Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming

Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management... Understanding spatial variability of indigenous nitrogen (N) supply (INS) is important to the implementation of precision N management (PNM) strategies in small scale agricultural fields of the North China Plain (NCP). This study was conducted to determine: (1) field-to-field and within-field variability in INS; (2) the potential savings in N fertilizers using PNM technologies; and (3) winter wheat (Triticum aestivum L.) N status variability at the Feekes 6 stage and the potential of using a chlorophyll meter (CM) and a GreenSeeker active crop canopy sensor for estimating in-season N requirements. Seven farmer’s fields in Quzhou County of Hebei Province were selected for this study, but no fertilizers were applied to these fields. The results indicated that INS varied significantly both within individual fields and across different fields, ranging from 33.4 to 268.4 kg ha−1, with an average of 142.6 kg ha−1 and a CV of 34%. The spatial dependence of INS, however, was not strong. Site-specific optimum N rates varied from 0 to 355 kg ha−1 across the seven fields, with an average of 173 kg ha−1 and a CV of 46%. Field-specific N management could save an average of 128 kg N ha−1 compared to typical farmer practices. Both CM and GreenSeeker sensor readings were significantly related to crop N status and demand across different farmer’s fields, showing a good potential for in-season site-specific N management in small scale farming systems. More studies are needed to further evaluate these sensing technology-based PNM strategies in additional farmer fields in the NCP. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming

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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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