Show simple item record

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

Creator: Blasch, G.
Creator: Zhenhai Li
Creator: Taylor, J.A.
Year: 2020
URI: https://hdl.handle.net/10883/20975
Format: PDF
Language: English
Publisher: Springer
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Netherlands
Volume: In press
DOI: 10.1007/s11119-020-09719-1
Description: 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.
Agrovoc: IMAGE ANALYSIS
Agrovoc: DATA ANALYSIS
Agrovoc: CROP YIELD
Agrovoc: CROP PRODUCTION
ISSN: 1385-2256
Journal: Precision Agriculture


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Socioeconomics
    Including topics such as farming systems, markets, impact & targeting, innovations, and GIS

Show simple item record