North China Plain Research Articles

Accounting for Greenhouse Gas Emissions in the Agricultural System of China Based on the Life Cycle Assessment Method

Agricultural systems contribute nearly one-third of global anthropogenic GHG emissions and are an important source of GHGs globally. The clarification of the GHG emission pattern from agriculture is of paramount importance in the establishment of an agricultural emission reduction mechanism and the realization of China’s dual-carbon target. Based on the life cycle assessment method (LCA), this paper comprehensively quantifies the greenhouse gas emissions from the agricultural system in China, encompassing rice, wheat, and corn cultivation as well as animal husbandry including cows, horses, donkeys, mules, camels, pigs, and sheep. The analysis covers the period 2000–2020 and examines the spatial distribution, temporal trends, and structural changes in the greenhouse gas emissions within China’s agriculture sector. The main results are as follows: (1) from 2000 to 2020, China witnessed a consistent upward trajectory in its total agricultural GHG emissions, exhibiting an average annual growth rate of 0.73%. Notably, methane (CH4) emissions emerged as the largest contributor, displaying an overall fluctuating pattern. Carbon dioxide (CO2) emissions demonstrated intermittent increases with a noteworthy annual growth rate of 3%, signifying the most rapid expansion within this context. Conversely, nitrous oxide (N2O) emissions experienced decline over the specified period. (2) GHG emissions from cultivation demonstrate an upward trajectory, primarily driven by the CH4 emissions originating from rice cultivation and CO2 resulting from straw incineration. Conversely, GHG emissions stemming from animal husbandry declined, with the primary source being CH4 emissions arising from animal enteric fermentation. Agricultural N2O emissions predominantly arise due to manure management and nitrogen fertilizer application. (3) Agricultural greenhouse gas emissions exhibit significant variations in spatial distribution, gradually concentrating towards the North China Plain, the middle and lower reaches of the Yangtze River, and the northeast. Specifically, agricultural CH4 emissions are progressively concentrated in China’s pivotal rice-growing regions, encompassing the middle and lower reaches of the Yangtze River Plain, as well as livestock breeding areas like Inner Mongolia. Agricultural CO2 emissions primarily concentrate in dryland crop production zones such as North China and Northeast China. Meanwhile, Agricultural N2O emissions predominantly occur in Inner Mongolia and the North China Plain. China’s agricultural greenhouse gas emissions in 2020 show a significant spatial clustering effect, with hotspots primarily concentrated in Shandong, Anhui, Henan, and other regions and cold spots focused in the western and southern areas. The emission patterns of agricultural GHGs are closely intertwined with farming practices, regional development levels, and national policy; hence, tailored measures for emission reduction should be formulated based on specific crop types, livestock categories, agricultural production activities, and regional development characteristics.

Influences of shallow groundwater depth on N2O diffusion along the soil profile of summer maize fields in North China Plain

The emissions of nitrous oxide (N2O) from agricultural fields are a significant contribution to global warming. Understanding the mechanisms of N2O emissions from agricultural fields is essential for the development of N2O emission mitigation strategies. Currently, there are extensive studies on N2O emissions on the surface of agricultural soils, while studies on N2O fluxes at the interface between the saturated and unsaturated zones (ISU) are limited. Uncertainties exist regarding N2O emissions from the soil-shallow groundwater systems in agricultural fields. In this study, a three-year lysimeter experiment (2019–2020, 2022) was conducted to simulate the soil-shallow groundwater systems under four controlled shallow groundwater depth (SGD) (i.e., SGD = 40, 70, 110, and 150 cm) conditions in North China Plain (NCP). Weekly continuous monitoring of N2O emissions from soil surface, N2O concentration in the shallow groundwater and the upper 10 cm of pores at the ISU, and nitrogen cycling-related parameters in the soil and groundwater was conducted. The results showed that soil surface N2O emissions increased with decreased shallow groundwater depth, and the highest emissions of 96.44 kg ha−1 and 104.32 kg ha−1 were observed at G2 (SGD = 40 cm) in 2020 and 2022. During the observation period of one maize growing season, shallow groundwater acted as a sink for the unsaturated zone when the groundwater depth was 40 cm, 70 cm, and 110 cm. However, when SGD was 150 cm, shallow groundwater became a source for the unsaturated zone. After fertilization, the groundwater in all treatment plots behaved as a sink for the unsaturated zone, and the diffusion intensity decreased with increasing SGD. The results would provide a theoretical basis for cropland water management to reduce N2O emissions.

