An official website of the United States government
Abstract Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003–2019) of measurements at the Kellogg Biological Station Long‐Term Ecological Research (LTER)/Long‐Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn–soybean–winter wheat rotations employing conventional, no‐till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2‐day precipitation, water‐filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%–42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long‐term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha−1) and daily air temperature (>23°C) as the most influential variables; in the no‐till system, climate variables such as precipitation and air temperature were important variables. In low‐input and organic systems, where red clover (Trifolium repensL.; before corn) and cereal rye (Secale cerealeL.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low‐input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long‐term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.
more »
« less
Quantification and machine learning based N 2 O–N and CO 2 –C emissions predictions from a decomposing rye cover crop
Abstract Cover crops improve soil health and reduce the risk of soil erosion. However, their impact on the carbon dioxide equivalence (CO2e) is unknown. Therefore, the objective of this 2‐yr study was to quantify the effect of cover crop‐induced differences in soil moisture, temperature, organic C, and microorganisms on CO2e, and to develop machine learning algorithms that predict daily N2O–N and CO2–C emissions. The prediction models tested were multiple linear regression, partial least square regression, support vector machine, random forest (RF), and artificial neural network. Models’ performance was accessed using R2, RMSE and mean of absolute value of error. Rye (Secale cerealeL.) was dormant seeded in mid‐October, and in the following spring it was terminated at corn's (Zea maysL.) V4 growth stage. Soil temperature, moisture, and N2O–N and CO2–C emissions were measured near continuously from soil thaw to harvest in 2019 and 2020. Prior to termination, the cover crop decreased N2O–N emissions by 34% (p = .05), and over the entire season, N2O–N emissions from cover crop and no cover crop treatments were similar (p = .71). Based on N2O–N and CO2–C emissions over the entire season and the estimated fixed cover crop‐C remaining in the soil, the partial CO2ewere −1,061 and 496 kg CO2eha–1in the cover crop and no cover crop treatments, respectively. The RF algorithm explained more of the daily N2O–N (73%) and CO2–C (85%) emissions variability during validation than the other models. Across models, the most important variables were temperature and the amount of cover crop‐C added to the soil.
more »
« less
- Award ID(s):
- 2202706
- PAR ID:
- 10419054
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Agronomy Journal
- Volume:
- 116
- Issue:
- 3
- ISSN:
- 0002-1962
- Format(s):
- Medium: X Size: p. 795-809
- Size(s):
- p. 795-809
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Rice paddies are one of the major sources of anthropogenic methane (CH4) emissions. The alternate wetting and drying (AWD) irrigation management has been shown to reduce CH4emissions and total global warming potential (GWP) (CH4and nitrous oxide [N2O]). However, there is limited information about utilizing AWD management to reduce greenhouse gas (GHG) emissions from commercial‐scale continuous rice fields. This study was conducted for five consecutive growing seasons (2015–2019) on a pair of adjacent fields in a commercial farm in Arkansas under long‐term continuous rice rotation irrigated with either continuously flooded (CF) or AWD conditions. The cumulative CH4emissions in the growing season across the two fields and 5 years ranged from 41 to 123 kg CH4‐C ha−1for CF and 1 to 73 kg CH4‐C ha−1for AWD. On average, AWD reduced CH4emissions by 73% relative to CH4emissions in CF fields. Compared to N2O emissions, CH4emissions dominated the GWP with an average contribution of 91% in both irrigation treatments. There was no significant variation in grain yield (7.3–11.9 Mg ha−1) or growing season N2O emissions (−0.02 to 0.51 kg N2O‐N ha−1) between the irrigation treatments. The yield‐scaled GWP was 368 and 173 kg CO2eq. Mg−1season−1for CF and AWD, respectively, showing the feasibility of AWD on a commercial farm to reduce the total GHG emissions while sustaining grain yield. Seasonal variations of GHG emissions observed within fields showed total GHG emissions were predominantly influenced by weather (precipitation) and crop and irrigation management. The influence of air temperature and floodwater heights on GHG emissions had high degree of variability among years and fields. These findings demonstrate that the use of multiyear GHG emission datasets could better capture variability of GHG emissions associated with rice production and could improve field verification of GHG emission models and scaling factors for commercial rice farms.more » « less
