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Dep. of Agron. and Range Sci. and Dep. of Biol. and Agric. Eng., Univ. of California, Davis, CA 95616
* Corresponding author (replant{at}ucdavis.edu)
Received for publication May 31, 2002. Data were analyzed from two 30-ha laser-leveled commercial fields in the Sacramento Valley, California, for crops grown between 1995 and 1999. Crops were wheat (Triticum aestivum L.), tomato (Lycopersicon esculentum Mill.), bean (Phaseolus vulgaris L.), sunflower (Helianthus annuus L.), and corn (Zea mays L.). One of the fields had a wheattomatobeansunflower rotation while the other had a wheattomatosunflowercorn rotation during the same period. Yield data were collected with a commercial yield monitor, except for tomato yield data, which were collected with an experimental yield monitor. After georeferencing, interpolation, and correction of yield-monitored data, analysis was performed on grid cells representing an area of 20-m square. Soil cores were extracted from the fields on a 60-m square grid. Yield and sand content were both separated into large- and small-scale components by median polish. Persistent large-scale trends in yield, which were consistent with large-scale trends in sand content, were observed. Six 2-yr comparisons via linear regression were performed in each field. Yield of one crop was a poor predictor of yield of another crop grown in another year, but after standardizing and averaging the yields, areas with the same average performance tended to be clustered together spatially. The standardized yields were also analyzed using K-means clustering. This provided a different spatial configuration of clusters from that of the standardized average but also a high level of spatial autocorrelation, which shows that both methods may be helpful in delineating management zones at the scale normally used by growers.
Abbreviations: CV, coefficient of variation SOM, soil organic matter SSM, site-specific management
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