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ARS Home » Pacific West Area » Pendleton, Oregon » Columbia Plateau Conservation Research Center » Research » Publications at this Location » Publication #309477

Research Project: Cultural Practices and Cropping Systems for Economically Viable and Environmentally Sound Oilseed Production in Dryland of Columbia Plateau

Location: Columbia Plateau Conservation Research Center

Title: On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat

Author
item Long, Daniel
item McCallum, John

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/13/2015
Publication Date: 3/28/2015
Publication URL: http://handle.nal.usda.gov/10113/4399572
Citation: Long, D.S., Mccallum, J.D. 2015. On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agriculture. 16:492-504. doi: 10.1007/s11119-015-9391-z.

Interpretive Summary: A method was developed for simultaneously mapping the grain yield, grain protein, and straw yield in farm fields from a combine harvester. The mapping system consists of three different sensors: mass-flow yield monitor to measure grain yield, in-line near infrared (NIR) sensor to measure grain protein, and light detection and ranging (LiDAR) sensor to measure crop height. Straw yield was estimated from crop height as derived from LiDAR. The resulting data can be used to map various indicators of environmental stress. One indicator is the harvest index (HI), or ratio of grain yield to total above ground crop biomass (e.g., grain yield + straw yield). Using HI, one can identify areas within a field where the crop experienced stress during vegetative growth, stress during grain filling, stress during both periods, or no stress. Application of this multi-sensor approach will enable farmers to better interpret maps of grain yield in their fields.

Technical Abstract: On-combine yield monitors are widely used in precision agriculture for locating areas within fields where yields are reduced. However, the crop yield variability can be better interpreted by utilizing grain protein maps to reveal the factors limiting yield. The objective of this study was to develop an on-combine multi-sensor system for obtaining site-specific measurements of grain yield, grain protein concentration, and straw yield at the same spatial resolution as grain yield. The methodology is based on a mass flow yield monitor, in-line near-infrared (NIR) spectrometer, and light detection and ranging (LiDAR) instrument. The LiDAR sensor is used to indirectly estimate straw yield through the measurement of crop height. Neighborhoods within the individual grain yield and protein maps obtained by the yield monitor and the protein sensor are correlated to identify areas within fields where grain yield was limited by nitrogen stress or water stress. In addition, scatter plots of grain yield and straw yield, and deviations from the observed maximum slope, are used to identify specific regions of environmental stress within fields. Multi-sensor data are acquired at coincident locations and thus, it is not necessary to interpolate data to a common estimation grid to enable their fusion. The on-combine, multi-sensor system is illustrated with results from farm fields in eastern Oregon, USA.