Quantification and Machine Learning Based N2O-N and CO2-C Emissions Predictions from a Decomposing Rye Cover Crop

Cover crops improve soil health and reduce the risk of soil erosion. However, their impact on the carbon dioxide equivalence (CO2e) is unknown. Therefore, objective of this two-year 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 (MLR), partial least square regression (PLSR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN). Models’ performance was accessed using R2 , RMSE and MAE. Rye (secale cereale) was dormant seeded in mid-October and in the following spring it was terminated at corn’s (Zea mays) 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=0.05) and over the entire season, N2O-N emissions from cover crop and no cover crop treatments were similar (p=0.71). Based on N2O-N and CO2-C emissions over the entire season and the estimated fixed cover crop carbon remaining in the soil, the partial CO2e were -1,061 and 496 kg CO2e ha-1 in 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