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

文献类型： 外文期刊

作者： Blasch, Gerald 1 ; Li, Zhenhai 3 ; Taylor, James A. 1 ;

作者机构： 1.Newcastle Univ, Sch Nat & Environm Sci, Agr Bldg, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

2.Int Maize & Wheat Improvement Ctr CIMMYT, Texcoco 56237, Mexico

3.Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

5.Univ Montpellier, INRAE, UMR ITAP, F-34196 Montpellier, France

关键词： Image analysis; Spatio-temporal variability; k-means clustering; Principal component analysis

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.385； 五年影响因子：5.004 ）

ISSN： 1385-2256

年卷期： 2020 年 21 卷 6 期

页码：

收录情况： SCI

摘要： 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.

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