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Wetlands are the largest natural source of methane to the atmosphere. In freshwater mineral-soil wetlands, about 30–90% of methane fluxes are mediated by plants through a reversal of mechanisms in place to transport oxygen into the roots as an adaptation to the predominantly anoxic conditions in wetland soils. The rates of methane transport by plants, regulated by photosynthesis and stomatal conductance, are highly variable and are not well represented in models due to a lack of observational data, leading to high variability in model results.

Credit: Jim West / Alamy Stock Photo

Jorge Villa from Ohio State University, USA, and colleagues investigate methane flux, plant-mediated methane transport and carbon uptake in three plant species (cattail, American lotus and water lily). They find that plant conductance of methane depends on the species as well as leaf area, and varies intra-seasonally. Although methane flux and CO2 uptake were correlated, this relationship cannot be generalized across plant functional types. Nevertheless, using species — distinguished based on whether gas transport is stomatal-controlled — could improve model predictions of wetland methane emissions. More

AGAGE data for marine N2O studies
The network of Advanced Global Atmospheric Gases Experiment (AGAGE) stations has been quantifying greenhouse gas levels including N2O since the late 1970s27. This global network now produces high frequency, high precision measurements; the data improved substantially in the mid-1990s with the incorporation of higher precision methodologies28. N2O data from the last ~20 years are measured every 40 min (Fig. 1b, Supplementary Figs. 1 and 2) and have a precision on any individual measurement of 0.1 ppb but much higher confidence for the ensemble28,29. Combining these measurements with atmospheric back trajectories generated by the HYSPLIT4 model from monitoring stations (Fig. 1a, Supplementary Data 1), we map the relationship between atmospheric N2O concentrations and marine O2 content. Similar analyses have previously been conducted to investigate recent continuing sources of chlorofluorocarbons from land30. The marine environment is dynamic, and time-resolution is difficult to sample for many biologically-active chemical parameters requiring in situ point measurements. The AGAGE dataset, however, does not suffer the same restrictions, and the high-frequency measurements can be utilized to investigate variability in detail, including the important interannual dynamics that arise due to the influence of El Niño–Southern Oscillation (ENSO) on these regions.
Land sources could obscure the attribution of marine N2O, so all back trajectories that intersect continents over a 20-day model run are excluded in our analysis. This rigid protocol limits the choice of stations and marine areas. Multiple stations were considered for analysis, but eliminated as being ill-suited for identifying features in the OMZs. The land and monsoonal influence in the Arabian Sea obscures any marine signal in that OMZ. Similarly, the available high precision North Pacific atmospheric measurement stations are located along the California coast and in Hawaii, and do not communicate with the eastern tropical North Pacific OMZ before mixing homogenizes any advected signal (Supplementary Fig. 3). The station at American Samoa, however, permits excellent monitoring of the ETSP. The easterly trade winds rapidly transport air parcels intersecting the region of low O2 across the Pacific (scale of days to weeks) before turbulent mixing with other air masses is able to fully mask the OMZ signal (Fig. 2). Moreover, along this trajectory, air tends to remain in the troposphere (Supplementary Fig. 4), minimizing stratospheric impacts on surface conditions. The raw N2O data from Samoa, when de-trended for the long-term anthropogenic effect and de-seasonalized to minimize the intra-annual oscillation driven largely by interhemispheric transport from the north as the Intertropical Convergence Zone moves southward during boreal winter (Supplementary Figs. 1 and 2), reveal striking relationships with the ocean biogeochemical state (Fig. 3).
Fig. 2: Back trajectory of Samoa atmospheric N2O.

Locations of air parcels with their de-trended nitrous oxide measurements at Samoa are plotted (a) 5, (b) 10, (c) 15, and (d) 20 days prior to arrival at Samoa. De-trended nitrous oxide measurements are represented by the color of the corresponding dot, as anomalies relative to the station mean. The data maintain a clear spatial delineation for the full 20-day period, with much higher concentrations passing over the eastern tropical Pacific, and much lower concentrations arriving from the west and the Southern Ocean. Overlain on this map are the 10 and 20% oxygen saturation horizons at the 1026.5 kg m−3 potential density level (black contours). The orange circle indicates the location of the Samoa station.

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Fig. 3: Gridded N2O anomalies relative to the Samoa mean.