Responses of the Key Phenological Characteristics and Carbon Flux of Winter Wheat to Climatic Time-Lag Effects and Crop Management on the North China Plain

Responses of the Key Phenological Characteristics and Carbon Flux of Winter Wheat to Climatic Time-Lag Effects and Crop Management on the North China Plain

Impacts of Northward Typhoons on Autumn Haze Pollution Over North China Plain

AbstractAlthough air quality in China has improved substantially over recent years, haze pollution events still occur frequently, especially over the North China Plain (NCP). Previous studies showed that typhoons are conducive to regional pollution events in eastern China; however, the underlying mechanism and quantitative understanding of the typhoons' impact on haze pollution remain unclear there. Here, based on ground‐based and satellite observations, reanalysis data, and model simulations, we show that northward typhoons approaching China are essential for autumn haze pollution over NCP. Elevated relative humidity levels and enhanced pollution accumulation, caused by northward typhoons and the corresponding high‐pressure systems, are responsible for the pollution enhancements over NCP. Compared with episodes without typhoon influence, cities near Taihang and Yan Mountain suffer from heavier haze pollution when typhoons approach, with PM2.5 concentrations increasing from 87.1 to 106.4 μg m−3. More water vapor from the Yellow and Bohai Seas and pollutants from eastern China are transported to these cities by typhoon‐induced southeasterly wind anomalies, facilitating the chemical formation of aerosols there. In addition, by the block of mountains, these southeasterly wind anomalies also lead to stronger local accumulation over cities and an elevation of pollutants along the mountains. What is more, with the implementation of emission reduction, the relative changes of PM2.5 concentrations between typhoon‐induced episodes and no‐typhoon episodes increase. This work highlights the importance of understanding the impact of synoptical weather on PM2.5 transport, accumulation, and formation processes in haze pollution mitigation in eastern China.

Assessing the impacts of climate change on crop yields, soil organic carbon sequestration and N2O emissions in wheat–maize rotation systems

Straw retention has been widely implemented to increase soil organic carbon (SOC) sequestration and greenhouse gas (GHG) mitigation for global agriculture. However, the combined effects of long-term straw retention on crop production, SOC sequestration and GHG emissions response to climate change remain unknown. Two nearby wheat–maize rotation field experiments in the North China Plain, combined with local weather, soil and agronomic measurements, were used to evaluate the applicability of the SPACSYS model. The model was then applied to assess the response of crop yield, SOC storage and nitrous oxide (N2O) emissions to three nitrogen fertilizer application levels (100, 200, 400 kg N ha−1) and three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) under straw retention for the wheat–maize rotation systems during 2021–2100. In general, the model was reasonably accurate with R2 and model efficiency (EF) ranging from 0.25 to 0.96 and 0.32–0.94, respectively. Climate change decreased wheat yield by 4–39%, while maize showed a slight increase (3%) under RCP2.6 and a decrease of 1–15% under RCP4.5 and RCP8.5. The SOC storage in the 0–20 cm soil increased at a rate of 73–195 kg C ha−1 yr−1 but decreased within the upper 1 m soil at 280–390 kg C ha−1 yr−1. Climate change reduced the positive effect of SOC sequestration except in RCP2.6 and stimulated substantial N2O emissions ranging from 1–7.5 to 2.9–23.2 kg N ha−1 yr−1, consequently, the global warming potential increased from –79–2555 to 548–9357 kg CO2-eq ha−1 yr−1 under various N fertilizer application levels. This study reveals that the negative feedback under climate change with modelling approach, and extensive efforts are needed to make adaptations to ensure food production and reduce GHG emissions in the future.

