Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

dc.contributor.author Bean, G. M.
dc.contributor.author Kitchen, N. R.
dc.contributor.author Sawyer, John
dc.contributor.author Camberato, J. J.
dc.contributor.author Ferguson, R. B.
dc.contributor.author Fernandez, F. G.
dc.contributor.author Franzen, D. W.
dc.contributor.author Laboski, C. A. M.
dc.contributor.author Nafziger, E. D.
dc.contributor.author Sawyer, J. E.
dc.contributor.author Scharf, P. C.
dc.contributor.author Schepers, J.
dc.contributor.author Shanahan, J. S.
dc.contributor.department Agronomy
dc.date 2018-09-19T15:20:27.000
dc.date.accessioned 2020-06-29T23:05:54Z
dc.date.available 2020-06-29T23:05:54Z
dc.date.issued 2018-01-01
dc.description.abstract <p>Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (<em>Zea mays</em>L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALG<sub>MU</sub>) to improve in-season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALG<sub>MU</sub> were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALG<sub>MU</sub> and EONR (MU<sub>DIFF</sub>) was 81 and 74 kg N ha<sup>–1</sup> for treatments receiving 0 and 45 kg N ha<sup>–1</sup> applied at planting, respectively. When ALG<sub>MU</sub> was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha<sup>–1</sup>. Without adjustment, 20 and 29% of sites were within 34 kg N ha<sup>–1</sup> of EONR with 0 and 45 kg N ha<sup>–1</sup> at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha<sup>–1</sup> of EONR. These results show that weather and soil information could be used to improve ALG<sub>MU</sub> N recommendation performance.</p>
dc.description.comments <p>This article is published as Bean, G. M., N. R. Kitchen, J. J. Camberato, R. B. Ferguson, F. G. Fernandez, D. W. Franzen, C. A. M. Laboski et al. "Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information." <em>Agronomy Journal </em>110 (2018): 1-11. doi: <a href="http://dx.doi.org/10.2134/agronj2017.12.0733" target="_blank">10.2134/agronj2017.12.0733</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/agron_pubs/525/
dc.identifier.articleid 1574
dc.identifier.contextkey 12892311
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath agron_pubs/525
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/4889
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/agron_pubs/525/2018_Sawyer_ImprovingActive.pdf|||Sat Jan 15 00:48:39 UTC 2022
dc.source.uri 10.2134/agronj2017.12.0733
dc.subject.disciplines Agriculture
dc.subject.disciplines Agronomy and Crop Sciences
dc.subject.disciplines Soil Science
dc.title Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 17ce8a78-56b3-47be-abcb-b22968be40f2
relation.isOrgUnitOfPublication fdd5c06c-bdbe-469c-a38e-51e664fece7a
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