De-trended and de-seasonalized N2O concentrations from Samoa (color) are gridded based on back-trajectory locations 15 days prior, and a mean value is calculated for air passing over each 5° grid cell at that time (color). Overlain on this map are the 10 and 20% oxygen saturation horizons at the 1026.5 kg m−3 potential density level (black lines). These two data sets come from entirely independent sources, and the coincidence between them is striking; high atmospheric N2O concentrations align with the oxygen minimum zone, extending westward into the tropical Pacific and down the Chilean coast of South America. The orange circle indicates the location of the Samoa station.

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The seasonal modulation in Samoa N2O has been well-established as an effect of northern influence. However, across the Pacific sites, trajectories remain mostly well-confined to the hemispheres in which they originated (Supplementary Fig. 5). The air parcels that reach Samoa from the northern hemisphere however, do not have markedly higher N2O concentrations than their southern counterparts. Many high concentration trajectories do travel across the Isthmus of Panama from the Caribbean (Supplementary Fig. 6), but these are filtered out by the land filter. Notably, 20 days prior there are still a large number of high concentration trajectories in the southern hemisphere, which travel northward off the coast of South America before transiting to Samoa. Indeed, very few trajectories actually cross over the South American continent; most are deflected either north or south along its western edge. As a result, land emissions of N2O from South America are unlikely to have a large impact on the visible spatial signal.
Oxygen minimum zones emerge as N2O hotspots
A divergence in concentrations is visible just one-day prior, with higher concentrations arriving from the north than from the south (Supplementary Fig. 3). This pattern remains moving further back in time; the highest concentrations pass over the eastern tropical Pacific, while the lowest come from the west passing over the Southern Ocean. The pattern becomes slightly less defined between 15 and 20 days-prior, as parcels with high concentrations diverge again along the western coasts of North and South America. Given the velocity of the trade winds and the distance between Samoa and the ETSP, ~8 m s−1 and 104 km, respectively, this time scale is reasonable. Certainly the observed atmospheric concentration anomalies of N2O at Samoa are a combined result of intra-hemispheric biological production with interhemispheric transport, but the analysis presented herein aims to tease apart the biological impact from the tropical Pacific ocean from the additional seasonal supply from the N2O-enriched northern hemisphere (Supplementary Fig. 7).
In defining the OMZ boundary by the 20% O2 saturation contour on the 1026.5 kg m−3 potential density surface, the 15-day back trajectories identify higher than average N2O among the air parcels passing over the OMZ (Fig. 3). This isopycnal layer, generally at depths of ~200–500 m (shallower in the eastern Pacific), tends to co-locate the minimum O2 concentration within the water column31. Whereas water this deep does not tend to equilibrate rapidly with the atmosphere, this density surface reflects broader features in the water column, such as the existence of a shallower N2O maximum driven by microbial processes18. This shallower N2O maximum can be more easily transmitted to the surface waters from the interplay of upwelling and lateral and vertical mixing32. Mesoscale and sub-mesoscale eddies in particular, below the spatial resolution that can be resolved by these data, are likely pathways that promote mixing of deeper N2O into the surface waters33,34.
The air masses reaching Samoa that pass over the OMZ are ~0.4 ppb higher than the remainder of the South Pacific. This intense localization to the lowest O2 waters in the eastern tropical Pacific, extending southward along the Peruvian and Chilean margin, suggests a disproportionate role of OMZs and coastal upwelling waters in generating atmospheric N2O relative to the open ocean. Extension of higher N2O anomalies along the equator outside of the 20% O2 contour also remain visible west to 140° W (Fig. 3), albeit with reduced magnitude, indicative of a separate likely N2O source from equatorial upwelling and subsequent outgassing35,36. Further, the air overlying the coastal Pacific along the South American upwelling region is especially high in its N2O content, indicative of the heterogeneities that exist even within suboxic waters17. The coastal-most waters are the most productive and the resulting organic matter fuels greater microbial generation of N2O. Moreover, the depth of the anoxic onset and coincident N2O maximum is generally shallower toward the coast, increasing the communication between ocean and air. Narrow, shallow shelves within the OMZs in particular are important producers of N2O17,23,37,38, but local sources with such spatial resolution cannot be identified via our methods.
While the absolute difference between N2O mixing ratios in air that has passed over the OMZ versus those that did not is small, the abundances above the OMZs are likely much higher but are diluted to the observed anomalies after 15 days of transport and mixing. Notably, the air parcels will continue to gain N2O as they travel over supersaturated water masses or lose N2O over undersaturated areas. The coherence of the 15-day backtracked Samoan N2O signal and its clear demarcation with the ETSP highlight the significance of the OMZs for the marine N2O efflux. Indeed, the offset between the air passing over the OMZ grid cells and the rest of the South Pacific matches the average seasonal variability in the Samoan data. The seasonal cycle, which includes terrestrial sources and is heavily influenced by seasonal shifts in the intertropical convergence zone, peaks in Austral winter (January/February) with minima 0.50 ± 0.13 ppb (se) lower in summer (July/August). The rapid N2O cycling rates inherent to the highly productive OMZs cause these regions to respond quickly to time-dependent shifts in the underlying biogeochemistry and physical structure of the water column18,24.
Further modulation by El Niño and La Niña
It is known that ETSP biogeochemistry is influenced by the El Niño/La Niña oscillations of the tropical Pacific39,40. La Niña brings enhanced upwelling of nutrients to the surface, thereby increasing primary production, further de-oxygenating the subsurface region, and supplying more organic matter to drive greater denitrification and N2O production13. Conversely, during an El Niño decreased upwelling reduces the surface productivity in the eastern tropical Pacific and deepens the oxycline, thereby contracting the OMZ and decreasing N2O emissions. In this study, the near-continuous data over 21 years permits a more quantitative analysis. The period from 1996 through 2016 included both a number of strong El Niño (1997/98, 2002/03, 2009/10, 2015/16) and La Niña (1998/99, 2007/08, 2010/11) states. Our analysis consists of dividing the N2O dataset among ENSO states in two different ways, both of which produced equivalent results: First, by aligning the N2O oscillation against the Niño 3.4 index, resulting in a time lag of 3 months relative to the ENSO state; second, by producing a running integral of the Niño 3.4 index forward in time, which essentially represents a cumulative effect of prolonged El Niño/La Niña periods (Fig. 1c). The Niño 3.4 index describes a 5-month running mean of sea surface temperature anomalies in the central equatorial Pacific, and was used because it is indicative of the broader Pacific region.
Strikingly, during La Niña, air parcels passing over OMZ grid cells increase on top of their already higher than average concentrations, whereas parcels that do not pass over OMZ grid cells exhibit no systematic change (Fig. 4). This increase with La Niña is not uniform across the OMZ, however, as the lowest O2 waters ( More

