DOI: 10.5061/dryad.9cnp5hqv1
title: Machine learning reveals dynamic controls of soil nitrous oxide (N2O) emissions from diverse long-term cropping systems

description: 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 (&gt;15 kg N ha-1) and daily temperature
 (&gt;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.
description: This study aimed to identify critical management, environmental,
 and biogeochemical drivers of nitrous oxide (N<sub>2</sub>O)
 emissions and their differential relationships and threshold conditions
 for emissions under diverse long-term cropping rotations. Historical 17-y
 (2003 to 2019) data on yearly N<sub>2</sub>O emissions, soil
 properties, agricultural management practices, and weather were obtained
 from the Main Cropping System Experiment (MCSE) of the Kellogg Biological
 Station (KBS) Long-Term Ecological Research (LTER) data catalog (https://lter.kbs.msu.edu/datatables). Data were extracted from four treatments, including: (i) a Conventional system with chisel tillage and standard chemical inputs, (ii) a No-till system with standard chemical inputs, (iii) a Reduced input system with chisel tillage, low fertilizer inputs, and cover crops, and (iv) a Biologically-based/organic system managed organically using chisel tillage, cover crops, and no synthetic chemical inputs. Nitrous oxide flux measurements were made using static chambers at weekly to monthly intervals. Details on N<sub>2</sub>O flux measurements can be found at https://lter.kbs.msu.edu/protocols/113. During each gas sampling event, gravimetric water content (GWC) was measured from the 0-25 cm depth. Water-filled pore space (WFPS) was determined using the GWC and a consistent bulk density of 1.44 g cm<sup>-3</sup> across all treatments. Soil samples (0 to 25 cm depth) were collected biweekly each year for NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>-</sup> concentrations. Soils were extracted with 2M KCl and analyzed for NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>-</sup>concentrations in a continuous flow analyzer (Alpkem 3550, O.I. Analytical, College Station, TX). Soil NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, and WFPS values were linearly interpolated between two sampling dates to match the N<sub>2</sub>O sampling dates when soil samples were not collected on the gas sampling days due to management and weather reasons.
description: # Machine Learning Reveals Dynamic Controls of Soil Nitrous Oxide (N2O)
 Emissions from Diverse Long-term Cropping Systems
 [https://doi.org/10.5061/dryad.9cnp5hqv1](https://doi.org/10.5061/dryad.9cnp5hqv1) ## Description of the data and file structure This study aimed to identify critical management, environmental, and biogeochemical drivers of nitrous oxide (N2O) emissions and their differential relationships and threshold conditions for emissions under diverse long-term cropping rotations. ### Files and variables #### File: Dhaliwal\_et\_al\_2024\_JEQ\_Data.xlsx **Description:**  ##### Variables * **Description of data columns** There were no missing values in the data. **Date:** Gas sampling dates **trt:** Treatments: T1- Conventional; T2- No-till; T3- Reduced input; and T4- Biologically-based/Organic **rep:** Replications: R1, R2, R3, and R4 **N2O:** Daily average N2O flux (g N2O-N ha-1d-1) **lgN2O:** Daily average N2O flux (g logN2O-N ha-1d-1) **NO3:** NO3-N content in the top 25-cm soil layer (kg N ha-1) **NH4:** NH4-N content in the top 25-cm soil layer (kg N ha-1) **WFPS:** Water-filled pore space **Cumulative.2.day precipitation:** Cumulative precipitation in the last two days before gas sampling (mm) **Tavg:** Daily average air temperature (°C) **Crop:** Crop phase: corn, soybean, and wheat ## Code/software #### **File: Dhaliwal\_et\_al\_2024\_JEQ\_R\_code.txt** ## Access information Data was derived from the following sources: * [https://lter.kbs.msu.edu/datatables](https://lter.kbs.msu.edu/datatables)