Login

Proceedings

Find matching any: Reset
Add filter to result:
Evaluating a Satellite Remote Sensing and Calibration Strip-based Precision Nitrogen Management Strategy for Corn in Minnesota and Indiana
1K. Mizuta, 2A. C. Morales, 1L. N. Lacerda, 1Y. Miao, 2D. Cammarano, 1J. A. Coulter, 2R. L. Nielsen, 3R. Gunzenhauser, 3B. McArtor, 4K. Kuehner, 1S. Wakahara, 1D. J. Mulla, 2D. . Quinn
1. University of Minnesota
2. Purdue University
3. Granular Inc.
4. Minnesota Department of Agriculture

Precision nitrogen (N) management (PNM) aims to match N supply with crop N demand in both space and time and has the potential to improve N use efficiency (NUE), increase farmer profitability, and reduce N losses and negative environmental impacts. However, current PNM adoption rate is still quite low. A remote sensing and calibration strip-based PNM strategy (RS-CS-PNM) has been developed by the Precision Agriculture Center at the University of Minnesota. The objective of this research was to systematically evaluate this RS-CS-PNM strategy under diverse on-farm conditions in terms of corn yield, NUE, economic returns and environmental impact as compared with farmer’s N rate (FNR) and a commercially available crop growth model-based N management strategy. Ten commercial fields in Minnesota and Indiana, USA were selected in 2021 for this study. A series of N rate strips across each field were set up before planting, including 35% FNR, 35%FNR, 70%FNR, 100% FNR, and 130% FNR, with 3-5 replications depending on the field size. The 130% FNR strip can be regarded as N rich strip. The strips were further delineated into small grids approximately 45 m long, with width being 21.4 to 24.4 m depending on each farmer’s fertilizer applicator’s width. The adjacent grids that represented the range of preplant N treatments were considered as one block. For the RS-CS-PNM strategy, normalized difference vegetation index (NDVI) calculated from the PlanetScope images was calculated and response curves were created with NDVI and the applied preplant N rates for each block at V7-V9 growth stages. Then the side-dress N rate for each grid was determined by deducting the preplant N rate (35% and 70% FNR) from the block-specific optimal N rate (ONR). Granular’s crop growth model-based PNM strategy (GCGM-PNM) was used in this study, and side-dress N application rates were determined by the company using their crop growth model-based algorithm for one of the two 35% FNR strips in each replication. Yield monitor data were cleaned to remove erroneous data. The crop yield, partial factor productivity, economic returns, and residual nitrate N at harvest for the RS-CS-PNM strategy were compared with FNR and the GCGM-PNM strategy. Preliminary analysis indicated that the RS-CS-PNM performance varied from field to field and within a field. More analyses are being performed and the results will be provided for the full paper and presented at the conference. 

Keyword: Precision nitrogen management, Satellite remote sensing, Corn yield, Nitrogen use efficiency, Economic returns, Soil nitrate nitrogen, On-farm trial