Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems
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Multi‑temporal yield pattern analysis method for deriving yield zones in crop production systems Gerald Blasch1,2 · Zhenhai Li3,4 · James A. Taylor1,5
© The Author(s) 2020
Abstract 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, Multitemporal 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 productivitystability 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. Keywords Image analysis · Spatio-temporal variability · k-means clustering · Principal component analysis
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1111 9-020-09719-1) contains supplementary material, which is available to authorized users. * Gerald Blasch [email protected] Extended author information available on the last page of the article
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Precision Agriculture
Introduction Yield data were one of the earliest forms of precision agriculture (PA) data available to growers and researchers, with Global Navigation Satellite System (GNSS) enabled grain yield monitors becoming commercially available in the early 1990s. The spatial variation displayed in these early grain maps was certainly a principal driver for the initial development of site-specific crop management. Grain yield mapping remains widely accessible to growers in mechanized production systems and is the only definitive method for spatially auditing the actual at-harvest yield. Despite this, the adoption of yield mapping has been relatively slow (Bramley 2009; Fountas et al. 2005; Kutter et al.
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