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning

The coordinated control of PM2.5 and O3 pollution has become a critical factor restricting the improvement of air quality in China. In this work, precursors and related influencing factors were utilized to establish PM2.5 and O3 estimation models in the North China Plain (NCP), the Yangzi River Delta (YRD), and the Pearl River Delta (PRD) using a multi-task-learning (MTL) model. The prediction accuracy of these three MTL models was high, with R2 values ranging from 0.69 to 0.83. Subsequently, these MTL models were used to quantitatively reveal the relative importance of each factor to PM2.5 and O3 collaborative pollution simultaneously. Precursors and meteorological factors were the two most critical influencing factors for PM2.5 and O3 pollution in three regions, with their relative importance values larger than 29.99% and 15.89%, respectively. Furthermore, these models were used to reveal the response of PM2.5 and O3 to each precursor in each region. In the NCP and the YRD, the two most important precursors of PM2.5 pollution are SO2 and HCHO, while the two most critical factors for O3 pollution are HCHO and NO2. Therefore, SO2 and VOC emissions reduction is the most important measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in these two regions. In terms of the PRD, SO2 and NO2 are the most important precursors of PM2.5 pollution, while the most important precursors for O3 pollution are HCHO and SOX, respectively. Thus, NO2, SO2, and VOC emission reduction is the most critical measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in the PRD. Overall, this study provides clues and references for the control of PM2.5 and O3 collaborative pollution in the NCP, the YRD, and the PRD.

The impacts of irrigation methods and regimes on the water and nitrogen utilization efficiency in subsoiling wheat fields

Long-term rotary tillage limit water infiltration and crop productivity in North China Plain (NCP). The practice of subsoiling to fracture plow pans has made beneficial impacts on soil surface structure and water infiltration. Further, appropriate irrigation method coupled with irrigation regime can improve crop productivity in subsoiling condition. A three-year field trial (2020–2023) was carried out to assess the effects of the irrigation regime and the method on winter wheat evapotranspiration (ET), grain yield (GY), water productivity (WP), partial factor productivity from applied nitrogen (PPFN), and economic analysis. The three irrigation regimes were irrigated when soil moisture levels decreased to 70%, 60% and 50% of the field capacity (referred as H, M and L) and two irrigation methods were the surface drip irrigation (SDI) and the micro-sprinkler irrigation (MSI). The traditional flood irrigation with 70% of the field capacity in subsoiling filed was CK. The results showed optimizing irrigation method and irrigation regime significantly influenced ET, GY, WP, PPFN, and net incomes. As the irrigation amount increased, the ET first increased while GY, WP, PPFN, and net incomes increased and then slightly decreased. Based on the three-year average, the maximum GY of 9454 kg ha−1 and the net income of 11089 yuan ha−1 was achieved in SDI-M, which had WP of 2.3 kg m−3 and PPFN of 39.4 kg kg−1. At the same time, SDI-M did not result in much increase of ET (average of 405.1 mm in three seasons). Considering comprehensively ET, GY, WP, PPFN, and net incomes, to irrigate when soil moisture decreases to 60% of the field capacity by surface drip irrigation was the optimal strategy in all aspects. These results will provide a scientific reference for irrigation management in subsoiling wheat field in NCP, as well as in similar production areas worldwide.

Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method

Accurate estimation of ambient ozone concentrations is key to evaluating its detrimental impacts on human health and the ecological environment. While China has made strides in modeling ambient ozone exposure, nationwide modeling performance and spatial resolution need improvement. To address this, we developed an advanced three-level method, integrating multi-source data as predictors to estimate daily full-coverage ozone concentrations across China, with a 5-km spatial resolution. This modeling method employs subregional division, machine-learning models tailored to each subregion, and a stacking approach for estimate integration. This approach achieved substantial daily scale model performance, yielding R2 [root mean square error] values of 0.94, 0.83, and 0.74 [9.63 μg/m³, 16.59 μg/m³, 20.97 μg/m³] for sample-based, city-based, and temporal 10-fold cross-validation, respectively. Our modeling process identified six subregions for ground-level ozone modeling: Northeast (NE), North China Plain (NCP), Lower and Middle Reaches Plain of the Yangtze River (LRPYR), Southeast (SE), Southwest (SW), and Middle-West (MW). Among multi-source predictors, temperature and humidity are the most important modeling predictors in all subregions. Validation and comparative analyses highlighted the enhancement of overall model performance through subregional division and stacking machine-learning models, particularly for improving predictive accuracy on days without ground-level ozone data. The resultant estimates showed that high ozone exposure in 2020 was concentrated in the spring and summer. Our method and findings hold potential for improving ground-level ozone estimation and advancing ozone-related epidemiological research and environmental management.

