Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field

Benjamin U Meinen; Derek T Robinson

doi:10.1016/j.scitotenv.2020.138320

Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field

Sci Total Environ. 2020 Aug 10:729:138320. doi: 10.1016/j.scitotenv.2020.138320. Epub 2020 Apr 20.

Authors

Benjamin U Meinen¹, Derek T Robinson²

Affiliations

¹ Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada. Electronic address: benjmeinen@hotmail.com.
² Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada.

PMID: 32387767
DOI: 10.1016/j.scitotenv.2020.138320

Abstract

Distributed measurements of agricultural erosion at the farm-scale are needed to evaluate both the on and off-site impacts of sediment redistribution. While best management practices have been shown to reduce surface erosion rates and improve water quality, their farm-scale effects can be difficult to quantify. In this study we use imagery from an unmanned aerial vehicle (UAV) and structure-from-motion multi-view stereo (SfM-MVS) to quantify erosion rates and their effects on crop yield across a 15.9-ha agricultural field. Our results highlight that the installation of catch basins were able to stop 159.52 t of sediment and associated nutrients from entering the waterway adjacent to the study site over the course of one year, corresponding to an erosion rate of 18.83 t ha^-1 yr^-1 across six study basins. Poor soil structure resulting from downslope tillage reduced crop yields on topographic shoulders of the study site, while accelerated water erosion processes were responsible for large areas of washout that caused highly variable crop growth in footslopes. The highest crop yields were associated with backslopes and topographically flat regions of the field which experienced minimal erosion. Change-detection results showed that UAV imagery was able to reliably quantify depositional plumes and was comparable to that of a terrestrial laser scanner (TLS) using a ± 0.04 m confidence interval.

Keywords: Best management practices; Change-detection; Crop yield; Erosion; Structure-from-motion (SfM); Unmanned aerial vehicle (UAV).