Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea.

The use of observation-dependent methods for crop productivity and food security assessment is challenging in data-sparse regions. This study presents a transferable framework and applies it to North Korea (NK) to assess rice productivity based on climate similarity, transferable machine-learning techniques, and extendable multi-source data. We initially divided the primary phenological stages of rice in the study region and extracted dynamic rice distributions based on Moderate Resolution Imaging Spectroradiometer products and phenological observations. We compared the performances of four representative environmentally driven models (Linear Regression, back-propagation Neural Network, Support Vector Machine, and Random Forest) in simulating rice productivity using an extensive dataset that included multi-angle vegetation monitoring, climate variables, and planting distribution information. The framework integrated an optimal environmentally driven model with agricultural management practices for transferability to predict rice productivity in NK over multiple years. Additionally, two crop growth scenarios (whole growth period (WGP) and seeding-heading period (SHP)) were compared to assess pre-harvest forecasting capabilities and identify dominant factors. Finally, independent datasets from the Food and Agriculture Organization, World Food Program, and Global Gridded Crop Models were used to validate the magnitude and spatial distribution of the predicted results. The results showed that phenological identification based on remote sensing can accurately capture rice growth characteristics and map rice distribution. Random Forest outperformed other models in simulating rice productivity variation, with r-squares of 0.87 and 0.83 in the WGP and SHP, respectively. The solar-induced chlorophyll fluorescence, maximum temperature, and evapotranspiration collectively determined approximately 40 % of the variation in yield simulated using Random Forest. Conversely, planting areas contributed over 42 % of the variation in rice production. Compared to Food and Agriculture Organization statistics, the environmentally driven framework explained 78.72 % and 76.89 % of the production variation and 69.42 % and 71.15 % of the yield variation in NK under the WGP and SHP, respectively. Moreover, the environmental management-driven framework captured over 90 % of the yield variation. The predicted spatial pattern of rice productivity exhibited significant concordance with the World Food Program and Global Gridded Crop Model reports. In summary, the proposed transferable framework for crop productivity assessment contributes to early warnings of production reduction and has the potential for scalability across various crops and data-sparse regions.

Global nitrous oxide emissions from livestock manure during 1890-2020: An IPCC tier 2 inventory.

Nitrous oxide (N2O) emissions from livestock manure contribute significantly to the growth of atmospheric N2O, a powerful greenhouse gas and dominant ozone-depleting substance. Here, we estimate global N2O emissions from livestock manure during 1890-2020 using the tier 2 approach of the 2019 Refinement to the 2006 IPCC Guidelines. Global N2O emissions from livestock manure increased by ~350% from 451 [368-556] Gg N year-1 in 1890 to 2042 [1677-2514] Gg N year-1 in 2020. These emissions contributed ~30% to the global anthropogenic N2O emissions in the decade 2010-2019. Cattle contributed the most (60%) to the increase, followed by poultry (19%), pigs (15%), and sheep and goats (6%). Regionally, South Asia, Africa, and Latin America dominated the growth in global emissions since the 1990s. Nationally, the largest emissions were found in India (329 Gg N year-1), followed by China (267 Gg N year-1), the United States (163 Gg N year-1), Brazil (129 Gg N year-1) and Pakistan (102 Gg N year-1) in the 2010s. We found a substantial impact of livestock productivity, specifically animal body weight and milk yield, on the emission trends. Furthermore, a large spread existed among different methodologies in estimates of global N2O emission from livestock manure, with our results 20%-25% lower than those based on the 2006 IPCC Guidelines. This study highlights the need for robust time-variant model parameterization and continuous improvement of emissions factors to enhance the precision of emission inventories. Additionally, urgent mitigation is required, as all available inventories indicate a rapid increase in global N2O emissions from livestock manure in recent decades.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit.

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Ammonium-derived nitrous oxide is a global source in streams.

Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020.

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Maximizing carbon sequestration potential in Chinese forests through optimal management.

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

Four decades of full-scale nitrous oxide emission inventory in China.

