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Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Lookup NU author(s): Dr Gerald Blasch, Dr Zhenhai Li, Dr James Taylor



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2020, The Author(s). Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

Publication metadata

Author(s): Blasch G, Li Z, Taylor JA

Publication type: Article

Publication status: Published

Journal: Precision Agriculture

Year: 2020

Volume: 21

Issue: 6

Pages: 1263-1290

Print publication date: 01/12/2020

Online publication date: 06/05/2020

Acceptance date: 02/04/2020

Date deposited: 09/11/2020

ISSN (print): 1385-2256

ISSN (electronic): 1573-1618

Publisher: Springer


DOI: 10.1007/s11119-020-09719-1


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Funder referenceFunder name
ST/N006801/1STFC (formerly PPARC)
ST/N006801/1STFC (formerly PPARC)
ST/N006801/1STFC (formerly PPARC)