A Field‐Scale Approach to Estimate Nitrate Loading to Groundwater

Abstract

The quantification of groundwater NO loading associated with a specific field or set of management practices so that groundwater quality improvements can be objectively assessed is a major challenge. The magnitude and timing of NO export from a single agricultural field under raspberry ( L.) production were investigated by combining high-resolution groundwater NO concentration profiles (sampled using passive diffusion samplers) with Darcy's flux estimation at the field's down-gradient edge (based on field-measured hydraulic gradients and laboratory-estimated hydraulic conductivity). Annual recharge estimated using Darcy's law (1002 mm) was similar to that obtained using two other approaches. The similarity in the rate of Cl applied to the field and the estimated export flux over the 1-yr monitoring period (51 vs. 56 kg Cl ha) suggested the mass flux estimation approach was robust. An estimated 80 kg NO-N ha was exported from the agricultural field over the 1-yr monitoring period. The greatest monthly groundwater mass flux exported was observed in February and March (∼11 kg NO-N ha), and was associated with NO leached from the soil zone during the onset of precipitation in the previous autumn. Provided the groundwater recharged from the field of interest can be isolated within a vertical profile, this approach is an effective method for obtaining spatially integrated estimates of the magnitude and timing of NO loading to groundwater.

Quantifying Nitrate Leaching under Commercial Red Raspberry Using Passive Capillary Wick Samplers

“…Similarly, residual soil NO 3 –N in commercial raspberry fields in the Lower Fraser Valley, BC, which received manure (355 kg N ha −1 ), tended to be greater than for fields that received only mineral fertilizer (165 kg N ha −1 ) (Zebarth et al, 1998). In studies that used groundwater methods to estimate groundwater NO 3 –N loading, 174 kg N ha −1 was estimated under a manured raspberry field (Kuipers et al, 2014), whereas 80 kg N ha −1 was estimated under the current study field, which, at the time, had not recently received manure (Malekani et al, 2018).…”

“…Previous studies using N budgets have identified raspberry fields as important sources of NO 3 –N leaching to the ASA, with isotopic signatures of the NO 3 –N indicating poultry manure as an important N source (Wassenaar, 1995; Wassenaar et al, 2006; Zebarth et al, 1998). Two studies have quantified groundwater NO 3 –N loading from individual raspberry fields in the ASA using groundwater methods (Kuipers et al, 2014; Malekani et al, 2018). However, there are currently no published studies that have quantified inter‐ and intra‐annual leaching of NO 3 –N from the root zone of raspberry over the ASA.…”

Quantification of Groundwater Nitrate Loading after Raspberry Field Renovation Using High‐Resolution Passive Diffusion Sampling

“…Recent groundwater monitoring approaches have used vertical concentration profiles in shallow groundwater to quantify NO 3 loading from individual agricultural fields (Stites and Kraft, 2001; Kuipers et al, 2014; Malekani et al, 2018). Malekani et al (2018) successfully quantified annual NO 3 loading from an individual raspberry field over the ASA by combining high‐resolution NO 3 concentrations with volumetric groundwater flux estimated using Darcy's law. Passive diffusion samplers were used to measure NO 3 concentrations with depth at the down‐gradient edge of a raspberry field.…”

“…The strength of this approach is that it integrates spatial and temporal variations to produce a robust estimate of N losses over larger spatial (field‐scale) and temporal (annual) scales. Thus, the approach of Malekani et al (2018) can be applied to examine the interannual effect of raspberry field renovation on NO 3 loading to groundwater in the ASA.…”

“…Specifically, the study aimed to understand how the temporal and spatial variation in groundwater NO 3 concentrations related to the soil and crop management practices given the hydrologic factors including water table fluctuation, hydraulic gradient, flow direction, and hydraulic conductivity. The NO 3 concentration data collected from Years 1 and 6 of this study were from a previously published study (Malekani et al, 2018), and part of an unpublished study (Schincariol, 2019), respectively.…”