-
Abstract We use TROPOMI (TROPOspheric Monitoring Instrument) tropospheric nitrogen dioxide (NO2) measurements to identify cropland soil nitrogen oxide (NOx = NO + NO2) emissions at daily to seasonal scales in the U.S. Southern Mississippi River Valley. Evaluating 1.5 years of TROPOMI observations with a box model, we observe seasonality in local NOxenhancements and estimate maximum cropland soil NOxemissions (15–34 ng N m−2 s−1) early in growing season (May–June). We observe soil NOxpulsing in response to daily decreases in volumetric soil moisture (VSM) as measured by the Soil Moisture Active Passive (SMAP) satellite. Daily NO2enhancements reach up to 0.8 × 1015 molecules cm−24–8 days after precipitation when VSM decreases to ~30%, reflecting emissions behavior distinct from previously defined soil NOxpulse events. This demonstrates that TROPOMI NO2observations, combined with observations of underlying process controls (e.g., soil moisture), can constrain soil NOxprocesses from space.more » « less
-
Abstract The atmospheric concentration of nitrous oxide (N2O) has increased by 23% since the pre‐industrial era, which substantially destructed the stratospheric ozone layer and changed the global climate. However, it remains uncertain about the reasons behind the increase and the spatiotemporal patterns of soil N2O emissions, a primary biogenic source. Here, we used an integrative land ecosystem model, Dynamic Land Ecosystem Model (DLEM), to quantify direct (i.e., emitted from local soil) and indirect (i.e., emissions related to local practices but occurring elsewhere) N2O emissions in the contiguous United States during 1900–2019. Newly developed geospatial data of land‐use history and crop‐specific agricultural management practices were used to force DLEM at a spatial resolution of 5 arc‐min by 5 arc‐min. The model simulation indicates that the U.S. soil N2O emissions totaled 0.97 ± 0.06 Tg N year−1during the 2010s, with 94% and 6% from direct and indirect emissions, respectively. Hot spots of soil N2O emission are found in the US Corn Belt and Rice Belt. We find a threefold increase in total soil N2O emission in the United States since 1900, 74% of which is from agricultural soil emissions, increasing by 12 times from 0.04 Tg N year−1in the 1900s to 0.51 Tg N year−1in the 2010s. More than 90% of soil N2O emission increase in agricultural soils is attributed to human land‐use change and agricultural management practices, while increases in N deposition and climate warming are the dominant drivers for N2O emission increase from natural soils. Across the cropped acres, corn production stands out with a large amount of fertilizer consumption and high‐emission factors, responsible for nearly two‐thirds of direct agricultural soil N2O emission increase since 1900. Our study suggests a large N2O mitigation potential in cropland and the importance of exploring crop‐specific mitigation strategies and prioritizing management alternatives for targeted crop types.more » « less
-
Abstract Groundwater irrigation of cropland is expanding worldwide with poorly known implications for climate change. This study compares experimental measurements of the net global warming impact of a rainfed versus a groundwater‐irrigated corn (maize)–soybean–wheat, no‐till cropping system in the Midwest US, the region that produces the majority of U.S. corn and soybean. Irrigation significantly increased soil organic carbon (C) storage in the upper 25 cm, but not by enough to make up for the CO2‐equivalent (CO2e) costs of fossil fuel power, soil emissions of nitrous oxide (N2O), and degassing of supersaturated CO2and N2O from the groundwater. A rainfed reference system had a net mitigating effect of −13.9 (±31) g CO2e m−2 year−1, but with irrigation at an average rate for the region, the irrigated system contributed to global warming with net greenhouse gas (GHG) emissions of 27.1 (±32) g CO2e m−2 year−1. Compared to the rainfed system, the irrigated system had 45% more GHG emissions and 7% more C sequestration. The irrigation‐associated increase in soil N2O and fossil fuel emissions contributed 18% and 9%, respectively, to the system's total emissions in an average irrigation year. Groundwater degassing of CO2and N2O are missing components of previous assessments of the GHG cost of groundwater irrigation; together they were 4% of the irrigated system's total emissions. The irrigated system's net impact normalized by crop yield (GHG intensity) was +0.04 (±0.006) kg CO2e kg−1yield, close to that of the rainfed system, which was −0.03 (±0.002) kg CO2e kg−1yield. Thus, the increased crop yield resulting from irrigation can ameliorate overall GHG emissions if intensification by irrigation prevents land conversion emissions elsewhere, although the expansion of irrigation risks depletion of local water resources.more » « less