China is among the top nitrous oxide (N2O)-emitting countries, but existing national inventories do not provide full-scale emissions including both natural and anthropogenic sources. We conducted a four-decade (1980-2020) of comprehensive quantification of Chinese N2O inventory using empirical emission factor method for anthropogenic sources and two up-to-date process-based models for natural sources. Total N2O emissions peaked at 2287.4 (1774.8-2799.9) Gg N2O yr-1 in 2018, and agriculture-developed regions, like the East, Northeast, and Central, were the top N2O-emitting regions. Agricultural N2O emissions have started to decrease after 2016 due to the decline of nitrogen fertilization applications, while, industrial and energetic sources have been dramatically increasing after 2005. N2O emissions from agriculture, industry, energy, and waste represented 49.3%, 26.4%, 17.5%, and 6.7% of the anthropogenic emissions in 2020, respectively, which revealed that it is imperative to prioritize N2O emission mitigation in agriculture, industry, and energy. Natural N2O sources, dominated by forests, have been steadily growing from 317.3 (290.3-344.1) Gg N2O yr-1 in 1980 to 376.2 (335.5-407.2) Gg N2O yr-1 in 2020. Our study produces a Full-scale Annual N2O dataset in China (FAN2020), providing emergent counting to refine the current national N2O inventories.

Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era.

Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr-1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr-1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Net greenhouse gas balance in U.S. croplands: How can soils be part of the climate solution?

Agricultural soils play a dual role in regulating the Earth's climate by releasing or sequestering carbon dioxide (CO2 ) in soil organic carbon (SOC) and emitting non-CO2 greenhouse gases (GHGs) such as nitrous oxide (N2 O) and methane (CH4 ). To understand how agricultural soils can play a role in climate solutions requires a comprehensive assessment of net soil GHG balance (i.e., sum of SOC-sequestered CO2 and non-CO2 GHG emissions) and the underlying controls. Herein, we used a model-data integration approach to understand and quantify how natural and anthropogenic factors have affected the magnitude and spatiotemporal variations of the net soil GHG balance in U.S. croplands during 1960-2018. Specifically, we used the dynamic land ecosystem model for regional simulations and used field observations of SOC sequestration rates and N2 O and CH4 emissions to calibrate, validate, and corroborate model simulations. Results show that U.S. agricultural soils sequestered 13.2 ± 1.16 $$ 13.2\pm 1.16 $$ Tg CO2 -C year-1 in SOC (at a depth of 3.5 m) during 1960-2018 and emitted 0.39 ± 0.02 $$ 0.39\pm 0.02 $$ Tg N2 O-N year-1 and 0.21 ± 0.01 $$ 0.21\pm 0.01 $$ Tg CH4 -C year-1 , respectively. Based on the GWP100 metric (global warming potential on a 100-year time horizon), the estimated national net GHG emission rate from agricultural soils was 122.3 ± 11.46 $$ 122.3\pm 11.46 $$ Tg CO2 -eq year-1 , with the largest contribution from N2 O emissions. The sequestered SOC offset ~28% of the climate-warming effects resulting from non-CO2 GHG emissions, and this offsetting effect increased over time. Increased nitrogen fertilizer use was the dominant factor contributing to the increase in net GHG emissions during 1960-2018, explaining ~47% of total changes. In contrast, reduced cropland area, the adoption of agricultural conservation practices (e.g., reduced tillage), and rising atmospheric CO2 levels attenuated net GHG emissions from U.S. croplands. Improving management practices to mitigate N2 O emissions represents the biggest opportunity for achieving net-zero emissions in U.S. croplands. Our study highlights the importance of concurrently quantifying SOC-sequestered CO2 and non-CO2 GHG emissions for developing effective agricultural climate change mitigation measures.

Soil legacy nutrients contribute to the decreasing stoichiometric ratio of N and P loading from the Mississippi River Basin.

Human-induced nitrogen-phosphorus (N, P) imbalance in terrestrial ecosystems can lead to disproportionate N and P loading to aquatic ecosystems, subsequently shifting the elemental ratio in estuaries and coastal oceans and impacting both the structure and functioning of aquatic ecosystems. The N:P ratio of nutrient loading to the Gulf of Mexico from the Mississippi River Basin increased before the late 1980s driven by the enhanced usage of N fertilizer over P fertilizer, whereafter the N:P loading ratio started to decrease although the N:P ratio of fertilizer application did not exhibit a similar trend. Here, we hypothesize that different release rates of soil legacy nutrients might contribute to the decreasing N:P loading ratio. Our study used a data-model integration framework to evaluate N and P dynamics and the potential for long-term accumulation or release of internal soil nutrient legacy stores to alter the ratio of N and P transported down the rivers. We show that the longer residence time of P in terrestrial ecosystems results in a much slower release of P to coastal oceans than N. If contemporary nutrient sources were reduced or suspended, P loading sustained by soil legacy P would decrease much slower than that of N, causing a decrease in the N and P loading ratio. The longer residence time of P in terrestrial ecosystems and the increasingly important role of soil legacy nutrients as a loading source may explain the decreasing N:P loading ratio in the Mississippi River Basin. Our study underscores a promising prospect for N loading control and the urgency to integrate soil P legacy into sustainable nutrient management strategies for aquatic ecosystem health and water security.