Experimental site and test cultivars
A field experiment of cover crop planting in a winter fallow field was conducted in Changsha (28° 11′ N, 113° 04′ E), Hunan Province, China, from 2014–2015. The soil in the experimental field was tidal clay, with 1.16% organic carbon, 0.17% total N, and a pH of 6.15.
Experimental design and field management
A randomized block experiment was established with 3 different treatments, including cover crops (Lolium spp. and Astragalus sinicus) with chicken grazing (+ C), cover crops without chicken grazing (− C), and a bare, fallow field (CK). Each field plot covers 140 m2, and there were three replications. To prevent the movement of water between adjacent plots, ridges were covered with a plastic sheet inserted into the soil to a depth of 0.5 m.
Ryegrass and milk vetch were planted on October 10th, 2014, at seed densities of 23 and 40 kg ha−1, respectively. Thirty-day-old yellow chickens were introduced into the field on November 25th. To ensure the homogeneity of the chicken manure inputs, a 3 m × 3 m cage was used during the process of chicken grazing. There were 30 chickens in each cage. Five kilograms of corn flour was fed to the chickens in each cage daily. The corn flour was 1.8% nitrogen. The cage was moved every 7 days in the chicken-grass plot until February 2, 2015. The quantity of in situ chicken manure input into the system within the symbiotic period (69 days) in these plots was estimated to be 96.3 t ha−1 by collecting the chicken waste in an underground container. The underground container was a square with a side length of 50 cm and a height of 10 cm. There were 3 symbiotic periods in these plots, and the chicken waste samples were collected every 12 h for three days. On March 27th, 2015, the average aboveground biomass of the cover crops was 11.7 t ha−1 in the + C plot and 14.4 t ha−1 in the − C plot. All the procedures used in this experiment were conducted in accordance with the Chinese Guidelines for Animal Welfare. The experimental procedures performed in the current study were approved by the Hunan Agricultural University Institutional Animal Care and Use Committee (Changsha, China). Furthermore, all the experimental protocols, including animal handling, were performed humanly, and animal welfare was specially considered. We further confirmed that no animals were harmed or stressed during the experimental period.
The cover crops were incorporated into the soil on March 27th, and all the plots were used to grow double-season rice. The early rice cultivar ‘Zhongjiazao 17’ and the late rice cultivar ‘Xiangwanxian 12’ were used in the experiment, and their growth durations were 109 days and 115 days, respectively. Rice seedlings were transplanted on May 5th and harvested on July 12th for the early-season rice, followed by the late-season rice, which was transplanted on July 25th and harvested on October 30th. The seedlings were 35 and 25 days old in the early and late seasons, respectively. The transplantation density was 30 hills m−2 for the early rice season and 25 hills m−2 for the late rice season.
We supplied nitrogen (N) in the form of urea, calcium superphosphate for phosphorus pentoxide (P2O5), and potassium chloride for potassium oxide (K2O) in the rice growing season. The quantity of N supplied was 74 kg ha−1 in the early rice season and 102 kg ha−1 in the late rice season. Urea was applied three times during the rice season; the ratio of tillering fertilizer to panicle fertilizer (grain fertilizer) was 70:30 in the early rice season and 50:50 in the late rice season. The quantity of P2O5 and K2O supplied was 60 kg ha−1, and the same quantity was applied in both seasons. Potassium chloride was applied twice during the rice season, 50% as basal fertilizer and 50% as tillering fertilizer. The calcium superphosphate was applied as a basal fertilizer before transplantation. Water management was performed according to the technology used for double rice cropping systems (local high-yield cultivation) (Table 4).
Table 4 Experimental design16.
Full size table

