Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-08T14:29:57.900Z Has data issue: false hasContentIssue false
  • English
  • Français

Relating active optical sensor measurements to barley yield

Published online by Cambridge University Press:  01 June 2017

R. Hackett
R. Hackett*
Affiliation:
Teagasc, Oak Park Research Centre, Carlow, Ireland
*
†E-mail: [email protected]
Get access

Abstract

The objective of this work was to determine the most appropriate growth stage to make reflectance measurements that would indicate yield in high yielding winter barley crops. The results indicated that where different rates of fertiliser N were applied, at the same crop growth stages, the best relationship between vegetation indices, calculated on the basis of reflectance measurements, and grain yield were found to occur from booting to early grain fill. Where the timing of fertiliser N inputs was different, for a given level of fertiliser N addition, poor correlations between vegetation indices and grain yield during the stem elongation phase were observed.

Keywords

barleyyieldvegetation indexNDVI
Type
Crop Sensors and Sensing
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 162 - 166
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Babar, MA, Reynolds, MP, van Ginkel, M, Klatt, AR, Raun, WR and Stone, ML 2006. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science 46, 578–588.CrossRefGoogle Scholar
Coulter, BS and Lalor, S 2008. Major and Micro Nutrient Advice for Productive Agricultural Crops. Teagasc, Johnstown Castle, Wexford, Ireland.Google Scholar
Fetch, TG Jr, Steffenson, BJ and Pederson, VD 2004. Predicting agronomic performance of barley using canopy reflectance data. Canadian Journal of Plant Science 84 (1), 1–9.CrossRefGoogle Scholar
Kennedy, SP, Bingham, IJ and Spink, JH 2017. Determinants of spring barley yield in a high-yield potential environment. Journal of Agricultural Science 155 (1), 60–80.CrossRefGoogle Scholar
Li-Hong, XUE, Wei-Xing, C and Lin-Zhang, Y 2007. Predicting grain yield and protein content in winter wheat at different N supply levels using canopy reflectance spectra. Pedosphere 17 (5), 646–653.Google Scholar
Magney, TS, Eitel, JU, Huggins, DR and Vierling, LA 2016. Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agricultural and Forest Meteorology 217, 46–60.CrossRefGoogle Scholar
Marti, J, Bort, J, Slafer, GA and Araus, JL 2007. Can wheat yield be assessed by early measurements of normalized difference vegetation index? Annals of Applied Biology 150 (2), 253–257.CrossRefGoogle Scholar
Sharma, LK, Bu, H, Denton, A and Franzen, DW 2015. Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, USA. Sensors 15 (11), 27832–27853.CrossRefGoogle Scholar
Tucker, CJ, Holben, BN, Elgin, JH Jr and McMurtrey, JE 1980. Relationship of spectral data to grain yield variations. Photogrammetric Engineering and Remote Sensing 46, 657–666.Google Scholar