Agricultural ditches are hotspots of greenhouse gas emissions controlled by nutrient input.

Agricultural ditches are pervasive in agricultural areas and are potential greenhouse gas (GHG) hotspots, since they directly receive abundant nutrients from neighboring farmlands. However, few studies measure GHG concentrations or fluxes in this particular water course, likely resulting in underestimations of GHG emissions from agricultural regions. Here we conducted a one-year field study to investigate the GHG concentrations and fluxes from typical agricultural ditch systems, which included four different types of ditches in an irrigation district located in the North China Plain. The results showed that almost all the ditches were large GHG sources. The mean fluxes were 333 µmol m-2 h-1 for CH4, 7.1 mmol m-2 h-1 for CO2, and 2.4 µmol m-2 h-1 for N2O, which were approximately 12, 5, and 2 times higher, respectively, than that in the river connecting to the ditch systems. Nutrient input was the primary driver stimulating GHG production and emissions, resulting in GHG concentrations and fluxes increasing from the river to ditches adjacent to farmlands, which potentially received more nutrients. Nevertheless, the ditches directly connected to farmlands showed lower GHG concentrations and fluxes compared to the ditches adjacent to farmlands, possibly due to seasonal dryness and occasional drainage. All the ditches covered approximately 3.3% of the 312 km2 farmland area in the study district, and the total GHG emission from the ditches in this area was estimated to be 26.6 Gg CO2-eq yr-1, with 17.5 Gg CO2, 0.27 Gg CH4, and 0.006 Gg N2O emitted annually. Overall, this study demonstrated that agricultural ditches were hotspots of GHG emissions, and future GHG estimations should incorporate this ubiquitous but underrepresented water course.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Greenhouse gas emissions from extractive industries in a globalized era.

The extractive industry consumes vast amounts of energy and is a major contributor to greenhouse gas (GHG) emissions. However, its climatic impacts have not yet been fully accounted for. In this study, we estimated the GHG emissions from extractive activities globally with a focus on China, and assessed the main emission drivers. In addition, we predicted the Chinese extractive industry emissions in the context of global mineral demand and cycling. As of 2020, GHG emissions from the global extractive industry had reached 7.7 billion tons of CO2 equivalents (CO2e), accounting for approximately 15.0% of the global anthropogenic GHG emissions (excluding GHG emissions from land use, land-use change, and forestry activities (LULUCF), with China being the largest emitter, accounting for 3.5% of global emissions. Extractive industry GHG emissions are projected to peak by 2030 or even earlier to achieve low-carbon peak targets. The most critical pathway for reducing GHG emissions in the extractive industry is to control emissions from coal mining. Therefore, reducing methane emissions from mining and washing coal (MWC) should be prioritized.

Hot moment of N2O emissions in seasonally frozen peatlands.

Since the start of the Anthropocene, northern seasonally frozen peatlands have been warming at a rate of 0.6 °C per decade, twice that of the Earth's average rate, thereby triggering increased nitrogen mineralization with subsequent potentially large losses of nitrous oxide (N2O) to the atmosphere. Here we provide evidence that seasonally frozen peatlands are important N2O emission sources in the Northern Hemisphere and the thawing periods are the hot moment of annual N2O emissions. The flux during the hot moment of thawing in spring was 1.20 ± 0.82 mg N2O m-2 d-1, significantly higher than that during the other periods (freezing, -0.12 ± 0.02 mg N2O m-2 d-1; frozen, 0.04 ± 0.04 mg N2O m-2 d-1; thawed, 0.09 ± 0.01 mg N2O m-2 d-1) or observed for other ecosystems at the same latitude in previous studies. The observed emission flux is even higher than those of tropical forests, the World's largest natural terrestrial N2O source. Furthermore, based on soil incubation with 15N and 18O isotope tracing and differential inhibitors, heterotrophic bacterial and fungal denitrification was revealed as the main source of N2O in peatland profiles (0-200 cm). Metagenomic, metatranscriptomic, and qPCR assays further revealed that seasonally frozen peatlands have high N2O emission potential, but thawing significantly stimulates expression of genes encoding N2O-producing protein complexes (hydroxylamine dehydrogenase (hao) and nitric oxide reductase (nor)), resulting in high N2O emissions during spring. This hot moment converts seasonally frozen peatlands into an important N2O emission source when it is otherwise a sink. Extrapolation of our data to all northern peatland areas reveals that the hot moment emissions could amount to approximately 0.17 Tg of N2O yr-1. However, these N2O emissions are still not routinely included in Earth system models and global IPCC assessments.

