dc.creatorBlasch, G.
dc.creatorZhenhai Li
dc.creatorTaylor, J.A.
dc.date2020-10-10T00:15:15Z
dc.date2020-10-10T00:15:15Z
dc.date2020
dc.date.accessioned2023-07-17T20:06:15Z
dc.date.available2023-07-17T20:06:15Z
dc.identifierhttps://hdl.handle.net/10883/20975
dc.identifier10.1007/s11119-020-09719-1
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512768
dc.descriptionEasy-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.
dc.description1263-1290
dc.formatPDF
dc.languageEnglish
dc.publisherSpringer
dc.relationhttps://link.springer.com/article/10.1007/s11119-020-09719-1#Sec26
dc.rightsCIMMYT 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
dc.rightsOpen Access
dc.source6
dc.source21
dc.source1385-2256
dc.sourcePrecision Agriculture
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectIMAGE ANALYSIS
dc.subjectDATA ANALYSIS
dc.subjectCROP YIELD
dc.subjectCROP PRODUCTION
dc.titleMulti-temporal yield pattern analysis method for deriving yield zones in crop production systems
dc.typeArticle
dc.typePublished Version
dc.coverageNetherlands


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