Variations in optical properties of water- and methanol-soluble organic carbon in PM2.5 in Tianjin and Handan over the Wintertime of 2018–2020

The impact of the Three-year Action Plan for winning the “blue sky defense battle” on the pollution levels and chemical characteristics of fine particulate matter has been intensively studied over the past few years. However, the effect of air pollution control on light absorption of brown carbon remains unclear. In this study, the light absorption and fluorescence properties of water- and methanol-soluble organic carbon (WSOC and MSOC) in PM2.5 in Tianjin and Handan in North China Plain were measured and evaluated for three consecutive winters during the Three-year Blue-Sky Action (2018–2020). Overall, the WSOC and MSOC mass concentrations and light absorption coefficients of WSOC and MSOC at 365 nm generally decreased from 2018 to 2020, indicating that the emission of solvent-soluble organic carbon has been effectively controlled with the implementation of pollution abatement measures, such as the enforcement of clean energy policies. The light absorption coefficient and mass absorption efficiency at 365 nm of MSOC were 1.41–3.82 and 1.13–1.72 times higher than that of WSOC, respectively. Fluorescent spectra coupled with parallel factor analysis indicated that less‑/highly‑oxygenated humic-like (LO-/HO-HULIS) and non-N aromatic species (non-Nas) were the major fluorophores in both WSOC and MSOC in the two cities, with LO-HULIS and HO-HULIS as two major contributors to WSOC light absorption, while both HULIS and non-Nas fluorophores had a strong correlation with MSOC light absorption. Correlation analysis with source tracers and fluorescent indices indicated that the influence of combustion emissions remains significant, and secondary formation made a non-negligible contribution to the light absorption and fluorescent components of WSOC and MSOC in both cities. This work will contribute to a better understanding of the evolution of concentration levels and optical properties of WSOC and MSOC during pollution abatement and will inform the subsequent development of measures to achieve a win-win situation for both air quality and climate change.

Nighttime warming and nitrogen addition effects on the microclimate of a freshwater wetland dominated by Phragmites australis

The critical impacts of microclimate on carbon (C) cycling have been widely reported. However, the potential effects of global change on wetland microclimate remain unclear, primarily because of the absence of field manipulative experiment in inundated wetland. This study was designed to examine the effects of nighttime warming and nitrogen (N) addition on air, water, and sediment temperature and also reveal the controlling factors in a Phragmites australis dominated freshwater wetland on the North China Plain. Nighttime warming increased daily air, water, and sediment temperature by 0.24 °C, 0.27 °C, and 0.36 °C, respectively. The diurnal temperature range of water was decreased by 0.44 °C under nighttime warming, whereas warming had no effect on diurnal temperature range of air and sediment. In addition, N addition caused a reduction of 0.20 °C and 0.14 °C in daily water and sediment temperature by increasing vegetation coverage. There was a significant interaction between nighttime warming and N addition on water temperature. Furthermore, the vapor pressure deficit is the main factor affecting the extent of the warming-induced increases in air temperature. The changes of height and leaf area index of Phragmites australis are responsible for the cooling effects in the N addition plots. This study provides empirical evidence for the positive climate warming – microclimate feedback in freshwater wetland. However, N deposition leads to decreased water and sediment temperature. Our findings highlight the importance of incorporating the differential impacts of nighttime warming and N addition on air, water, and sediment temperature into the predictions of wetland C cycling responses to climate change.

The Study of Various Regression Models Establishment to Identify Farmland Soil Moisture Content at Different Depths Using Unmanned Aerial Vehicle Multispectral Data: A Case in North China Plain

Soil moisture content is one of the most important soil indices for agriculture production. With the increasing food requirement and limited irrigation water sources, it is of great significance to accurately and quickly measure the soil moisture content for precision irrigation, especially in deficient agricultural areas, such as North China Plain. To achieve this goal, more attention was paid to the application of unmanned aerial vehicle multispectral reflectance technology. However, it was urgent to enhance the regression models between spectral data and soil realistic moisture content, and there were limited studies about the regression research on deep soil layers. Thus, the farmland of winter wheat–summer maize double cropping at Yongnian District, Hebei, North China, was selected as the study area. A six-band multispectral camera mounted on a low-altitude unmanned aerial vehicle (UAV) was used to obtain the field spectral reflectance with bands from 470~810 nm, and meanwhile, soil moisture content at different depths (10, 20, 30, 40, 50, and 60 cm) was measured after maize sowing. Unary linear regression (ULR), multivariate linear regression (MLR), ridge regression (RR), and an artificial neural network (ANN) were employed to establish regression models. The results demonstrated that the sensitive bands of spectral reflectance were 690 nm, 470 nm, and 810 nm. Those models all established significant regression at the depths of 0–20 cm and 40–60 cm, particularly at 10, 50, and 60 cm soil layers. However, for a depth of 20–40 cm, the prediction accuracy was generally lower. Among MLR, RR, and BP models, the MLR exhibited the highest identification accuracy, which was most recommended for the application. The findings of this study provide technical guidance and effective regression for the multispectral reflectance on the farmland of North China Plain, especially for deep soil layer moisture prediction.