Soil respiration-driven CO2 pulses dominate Australia's flux variability.

The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO2) sink. However, the scarcity of in situ observations in remote areas prevents the deciphering of processes that force the CO2 flux variability. In this study, by examining atmospheric CO2 measurements from satellites in the period 2009-2018, we find recurrent end-of-dry-season CO2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia's CO2 balance. They cause two to three times larger seasonal variations compared with previous top-down inversions and bottom-up estimates. The pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia's semiarid regions. The suggested continental-scale relevance of soil-rewetting processes has substantial implications for our understanding and modeling of global climate-carbon cycle feedbacks.

Temperature Changes Induced by Biogeochemical and Biophysical Effects of Bioenergy Crop Cultivation.

The production of bioenergy with carbon capture and storage (BECCS) is a pivotal negative emission technology. The cultivation of dedicated crops for BECCS impacts the temperature through two processes: net CO2 removal (CDR) from the atmosphere (biogeochemical cooling) and changes in the local energy balance (biophysical warming or cooling). Here, we compare the magnitude of these two processes for key grass and tree species envisioned for large-scale bioenergy crop cultivation, following economically plausible scenarios using Earth System Models. By the end of this century, the cumulative CDR from the cultivation of eucalypt (72-112 Pg C) is larger than that of switchgrass (34-83 Pg C) because of contrasting contributions of land use change carbon emissions. The combined biogeochemical and biophysical effects are cooling (-0.26 to -0.04 °C) at the global scale, but 13-28% of land areas still have net warming signals, mainly due to the spatial heterogeneity of the biophysical effects. Our study shows that the deployment of bioenergy crop cultivation should not only be guided by the principles of maximizing yield and CDR but should also take an integrated perspective that includes all relevant Earth system feedbacks.

Hotspots, co-occurrence, and shifts of compound and cascading extreme climate events in Eurasian drylands.

Eurasian drylands are the regions that are most vulnerable to climate change. Climate extremes have caused enormous or even devastating impacts on ecosystems and the social economy in this region, and the compound climate extremes (com_CEs, two or more extreme events occurring simultaneously) and cascading climate extremes (cas_CEs, two or more extreme events occurring successively) have exacerbated these problems. However, little is known about the occurrence patterns of com_CEs and cas_CEs in the Eurasian drylands. Based on the ERA5 reanalysis data range from 1979 to 2020, we improved the methodology for the extraction of co-occurrence events and identified high-frequency types, their hotspots, and occurrence rhythms (seasonally and annually) in Eurasian drylands. Our results showed that com_CEs and cas_CEs have high similarities in the types and spatial hotspots of extreme events; however, the former has a wider geographical and spatial distribution, and the latter has a longer duration. Specifically, co-occurring drought and heatwave events (DH) frequently appear in South Asia and western mid-latitude regions during summer, while in the winter, high latitude regions should be alert to the co-occurrence of drought and low-temperature events (DT). Central Asia and the Mongolian Plateau regions are prone to frequent drought and wind events (DW), and wind and high precipitation events (WP) in the spring and autumn. We have noticed that mid-latitude may suffer from extreme events that have never occurred before, such as com_DH being scattered sporadically in the first two decades and suddenly surging in West Asia and East Asia after the year 2000, and com_DT migrating from high-latitude areas such as the Arctic Ocean coast to mid-latitudes. Our results contribute to understanding hotspots of co-occurring CEs in Eurasian drylands, where more efforts will be needed in the future, especially in mid-latitudes which may suffer extreme climate events that have never occurred before.

Forest expansion dominates China's land carbon sink since 1980.

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