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Precision Agriculture

, Volume 20, Issue 6, pp 1231–1250 | Cite as

Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada

  • Taifeng Dong
  • Jiali ShangEmail author
  • Jiangui LiuEmail author
  • Budong Qian
  • Qi Jing
  • Baoluo Ma
  • Ted Huffman
  • Xiaoyuan Geng
  • Abdoul Sow
  • Yichao Shi
  • Francis Canisius
  • Xianfeng Jiao
  • John M. Kovacs
  • Dan Walters
  • Jeff Cable
  • Jeff Wilson
Article

Abstract

Remote sensing has been recognized as a cost-effective way to detect the spatial and temporal variability of crop growth and productivity. In this study, multispectral RapidEye images were used to delineate homogeneous zones of soil and crop development in two fields in Ontario, Canada, one planted with canola (Brassica napus L.) and the other with spring wheat (Triticum aestivum L.). The two fields received different levels of nitrogen (N) treatments during the pre-planting land preparation phase. Soil textures, mineral nitrogen content and crop yield were used to interpret the results of zone delineation. The analysis of variance (ANOVA) tests revealed that the high-resolution RapidEye data, particularly the imagery acquired at peak crop growth stages (i.e. when leaf area index (LAI) is high), provided valuable information for delineating within-field variability of crop growth and yield. Further analysis showed that for both crops, the spatial patterns of crop growth condition varied throughout the growth cycle, revealing different impacts of soil properties and N fertilization on the crops. In particular, during peak growth stage, the within-field variability was most strongly affected by the pre-planting N application and had the strongest correlation with crop yield. These results suggest that high-resolution satellite data (e.g., RapidEye) could assist in making decisions on optimal N fertilization for enhanced crop productivity.

Keywords

RapidEye Homogeneous zone delineation Within-field variability Crop growth Crop yield Nitrogen 

Notes

Acknowledgements

This study was funded by the AgriFlex project of Agriculture and Agri-Food Canada (Project #2628), as well as the Canadian Space Agency through research projects on land productivity using Earth observation and crop modeling (Project #GRIP2013GENSX and #17SUSOARTO), and a Grant (Project #920161) provided to John M. Kovacs and Dan Walters from the Northern Ontario Heritage Fund Corporation of Canada. The authors would like to acknowledge the contribution of many students from Nipissing University who helped in field data collection. Finally, the authors would also like to acknowledge the assistance of Ferme Roberge of the West Nipissing Agricultural District.

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Copyright information

© Crown 2019

Authors and Affiliations

  • Taifeng Dong
    • 1
  • Jiali Shang
    • 1
    Email author
  • Jiangui Liu
    • 1
    Email author
  • Budong Qian
    • 1
  • Qi Jing
    • 1
  • Baoluo Ma
    • 1
  • Ted Huffman
    • 1
  • Xiaoyuan Geng
    • 1
  • Abdoul Sow
    • 1
  • Yichao Shi
    • 1
  • Francis Canisius
    • 2
  • Xianfeng Jiao
    • 1
  • John M. Kovacs
    • 3
  • Dan Walters
    • 3
  • Jeff Cable
    • 3
  • Jeff Wilson
    • 3
  1. 1.Ottawa Research and Development Centre, Agriculture and Agri-Food CanadaOttawaCanada
  2. 2.Canada Centre for Mapping and Earth Observation, Natural Resources CanadaOttawaCanada
  3. 3.Department of GeographyNipissing UniversityNorth BayCanada

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