Purdue University Graduate School
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posted on 2020-07-31, 20:46 authored by Kanru ChenKanru Chen

Indiana is the leading state of cover crop adoption within the Upper Mississippi River Basin. However, since 2015 the cover crop adoption has slowed to a plateau. In order to regain the previous momentum, there must be an increased understanding of the spatiotemporal dynamics of cover crop adoption on the county and watershed scale. Currently, the cover crop adoption is monitored biannually through a driving transect survey method that investigates only 8.5% of the watershed and extrapolates to the entire county. However, the observations made by the driving transect survey can merely cover limited fields and is time-consuming. In addition, the driving transect survey did not provide comparative analysis among consecutive years. Therefore, we developed a rapid cover crop survey method by using remote sensing technology. The fundamental objectives of this research are: (1) evaluating the accuracy of the rapid cover crop survey method relative to the driving transect data and determining the best cut-off value (COV) of Normalized Difference Vegetation Index (NDVI); (2) performing a hindcasting analysis of cover crop adoption within the Big Pine Creek Watersheds within the period of 2014-2018 by employing a rapid cover crop survey remote sensing techniques; (3) accessing cover crop adoption management tendencies of farmers within the Big Pine Watersheds, and (4) determining the cover crop adoption tenure of farmers within the Big Pine Creek watersheds between 2014 and 2018. The cover crop management tendency represents the farmers’ preference on cash crop rotation method after harvesting cover crops, and the cover crop adoption tenure means that how often farmers adopt cover crops in a specific field in the research period.

The results of this research demonstrated that relative to the conventional driving transect, remote sensing is a feasible method to successfully detect cover crop adoption on a county and watershed scale. Over a 4-year period (2015-2018), Producer’s Accuracy (PA) under the best COV, which represented how much vegetation-covered field recorded in transect data that can be captured in the processed NDVI map, was 89.02%. This PA value was relatively high compared with previous spatial crop classification research. The rapid remote sensing method also provided individual field locations of cover crop adoption over time within the entire watershed, compared to the driving transect that only gives extrapolated average of adoption. The hindcasting analysis of cover crop adoption revealed a 74% increase in cover crop acreage in the watershed from 2014 to 2018, which equated to a 0.71% increase in land receiving cover crops among all cultivated land annually. The evaluation of farmer cover crop adoption tendencies demonstrated that over a 4-year period, cover crop adoption going into corn was 19.7% greater on average relative to before soybean. Another key finding was that the level of cover crop adoption annually in the watershed was heavily influenced by the cash crop rotation. The cover crop tenure analysis demonstrated that agricultural fields of greater cover crop tenure represented the smallest portion of the cultivated land in the watershed, where 84.2% of the watershed was void of cover crop adoption and field that received cover crops for more than 4 consecutive years represented only 1% of cultivated land.

To conclude, we are confident that the rapid cover crop survey method could replace the traditional driving transect survey. Our findings suggest that rapid assessment methods of cover crop adoption involving processed NDVI map could help advance the effectiveness, speed, and accuracy of cover crop adoption and assessment in the state of Indiana and the entire Mississippi River Basin region.


Dr. Shalamar Armstrong


Degree Type

  • Master of Science


  • Agronomy

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Shalamar Armstrong

Additional Committee Member 2

Jason Ackerson

Additional Committee Member 3

Guofan Shao

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