Tracking shallow and deep groundwater storage changes in North China Plain with improved fusion method and hybrid spectral analysis approach

The North China Plain (NCP) is a critical agricultural hub in China, confronting a significant issue of groundwater depletion. However, different terrestrial water storage (TWS) change products from the Gravity Recovery and Climate Experiment (GRACE) satellites differ considerably in estimating groundwater depletion rates in the NCP. It is urgent to optimize these different products to obtain an elevated understanding of regional groundwater storage (GWS) changes. The generalized three-cornered hat (GTCH) method is used to quantify uncertainties of different GRACE products, and this prior information is integrated with the Bayesian model averaging (BMA) method to fuse GRACE products. Results demonstrate that the GTCH + BMA method can generate uniform GWS products with heightened precision, enhanced coherence, and diminished uncertainty in the NCP. After that, the shallow and deep GWS are meticulously differentiated based on the high-coverage in-situ data, and the shallow GWS (2005.1–2016.8) (-13.7 ± 4.6 mm/yr) declined faster than deep GWS (-6.05 ± 3.9 mm/yr) in the NCP. To further explore the primary factors driving these changes, the latest Hankel Spectrum Analysis (HSA) method combined with ridge regression simulation is employed to decouple the change components of human and natural factors in the shallow and deep GWS. The first attempt to give the contribution of human and natural factors to shallow GWS is 77.18 %/22.82 %, and to deep GWS is 87.59 %/12.41 %.

Long-term variations in surface ozone at the Longfengshan Regional Atmosphere Background Station in Northeast China and related influencing factors

Surface ozone (O3) is a crucial air pollutant that affects air quality, human health, agricultural production, and climate change. Studies on long-term O3 variations and their influencing factors are essential for understanding O3 pollution and its impact. Here, we conducted an analysis of long-term variations in O3 during 2006–2022 at the Longfengshan Regional Atmosphere Background Station (LFS; 44.44°N, 127.36°E, 330.5 m a.s.l.) situated on the northeastern edge of the Northeast China Plains. The maximum daily 8-h average (MDA8) O3 fluctuated substantially, with the annual MDA8 decreasing significantly during 2006–2015 (−0.62 ppb yr−1, p < 0.05), jumping during 2015–2016 and increasing clearly during 2020–2022. Step multiple linear regression models for MDA8 were obtained using meteorological variables, to decompose anthropogenic and meteorological contributions to O3 variations. Anthropogenic activities acted as the primary drivers of the long-term trends of MDA8 O3, contributing 73% of annual MDA8 O3 variability, whereas meteorology played less important roles (27%). Elevated O3 at LFS were primarily associated with airflows originating from the North China Plain, Northeast China Plain, and coastal areas of North China, primarily occurring during the warm months (May–October). Based on satellite products of NO2 and HCHO columns, the O3 photochemical regimes over LFS revealed NOx-limited throughout the period. NO2 increased first, reaching peak in 2011, followed by substantial decrease; while HCHO exhibited significant increase, contributing to decreasing trend in MDA8 O3 during 2006–2015. The plateauing NO2 and decreasing HCHO may contribute to the increase in MDA8 O3 in 2016. Subsequently, both NO2 and HCHO exhibited notable fluctuations, leading to significant changes in O3. The study results fill the gap in the understanding of long-term O3 trends in high-latitude areas in the Northeast China Plain and offer valuable insights for assessing the impact of O3 on crop yields, forest productivity, and climate change.

