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Multi-Temporal Yield Pattern Analysis - Adaption of Pattern Recognition to Agronomic Data
G. Blasch, J. A. Taylor
Newcastle University, School of Natural and Environmental Sciences, Agriculture Building, Newcastle upon Tyne, Tyne and Wear, NE1 7RU, UK

In precision agriculture, the understanding of yield variability, both spatial and temporal, can deliver essential information for the decision making of site-specific crop management. Since commercial yield mapping started in the early 1990s, most research studies have focused on spatial variance or short-term temporal variance analyzed statistically in order to produce trend maps. Nowadays, longer records of high-quality yield data are available offering a new potential to evaluate yield variability over time by using alternative (to the traditionally statistical approach) analysis methods, for example pattern recognition. The research idea of Multi-temporal Yield Pattern Analysis (MYPA) was inspired by the digital soil mapping method Multitemporal Soil Pattern Analysis (MSPA). In order to produce soil property maps, the MSPA method extracts stable soil reflectance pattern from satellite time series using pattern recognition combined with statistical pattern stability analysis. The MYPA approach is the adaption of image analysis techniques of the remote sensing discipline (here: pattern recognition) to agronomic data (here: yield data). The current state of the MYPA method will be presented that makes it possible to i) select outlier yield maps from yield map time series, ii) detect spatially homogenous yield pattern, and iii) evaluate their spatiotemporal variability. This method enables the generation of site-specific crop management zones considering both the productivity and stability of yield over space and time. The MYPA method consists basically of following steps: (1) identification and elimination of outlier yield maps, (2) yield pattern detection using principal component analysis; (3) evaluation of spatiotemporal yield pattern stability using statistical per-pixel analysis; and (4) management zones delineation based on k means clustering. Results from one demonstration field are presented and contrasted (with favourable outcomes) with the more traditional statistical mean approach to multi-temporal yield pattern delineation.    

Keyword: Yield maps, Spatio-temporal yield variability, k-means clustering, Principal component analysis