NDLI: Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.)

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Application of a web-based remote agro-ecological monitoring system for observing spatial distribution and dynamics of Bactrocera dorsalis in fruit orchards

Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.)

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Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.)

Content Provider	Springer Nature Link
Author	Walsh, Olga S. Klatt, A. R. Solie, J. B. Godsey, C. B. Raun, W. R.
Copyright Year	2012
Abstract	Previous studies have shown the importance of soil moisture (SM) in estimating crop yield potential (YP). The sensor based nitrogen (N) rate calculator (SBNRC) developed by Oklahoma State University utilizes the Normalized Difference Vegetation Index (NDVI) and the in-season estimated yield (INSEY) as the estimate of biomass to assess YP and to generate N recommendations based on estimated crop need. The objective was to investigate whether including the SM parameter into SBNRC could help to increase the accuracy of YP prediction and improve N rate recommendations. Two experimental sites (Lahoma and Perkins) in Oklahoma were established in 2006/07 and 2007/08. Wheat spectral reflectance was measured using a GreenSeeker™ 505 hand-held optical sensor (N-Tech Industries, Ukiah, CA). Soil–water content measured with matric potential 229-L sensors (Campbell Scientific, Logan, UT) was used to determine volumetric water content and fractional water index. The relationships between NDVI, INSEY and SM indices at planting and sensing at 5, 25, 60 and 75-cm depths versus grain yield (GY) were evaluated. Wheat GY, NDVI at Feekes 5 and soil WC at planting and as sensed at three depths were also analyzed for eight consecutive growing seasons (1999–2006) for Lahoma. Incorporation of SM into NDVI and INSEY calculations resulted in equally good prediction of wheat GY for all site-years. This indicates that NDVI alone was able to account for the lack of SM information and thus lower crop YP. Soil moisture data, especially at the time of sensing at the 5-cm depth could assist in refining winter wheat YP prediction.
Starting Page	343
Ending Page	356
Page Count	14
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	14
Issue Number	3
e-ISSN	15731618
Language	English
Publisher	Springer US
Publisher Date	2012-12-19
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Yield potential Precision sensing Nitrogen fertilizer rates Soil water content Agriculture Soil Science & Conservation Remote Sensing/Photogrammetry Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Atmospheric Sciences
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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