Characteristic of soil moisture utilisation with different water-sensitive cultivars of summer maize in the North China Plain

Characteristic of soil moisture utilisation with different water-sensitive cultivars of summer maize in the North China Plain

Holocene flood records and human impacts implied from the pollen evidence in the Daming area, North China Plain

Understanding the environmental significance of pollen and spores in alluvial plains is important for stratigraphic correlation and paleoenvironmental reconstruction. This paper presents palynological data from the North China Plain and explores their relationship with paleoflood records and human impacts since the Holocene. Our data reveal that pollen concentration and pollen assemblage vary in flood deposits (including overbank deposits and slackwater deposits) and inter-flood deposits (including sandy soils and lacustrine deposits). Flood deposits have higher fern percentages (28.6%) and lower herbaceous percentages (14.8%) compared to inter-flood deposits, though slackwater deposits share similar pollen concentrations and assemblages with sandy soils. Notably, overbank deposits are characterized by pollen-poor zones and aggregation of deteriorated pollen grains, especially in Unit III (755–385 cm, ca. 3.2–2.2 ka) and Unit V (190–0 cm, after ca. 0.6 ka). These findings suggest that overbank deposits correspond to strengthened hydrodynamic conditions at the flood-peak stage. Furthermore, the indicative pollen and spores provide compelling evidence for intensifying human impact in the North China Plain since the late Holocene. An aggregation of Selaginella sinensis at the depth of 640–610 cm indicates deforestation in the uplands since ca. 2.9 ka. Similarly, a sharp increase in Malvaceae percentage at the depth of 285–215 supports historical records of initial cotton planting in the Tang Dynasty (ca. 1.4–1.1 ka). The study underscores the value of palynological analysis for reconstructing paleoenvironment and human-environment interactions, providing a robust framework for understanding landscape evolution in the North China Plain.

The effects and mechanisms of deep straw incorporation and denitrifying bacterial agents on mitigating nitrate leaching and N2O emissions in four soil types in the North China Plain

The agricultural practice of straw incorporation is often employed to enhance soil fertility, improve soil properties and reduce nitrate leaching via deep burying. Nevertheless, the tradeoff between nitrate leaching and N2O emissions caused by deep straw incorporation in various soil types remains uncertain. To address this knowledge gap, a mesocosm column experiment was established to evaluate the impact of straw amendment (NS) and of straw plus application of denitrifying bacterial agents (NSS) at column depths of 20–60 cm on NO3--N leaching and N2O emission across four soil types (brown, cinnamon, fluvo-aquic and mortar black soil) in the North China plain. The results showed that the NS and NSS treatments significantly reduced NO3--N leaching by 47%–73% and 37%–81%, respectively, across four soil types, in comparison to the control without straw and bacterial agents (N), with the greatest reduction recorded in the mortar black soil (by 73% for NS and 81% for NSS). Meanwhile, the NS and NSS treatments resulted in a significant increase in cumulative N2O emissions (by 289% for NS and 598% for NSS) in the acidic brown soil with a soil pH ranging between 4.8 and 6.5, but showed no significant effect in the remaining three soil types in which soil pH is above 7.5. When compared to the NS treatment, NSS treatment reduced the cumulative N2O emissions in the fluvo-aquic soil by 42%. We further showed that soil type is the primary driver of bacterial community composition (R2 = 62.3%; P < 0.001), followed by soil depth (R2 = 5.2%; P < 0.001). The incorporation of straw and denitrifying bacterial agents induced significant change in the bacterial community in the middle depth layers, and increases in nosZ gene abundance and the relative abundance of inoculant-similar OTUs significantly by 0.5–37 folds and 2.8–6900 folds, respectively, in all soil types, suggesting the colonization of denitrifying bacterial agents. Our results further suggested that the Streptomycetales were the most important predictors for N2O emissions, and the relative abundance of Streptomycetales was highly positively correlated with N2O emissions across the four soil types. Overall, the study suggests that deep straw incorporation as soil amendment is effective in mitigating nitrate leaching in all four soil types, especially in the fluvo-aquic soil, due to its dual effects on controlling nitrate leaching and N2O emissions. However, it is not recommended for the acidic brown soils as it may cause increased N2O emissions.

