Nitrous oxide (N 2 O) is a potent greenhouse gas with implication for climate change. Agriculture accounts for 10% of all greenhouse gas emissions in the United States, but 75% of the country's N 2 O emissions. In the absence of PRE herbicides, weeds compete with soybean for available soil moisture and inorganic N, and may reduce N 2 O emissions relative to a weed-free environment. However, after weeds are killed with a POST herbicide, the dead weed residues may stimulate N 2 O emissions by increasing soil moisture and supplying carbon and nitrogen to microbial denitrifiers. Wider soybean rows often have more weed biomass, and as a result, row width may further impact how weeds influence N 2 O emissions. To determine this relationship, field studies were conducted in 2013 and 2014 in Arlington, WI. A two-by-two factorial treatment structure of weed management (PRE + POST vs. POST-only) and row width (38 or 76 cm) was arranged in a randomized complete block design with four replications. N 2 O fluxes were measured from static gas sampling chambers at least weekly starting 2 wk after planting until mid-September, and were compared for the periods before and after weed termination using a repeated measures analysis. N 2 O fluxes were not influenced by the weed by width interaction or width before termination, after termination, or for the full duration of the study at P ≤ 0.05. Interestingly, we observed that POST-only treatments had lower fluxes on the sampling day immediately prior to POST application (P = 0.0002), but this was the only incidence where weed influenced N 2 O fluxes, and overall, average fluxes from PRE + POST and POST-only treatments were not different for any period of the study. Soybean yield was not influenced by width (P = 0.6018) or weed by width (P = 0.5825), but yield was 650 kg ha −1 higher in the PRE + POST than POST-only treatments (P = 0.0007). These results indicate that herbicide management strategy does not influence N 2 O emissions from soybean, and the use of a PRE herbicide prevents soybean yield loss.
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Bailey et al. (2015) studied this question.
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