A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields

A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields Continuous paddy rice cultivation requires fields to be flooded most of the time limiting seriously the collection of detailed soil information. So far, no appropriate soil sensor technology for identifying soil variability of flooded fields has been reported. Therefore, the primary objective was the development of a sensing system that can float, acquire and process detailed geo-referenced soil information within flooded fields. An additional objective was to determine whether the collected apparent electrical conductivity (ECa) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record ECa using the electromagnetic induction sensor EM38, which does not require physical contact with the soil. Its feasibility was tested in an alluvial paddy field of 2.7 ha located in the Brahmaputra floodplain of Bangladesh. The high-resolution (1 × 1 m) ECa data were classified into three classes using the fuzzy k-means classification method. The variation among the classes could be attributed to differences in subsoil (0.15–0.30 m below soil surface) bulk density, with the smallest ECa values representing the lowest bulk density. This effect was attributed to differences in compaction of the plough pan due to differential puddling. There was also a significant difference in rice yield among the ECa classes, with the smallest ECa values representing the lowest yield. It was concluded that the floating sensing system allowed the collection of relevant soil information, opening potential for precision agriculture practices in flooded crop fields. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields

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Springer US
Copyright © 2011 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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