Intensive agriculture, nitrogen legacies, and water quality: intersections and implications

Abstract

More than a century of land-use changes and intensive agriculture across the Mississippi River Basin (MRB) has led to a degradation of soil and water resources. Nitrogen (N) leaching from the excess application of fertilizers has been implicated in algal blooms and the development of large, coastal “dead zones.” It is, however, increasingly recognized that water quality today is a function not only of the current-year inputs but also of legacy N within the watershed—legacy that has accumulated in soil and groundwater over decades of high-input agricultural practices. Although attempts have been made to quantify the extent to which soil organic nitrogen (SON) is being sequestered in agricultural soils with intensive fertilization, improved residue management, and the adoption of conservation tillage practices, the controls on accumulation dynamics as well as linkages between legacy N accumulation and water quality remain unclear. Here, we have used the process-based model CENTURY to quantify accumulation and depletion trajectories for soil N across a range of climate and soil types characteristic of the MRB. The model was calibrated against crop yield data and soil nitrogen accumulation data from a long-term field site. Model runs highlighted that under current management scenarios, N accumulation is greatest in regions with the highest crop yield, and this can be attributed to the higher residue rates with greater yields. We thus find that humans, through management practices, have homogenized spatial patterns of SON across the landscape by increasing SON magnitudes in warmer and drier regions. Results also suggest a regime shift in the relationship between soil organic N and N mineralization fluxes, such that N fluxes are greater now than in the 1930s, despite similar soil organic N magnitudes, mainly due to higher proportions of labile, unprotected soil organic matter. This regime shift leads to elevated N leaching to tiles and groundwater in landscapes under intensive agriculture.

A Random Forest in the Great Lakes: Stream Nutrient Concentrations Across the Transboundary Great Lakes Basin

“…The importance of the predictor variables was found to be similar at the seasonal scale, highlighting the pervasive influence of these landscape predictors (Figure S2 in Supporting Information S1). Our finding of a positive relationship between developed land and dissolved N and P concentrations is consistent with others in the GLB and across the world highlighting the role of anthropogenic land use on stream nutrient concentrations (Basu et al., 2010; Ilampooranan et al., 2022; Mooney et al., 2020; Stets et al., 2020). The positive relationship between tile drains and dissolved nutrient concentrations highlights the role of subsurface drainage networks in bypassing the nutrient‐filtering abilities of the soil and contributing to increasing concentrations of dissolved nutrients (Basu et al., 2011; Blann et al., 2009; Guan et al., 2011; K. W. King et al., 2015; M. Macrae et al., 2021; Schilling et al., 2012; Van Meter et al., 2020; Zanardo et al., 2012).…”

Pulling the rabbit out of the hat: Unravelling hidden nitrogen legacies in catchment‐scale water quality models

“…Accordingly, detailed analyses of the impact of model structure and/or parameter values on the simulated build-up of soil ON have rarely been performed (but see, e.g. Ilampooranan et al, 2022;Sarrazin et al, 2022). However, as N fluxes from the soil pools propagate through the model compartments, we need to quantify and evaluate soil N legacy in water quality models to more accurately simulate N fluxes and concentrations in receiving water bodies.…”

“…The explicit representation of one or several soil pools and, in particular, of immobile soil ON can enable simulation of N storage in soils over long timescales and thus of biogeochemical legacy effects of excessive N inputs (Figure 1). While this representation is comparably well established in current nutrient models, only very recently have some studies analysed the temporal dynamics of soil N storage in more detail (e.g., Ilampooranan et al, 2022;Lee et al, 2016;Sarrazin et al, 2022;, 2018. Accordingly, detailed analyses of the impact of model structure and/or parameter values on the simulated build-up of soil ON have rarely been performed (but see, e.g.…”

The value of instream stable water isotope and nitrate concentration data for calibrating a travel time‐based water quality model

“…the Hydrological Predictions for the Environment (HYPE; Lindström et al, 2010) and the Integrated Catchment model (INCA; Wade et al, 2002) are widely used for simulating nitrate concentration (Wellen et al, 2015). However, the hydrological transport of these models is based on celerity rather than pore water flow velocity, which may lead to challenges in representing longer time delays in nitrate (Hrachowitz et al, 2016;Ilampooranan et al, 2022;Lutz et al, 2022). Nonetheless, incorporating TTDs into water quality models to describe hydrological transport based on pore water flow water velocity remains largely unexplored.…”