Sediment and particulate phosphorus characteristics in grassed waterways from row crop corn and alfalfa fields collected by manual University of Exeter samplers and automatic sampling
✍ Scribed by John C. Panuska; Laura Ward Good; Peter A. Vadas; Dennis L. Busch; Asli Ozkaynak
- Book ID
- 102265799
- Publisher
- John Wiley and Sons
- Year
- 2011
- Tongue
- English
- Weight
- 219 KB
- Volume
- 25
- Category
- Article
- ISSN
- 0885-6087
- DOI
- 10.1002/hyp.7987
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✦ Synopsis
Abstract
Phosphorus (P) export from agricultural lands above known threshold levels can result in adverse impacts to receiving water quality. Phosphorus loss occurs in dissolved and sediment‐bound, or particulate phosphorous (PP), forms, with the latter often dominating losses from row‐cropped systems. To target practices, land managers need good computer models and model developers need good monitoring data. Sediment monitoring data (e.g. radiometric finger printing and sediment P sorption capacity) can help identify sediment source areas and improve models, but require more sediment mass than is typically obtained by automatic sampling. This study compares a simple suspended sediment sampler developed at the University of Exeter (UE) with automatic sampling in intermittent channels draining corn and alfalfa fields. The corn field had a greater runoff coefficient (27%) than alfalfa (11%). No differences were found in enrichment ratios (sediment constituent/soil constituent) in PP (PP~ER~) or percent loss on ignition (LOI~ER~) between paired UE samplers on corn. The median LOI~ER~ for the UE samplers (1·9%) did not differ significantly (p > 0·13) from the automatic sampler (2·0%). The PP~ER~ from the UE samplers was on average 20% lower than the automatic samplers. A correlation (r^2^ = 0·75) was found between sediment PP and % LOI from automatic samplers and UE samplers for particles < 50 µm, while for > 50 µm PP concentration did not change with changes in % LOI. Sediment ammonium‐oxalate extractable metals were similarly related to LOI, with the strongest correlation for iron (r^2^ = 0·71) and magnesium (r^2^ = 0·70). Copyright © 2011 John Wiley & Sons, Ltd.