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Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery

Ballester, Carlos, Hornbuckle, John, Brinkhoff, James, Smith, John and Quayle, Wendy 2017, Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery, Remote sensing, vol. 9, no. 11, pp. 1-18, doi: 10.3390/rs9111149.

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Title Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery
Author(s) Ballester, CarlosORCID iD for Ballester, Carlos orcid.org/0000-0002-6885-0883
Hornbuckle, JohnORCID iD for Hornbuckle, John orcid.org/0000-0003-0714-6646
Brinkhoff, JamesORCID iD for Brinkhoff, James orcid.org/0000-0002-0721-2458
Smith, John
Quayle, WendyORCID iD for Quayle, Wendy orcid.org/0000-0003-0622-1915
Journal name Remote sensing
Volume number 9
Issue number 11
Article ID 1149
Start page 1
End page 18
Total pages 18
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2017-11-08
ISSN 2072-4292
Keyword(s) multi-spectral imagery
nitrogen uptake
plant nitrogen content
precision agriculture
Summary The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha−1. Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure- and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r2 values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha−1) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha−1 at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop.
Language eng
DOI 10.3390/rs9111149
Field of Research 070103 Agricultural Production Systems Simulation
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30104766

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.