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Economically Optimal Nitrogen Side-dressing Based on Vegetation Indices from Satellite Images Through On-farm Experiments

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Listed:
  • Du, Qianqian
  • Mieno, Taro
  • Bullock, David
  • Edge, Brittani

Abstract

A methodology is introduced that combines data from on-farm precision experimentation (OFPE) with remotely sensed vegetative index (VI) data to derive site-specific economically optimal side-dressing N rates (EONRs). An OFPE was conducted on a central Illinois field in the 2019 corn growing season; the trial design targeted six side-dressing N rates ranging from 0 and 177 kg ha-1 on field plots, and yields were recorded at harvest using a standard GPS-linked yield monitor. NDRE values were calculated from Sentinel-2 satellite imagery during the V10 to V12 corn growth stages of the experiment’s crop. After partitioning the field by NDRE quartile, economically N side-dressing rates were calculated after estimating each quartile’s yield response function. Consistent with agronomic expectations, results showed that the parts of the field with lower NDRE values had higher yield; but the impact of increasing NDRE levels on the side-dressing rate’s marginal product and EONR was not monotonic. Simulations predicted that compared to the side-dressing strategy the farmer would have implemented if not participating in the OFPE, net revenues could have been increased by $54 ha-1 by using the methodology presented, suggesting high potential value of combining OFPE and VI data. A key advantage of the proposed methodology is that the data’s inference space is the field to be managed. Further study is needed to improve the featured methodology.

Suggested Citation

  • Du, Qianqian & Mieno, Taro & Bullock, David & Edge, Brittani, 2021. "Economically Optimal Nitrogen Side-dressing Based on Vegetation Indices from Satellite Images Through On-farm Experiments," Land, Farm & Agribusiness Management Department 316596, Harper Adams University, Land, Farm & Agribusiness Management Department.
    • Handle: RePEc:ags:haaewp:316596
    DOI: 10.22004/ag.econ.316596
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    References listed on IDEAS

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    1. Simon N. Wood & Zheyuan Li & Gavin Shaddick & Nicole H. Augustin, 2017. "Generalized Additive Models for Gigadata: Modeling the U.K. Black Smoke Network Daily Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1199-1210, July.
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    Keywords

    Crop Production/Industries; Land Economics/Use; Research and Development/Tech Change/Emerging Technologies;
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