an Anthrosol from Shanxi Province, a Cambisol from Shandong Province and a Phaeozem from Heilongjiang Province. The results showed soil type had significant influences on reactive gaseous nitrogen intensity, with reactive gaseous nitrogen emissions and yield mainly driven by soil factors: pH, nitrate, C:N ratio, cation exchange capacity and microbial biomass carbon. The highest reactive gaseous nitrogen emissions and reactive gaseous nitrogen intensity were in Acrisol while the highest vegetable yield occurred in Phaeozem. Nitrification inhibitor applications decreased N₂O and NO emissions by 1.8-61.0% and 0.8-79.5%, respectively, but promoted NH₃ volatilization by 3.2-44.6% across all soils. Furthermore, significant positive correlations were observed between inhibited N₂O+NO and stimulated NH₃ emissions with nitrification inhibitor additions across all soils, indicating that reduced nitrification posed the threat of NH₃ losses. Additionally, reactive gaseous nitrogen intensity was significantly reduced in the Anthrosol and Cambisol due to the reduced reactive gaseous nitrogen emissions and increased yield, respectively. Our findings highlight the benefits of nitrification inhibitors for integrating environment and agronomy in intensive vegetable ecosystems in China.