Soil chemical properties
Soil samples from the 0–20 cm soil layer were used to determine the soil chemical properties. The samples were collected during cover crop harvesting, early rice harvesting and late rice harvesting. The soil samples were air dried and the soil organic matter was determined using K2Cr2O7 and concentrated H2SO4 and heating. The soil total N was determined with the Kjeldahl method, which involved two steps: (1) the digestion of the samples to convert organic N into ({text{NH}}_{4}^{ + })–N and (2) the determination of ({text{NH}}_{4}^{ + })–N in the digest. The soil C:N ratio was calculated by dividing the SOC concentration by the TN concentration. Soil ammonium N was analyzed using indophenol blue colorimetry. Soil nitrate–N was analyzed using ultraviolet spectrophotometry.
In situ CH4 and CO2 flux measurements
During the rice growing season, in situ CH4 and CO2 flux were measured with a static chamber by circulating the gas within the chamber and pipes of an ultraportable greenhouse gas analyzer (CH4/CO2/H2O Analyzer; Los Gatos Research Corp., USA). The static chamber was a square with a side length of 50 cm and a height of 120 cm. A fluted base consistent with the static chamber was inserted in the soil in advance. On the sampling dates, daytime samples were collected from 9:00–11:00 a.m. and 15:00–17:00 p.m., and nighttime samples were collected from 19:00–21.00 p.m. The testing time in each plot was 5 min. The sampling dates were 170, 185, 199, 215, 230, 252, 268, 291, 304, 322, and 347 days after the chickens were introduced into the field. The samples were collected at intervals of 14 days, plus or minus one day if the weather forecast for a sampling date was rainy.
The temperature inside the static chamber needs to be accurately recorded at a soil depth of 3 cm. Plants (excluding the border plants) were sampled from a 0.24 m2 area of each plot on the sampling date. The plant samples were manually separated into leaf and straw and/or grains. The volume of the plant samples was measured with drainage. The effective volume in the chamber was reduced to subtract the internal plant volume from the chamber. The leaf area was determined with a leaf area meter (LI-3000A, LICOR, Lincoln, NE, USA). Lastly, the plant samples were oven-dried at 70 °C to constant weight to determine the aboveground biomass.
The CO2 (F, g m−2 day−1) and CH4 (F, mg m−2 day−1) fluxes were calculated using the following formula (Eq. 1):

$$ {text{F}} = frac{{{text{P}} times {text{V}}}}{{{text{R}} times {text{A}} times left( {{text{T}} + 273.15} right)}} times frac{{{text{dc}}}}{{{text{dt}}}}, $$
(1)

where P is the atmospheric pressure under standard conditions (101.2237 × 103 Pa); V is the effective volume in the chamber (m3), the difference between the volume of the static chamber and the volume of the plant, fan and temperature recorder; R is a gas constant (8.3144 J⋅mol−1 K−1); A is the area of the chamber cover (m2); T is the average temperature at testing time inside the chamber (°C); and dc/dt is the rate of change in the concentration of CO2 and CH4.
To accurately calculate the CO2 and CH4 fluxes in the paddy field, the daytime and nighttime CO2 and CH4 fluxes on the sampling dates were calculated using the following formulas (Eq. 2–4):

$$ {text{F}}_{{{text{daytime}}}} = {text{ S}}_{{{text{daytime}}}} times {text{M}} times left( {{text{F}}_{{1}} + {text{F}}_{{2}} } right)/{2,} $$
(2)

