A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Abstract

Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural management commonly attempts to integrate multiple datasets to determine management zones (MZs), homogenous units within the field, based on spatial characteristics of environmental and crop properties (i.e., terrain, soil, vegetation conditions). This study compared several multivariate spatial clustering methods to determine MZs for precision nitrogen fertilization in a citrus orchard. Six variables, namely normalized difference vegetation index, crop water stress index, digital surface model, slope, elevation and aspect, were used to characterize spatial variability within four plots. Six clustering model composites were compared, each including some or all of the following components: (1) spatial representation of the data (e.g., Getis Ord Gi*); (2) variable weights based on their relative contribution; and (3) clustering methods, including different extensions of K-means and hierarchical clustering algorithms. The fuzzy K-means algorithm applied to the weighted spatial representation was found to generate MZs with similar numbers of trees, while the K-means algorithm applied over the spatial representation generated MZs that were more continuous over space, with minimum fragmentation. Spatial variability was not constant across the orchard and among the different variables. Management of the sub-units, or plots, using spatial representation rather than the measured values, is proposed as a more suitable platform for agricultural practices. SSM is dependent upon available variable rate application technologies. Future development of fertilizer application for individual trees will require adjusting the statistical approach to support tree-specific management. The suggested model composite is flexible and may be composed of different models for delineating plot-specific MZs.

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Acknowledgements

This work was funded by the Center for Fertilization and Plant Nutrition, www.cfpn center and the “Eugene Kendel” grant via the Ministry of Agriculture and Rural Development in Israel. The authors would also like to thank Mr. Daniel Klutsky.

Funding

Center for Fertilization and Plant Nutrition (CFPN), www.cfpn.center; “Eugene Kendel” grant via the Ministry of Agriculture and Rural Development in Israel (Grant No. 20-12-0030).

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NOL—conceptualization, software, methods, formal analysis, interpretation, writing, editing; ABG: methods, interpretation, editing; AP: methods; DT: conceptualization, data collection, interpretation, writing; RL: conceptualization, methods, editing; SB data collection, interpretation; ER: data collection, interpretation, editing; TPK—conceptualization, methods, formal analysis, interpretation, writing, editing.

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Correspondence to N. Ohana-Levi.

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Ohana-Levi, N., Ben-Gal, A., Peeters, A. et al. A comparison between spatial clustering models for determining N-fertilization management zones in orchards. Precision Agric (2020). https://doi.org/10.1007/s11119-020-09731-5

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Keywords

  • Multivariate spatial clustering
  • Precision fertilization
  • Getis-ord gi*
  • Nitrogen use efficiency
  • Site-specific management
  • Principal component analysis