Long-term evolution and driving mechanisms of the Baiyangdian wetland based on land cover frequency characteristics

For sustainable conservation and management of wetlands, it is essential to quantify the evolution progress and driving mechanisms of wetlands. This study examined Baiyangdian Wetland as a research area, and remote images and statistical yearbooks were used as data sources. Dominant zones of wetland were defined based on spatial characteristics of land cover frequency. A transfer matrix and random forest were used to analyze the evolution process and driving mechanism of typical wetlands in the North China Plain. Based on the results, following conclusions were arrived: (1) There was significant spatial differentiation of land cover frequency characteristics in the Baiyangdian wetland, with the cultivated land-dominated zone, the aquatic vegetation-dominated zone, and the water body-dominated zone being the three core zones of wetland ecosystem evolution. (2) There was a frequent and significant change of the Baiyangdian wetland due to extreme climatic conditions. Between 1987 and 2017, the built-up land area and the water body area increased by 4.62 and 16.17 km2, respectively, while the aquatic vegetation area decreased by 20.70 km2. A combination of natural and human factors caused changes in the wetland’s structure. (3) The driving mechanisms of ecosystem evolution in the Baiyangdian wetland were influenced significantly by space, time, and land cover types. Specifically, on a spatial scale, social factors were the predominant factors contributing to changes in water body and cultivated land in the cultivated land-dominant zone and the aquatic vegetation-dominant zone. In contrast, natural factors were the dominant factors leading to changes in aquatic vegetation. There was a greater contribution from social factors to the driving mechanism of changes in the water body, aquatic vegetation, and cultivated land in the water body-dominant zone than from natural factors. On a temporal scale, it was primarily influenced by natural factors in the early stages and socio-economic factors in the late stages, with the year 2002 serving as a turning point. The scale of land cover types can be seen primarily in the differences among the agricultural lands, aquatic vegetation, and water body. In particular, the Secondary Industry Out Value (SIOV) was highly sensitive to changes in the cultivated area. In addition, cumulative evaporation (EC) has been identified as the main factor responsible for the evolution of aquatic vegetation, which is closely related to the changes in water body.

Genetic diversity and phylogeography of Japanese brome (Bromus japonicus Thunb. ex Murr.) populations in China

Japanese brome (Bromus japonicus Thunb. ex Murr.) is a pervasive annual weed with wide distribution in winter wheat fields across the North China Plain. In this study, we researched the genetic diversity and phylogeography of 24 B. japonicus populations in China based on total genomic DNA and chloroplast DNA. The results showed that 106 fragments were scored using 12 inter-simple sequence repeat (ISSR) primers, and 101 fragments (95.28%) were polymorphic. The findings revealed substantial genetic diversity and differentiation among populations from different locations (Ht = 0.2125, Hs = 0.0730, Gst = 0.6562 and Nm = 0.2619). Mean values of Nei’s genetic diversity (H) and Shannon index of diversity (I) were 0.0731 and 0.1068, respectively. In addition, 15 haplotypes were identified based on combined cpDNA regions among the 24 populations exhibiting abundant haplotype (gene) diversity and nucleotide diversity. The AMOVA based on ISSR and cpDNA both showed that genetic variation mainly exists among populations rather than within them. The STRUCTURE analysises of ISSR and cpDNA indicated that geographical location and genetic relationship had no significant correlation. The haplotype network also illustrated that the widespread haplotypes (H1, H2) might represent ancient polymorphism. The results obtained in this study demonstrate the presence of extensive genetic variability among and within the Chinese populations of B. japonicus, which is likely to contribute significantly to its adaptability and infestation as a weed species.

Spring Irrigation Reduces the Frequency and Intensity of Summer Extreme Heat Events in the North China Plain

AbstractIrrigation has distinct impacts on extreme temperatures. Due to the carryover effect of soil moisture into other seasons, temperature impacts of irrigation are not limited to irrigated seasons. Focusing on the North China Plain, where irrigation occurs in both spring (March‐April‐May) and summer (June‐July‐August), with a higher proportion of irrigation water applied during spring, we investigate the impact of spring irrigation on summer extreme heat events. Based on partial correlation analysis of data products, we find positive correlations between spring and summer soil moisture, suggesting that spring irrigation‐induced water surplus persists into the following summer and affects regional climate by impacting surface energy partitioning. Regional climate simulations confirm cross‐seasonal climatic effects and show that spring irrigation reduces the frequency and intensity of summer extreme heat events by approximately −2.5 days and −0.29°C, respectively. Our results highlight the importance of the cross‐seasonal climatic effect of irrigation in mitigating climate extremes.

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha−1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha−1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha−1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha−1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.