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Reduction in nitrification during the early transition from conventional to organic farming practices

350 210 Stroud Water Research Center

Price, J.R., D. Oviedo-Vargas, M. Peipoch, M.D. Daniels, and J. Kan. 2025. Ecosphere 16(8): e70375.

Permalink/ DOI (Open access)

Abstract

Little is known about the nitrogen transformation dynamics during the early transition phase from conventional to organic farming. We investigated changes in microbial N-cycling in agricultural fields transitioning from conventional to organic farming practices by quantifying nitrification/mineralization rates, extracellular enzyme activity (EEA), and nitrogen transformation genes (nitrification and denitrification). The farming practices we investigated contained three binary treatments: Management System (denoting both general approach and fertility source), Tillage, and Cover Crop.

Four years after the transition, we found that the process of converting conventionally managed fields to organic agricultural practices significantly reduced net nitrification rates, likely as a result of lower abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB). In addition to terms pertaining to the experimental treatments, we included a term, Year, in our models to control for noise due to the cash/cover crop rotation and weather-related differences. We found that the Year covariate to have highly significant variation related to net nitrification, soil NH4+-N concentration, the EEA ratio of NAG:BG, and the abundances of AOA, AOB, and the denitrifying gene nosZ.

In contrast to much of the published literature, our results showed the absence of a significant response to the Tillage and Cover Crop treatments after four years of conversion. Combined with year-to-year variation being generally more important of an influence than the Tillage and Cover Crop treatments, our results suggest that nutrient processes change gradually in response to farming practices. Therefore, incorporating research about the inter-year variations may yield predictive models that would be useful not just to researchers but also to guide farmers engaged in conventional-to-organic conversion projects.