$$ {text{F}}_{{{text{night}}}} = {text{ F}}_{{3}} times {text{S}}_{{{text{night}}}} times {text{M,}} $$
(3)

$$ {text{F}}_{{{text{day}}}} = {text{ F}}_{{{text{daytime}}}} + {text{F}}_{{{text{night}}}} , $$
(4)

where F1, F2 and F3 represent the values at 9:00–11:00 a.m. and 15:00–17:00 p.m. on sunny days and 19:00–21:00 p.m., respectively; S is the day length (s day−1) on the sampling date; and M is the relative molecular mass of CO2 or CH4 (g mol−1).
Seasonal emissions in CO2 and CH4 were calculated using the following formula (Eq. 5):

$$ {text{T }} = {text{a}} times {1}0 times left( {mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} [frac{{{text{F}}_{{text{i}}} + {text{F}}_{{{text{i}} + 1}} }}{2}left( {{text{t}}_{{{text{i}} + 1}} – {text{t}}_{{text{i}}} } right)] + frac{{{text{F}}_{{text{i}}} + {text{F}}_{{text{n}}} }}{2}} right), $$
(5)

where T (g m−2) is the total seasonal emissions, Fi and Fi+1 are the measured fluxes on two consecutive sampling days, ti+1 − ti is the number of days between the two sampling dates, 10 is the conversion coefficient from g m−2 to kg ha−1, and a is the conversion coefficient of the rice growth period (86/61 in the early season and 132/96 in the late season).
In addition, the period from early rice harvesting to late rice transplanting is 13 days. The emissions were calculated using the following formula (Eq. 6):

$$ {text{T}}_{{{text{ER}} – {text{LR}}}} = {text{ T}}_{{{text{ER}}}} /{86} times {6}.{5} + {text{T}}_{{{text{LR}}}} /{132} times {6}.{5,} $$
(6)

where TER-LR (g m−2) is the total emissions from early rice harvesting to late rice transplanting, TER and TLR are the total seasonal emissions in the early rice season and late rice season, respectively, and 86 and 132 are the number of days from sowing to harvesting in the early rice season and late rice season, respectively.
Soil microbe and dissolved carbon and nitrogen measurements
In 2014, soil was sampled from the 0–20 cm soil layer, and the sampling dates were 10, 28, 56, 74, 120, 170, 183, 199, 215, 234, 252, 268, 294, 301, 322, and 347 days after chicken grazing. Fresh soil samples were taken to determine the soil microbial carbon and nitrogen contents by chloroform fumigation-incubation and K2SO4 extraction. Soil microbial carbon (SMC, mg kg−1) = EC/0.38 and soil microbial nitrogen (SMN, mg kg−1) = EN × 0.45, where 0.33 and 0.45 are the conversion coefficients of SMC and SMN, respectively. EC and EN are the differences in organic carbon and nitrogen between fumigation and nonfumigation based K2SO4 extraction. In addition, other fresh soil samples were used to determine the soil dissolved carbon and nitrogen by K2SO4 extraction.
Yield and its components
When the rice was mature, 10 hills were sampled randomly from a 5 m2 harvest area to determine the yield components. Panicle number was counted on each hill to determine the panicle number per m2. The panicles were hand-threshed, and the filled spikelets were separated from the unfilled spikelets by submerging them in tap water. Three subsamples of 30 g of filled spikelets and 3 g of unfilled spikelets were taken to count the number of spikelets. Based on the spikelets per panicle, the grain-filling percentage (100 × filled spikelet number/total spikelet number) was determined. The grain yield was determined from a 5 m2 area in each plot and adjusted to the standard moisture content of 0.14 g H2O g−1.
Data analysis
The global warming potential (GWP) was the overall GWP of CH4 and N2O emissions per unit rice field (ha). The 100-year radiative forcing potential coefficients relative to CO2 were 25 and 298 for CH4 and N2O, respectively (IPCC, 2007). The net ecosystem exchange (NEE) was the value of Fdaytime, ecosystem respiration (Reco) was the value of Fnighttime, and gross primary production (GPP) was the sum of the NEE and Reco. The means of the indexes were organized in Excel 2016. The SD (standard deviation) of the indexes were determined by descriptive statistics with a 95% confidence interval. Analysis of variance (ANOVA) and multiple comparisons were performed using Statistix ver. 8.0 (2004) to evaluate the effects of planting cover crops and chicken grazing on the SOC, STN, C:N ratio, DOC, DON, SMN, SMC, and grain yield and its components. More

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