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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.
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Long‐term research avoids spurious and misleading trends in sustainability attributes of no‐till
Abstract Agricultural management recommendations based on short‐term studies can produce findings inconsistent with long‐term reality. Here, we test the long‐term environmental sustainability and profitability of continuous no‐till agriculture on yield, soil water availability, and N2O fluxes. Using a moving window approach, we investigate the development and stability of several attributes of continuous no‐till as compared to conventional till agriculture over a 29‐year period at a site in the upper Midwest, US. Over a decade is needed to detect the consistent effects of no‐till. Both crop yield and soil water availability required 15 years or longer to generate patterns consistent with 29‐year trends. Only marginal trends for N2O fluxes appeared in this period. Relative profitability analysis suggests that after initial implementation, 86% of periods between 10 and 29 years recuperated the initial expense of no‐till implementation, with the probability of higher relative profit increasing with longevity. Importantly, statistically significant but misleading short‐term trends appeared in more than 20% of the periods examined. Results underscore the importance of decadal and longer studies for revealing consistent dynamics and emergent outcomes of no‐till agriculture, shown to be beneficial in the long term.
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- PAR ID:
- 10453707
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 26
- Issue:
- 6
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 3715-3725
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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 LTER/LTAR site to better understand 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 to 42% of daily N2O flux variability in 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 temperature (>23 °C) as the most influential variables; in the No-till system, climate variables, precipitation, and temperature were important variables. In low input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant variable, followed by precipitation and 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
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