Quantifying yield and water quality benefits of cover cropping at a landscape scale using satellite imagery

Kushal KC
Graduate (MS)
Sami Khanal
Food, Agricultural and Biological Engineering

Cover cropping is one of the conservation practices that could help alleviate soil health problems and reduce nutrient losses to water resources. Although there exist substantial scientific research and evidence about cover crops and their impacts on soil and water quality, they are mainly based on controlled field experimental trials. Currently, there is a limited understanding of spatial and temporal trends in cover cropping practices and their impacts at a landscape scale. Thus, the objective of this study is to assess the impacts of cover cropping on crop productivity and water quality in the Western Lake Erie Basin. A long-term cover cropping inventory from 2008 to 2019 was developed using machine learning in satellite images as the first phase of the study. To quantify the impact of cover crops on agricultural productivity, spatially explicit corn and soybean yield maps at a field scale were generated by leveraging satellite and weather-based covariates with county-scale yield data. For water quality assessment, nutrient loading data from one of the water quality monitoring sites stationed at Maumee River (Heidelberg University, National Center for Water Quality Research) was used and compared against cover crop areas annually. Although cover crops did not seem to boost agricultural productivity, they seemed to reduce nutrient (nitrate and soluble reactive phosphorus (SRP)) loadings. We observed a negative correlation between cover crop area and nitrate and SRP concentration and load. This study shows that cover cropping practice can be monitored at a landscape scale utilizing publicly available images from earth-observing satellites. Understanding of cover cropping practices and their impacts on ecosystem services at a landscape scale is significant to developing critical public policies for improving the sustainability of the agricultural production systems.