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• 1. [Image] Growing wheat in the Klamath Valley, Oregon

  	October 24, 1907
  	Citation × Citation Citation Abstract Copy Title: Growing wheat in the Klamath Valley, Oregon Author: United States. Bureau of Reclamation Year: 1907, 2005, 2004 October 24, 1907 Title: Growing wheat in the Klamath Valley, Oregon Author: United States. Bureau of Reclamation Year: 1907, 2005, 2004 October 24, 1907 Close

• 2. [Image] Men shocking wheat

  	Photo possibly taken near present day Milton-Freewater, Oregon.
  	Citation × Citation Citation Abstract Copy Title: Men shocking wheat Year: 1910 Photo possibly taken near present day Milton-Freewater, Oregon. Title: Men shocking wheat Year: 1910 Photo possibly taken near present day Milton-Freewater, Oregon. Close

• 3. [Image] Sacks of wheat on James C. Wyatt's field

  	983 - Klamath; September 15, 1941
  	Citation × Citation Citation Abstract Copy Title: Sacks of wheat on James C. Wyatt's field Author: United States. Bureau of Reclamation Year: 1941, 2006, 2005 983 - Klamath; September 15, 1941 Title: Sacks of wheat on James C. Wyatt's field Author: United States. Bureau of Reclamation Year: 1941, 2006, 2005 983 - Klamath; September 15, 1941 Close

• 4. [Article] Remote-sensing and geographic information system techniques to map spatial variation of wheat grain yield

  Farmers and researchers are aware of spatial variation in grain yield within farms or fields. Fertilizer management may be improved if techniques can be developed to identify grain yield variations in ...

  Citation × Citation Citation Abstract Copy Title: Remote-sensing and geographic information system techniques to map spatial variation of wheat grain yield Author: Roberts, Michael C. (Michael Coy), 1951- Farmers and researchers are aware of spatial variation in grain yield within farms or fields. Fertilizer management may be improved if techniques can be developed to identify grain yield variations in wheat fields. Aerial color infrared (CIR) photography was used to identify winter wheat (Triticum aestivum L.) canopy biomass variability in the Spring of the growing season. Low yielding areas identified from CIR photography were associated with shallow soil profiles consistent with soil forming factors of the region, and were significantly different from average and high yielding areas. The high yielding areas were located within a few meters of a drainage way, and were not significantly different than the average yielding areas except in one field with a deep soil profile and low variance. Fields with heterogeneous CIR photographs had high variances because of many dissimilar inclusions. CIR photography, although useful to distinguish vegetational differences, requires complex timing, ground verification, and correction to estimate yield variability. A geographic information system (GIS) was used to overlay photo interpreted biomass and soil map units. The overlay analysis allowed construction of a higher (first) order soil map indicating inclusions. Area calculation of the inclusions and map units using a GIS function combined with estimated yield (no variance estimates or confidence intervals associated with the estimated yield) data suggests fertilizer management with a first order soil map to increase fertilizer efficiency by up to six percent. Future research combining remotely-sensed subsidiary variables correlated with moisture supply capacity estimates from soil survey methods may assess, using relatively new spatially dependent interpolation methods, the local and regional variation in wheat grain yield. Title: Remote-sensing and geographic information system techniques to map spatial variation of wheat grain yield Author: Roberts, Michael C. (Michael Coy), 1951- Farmers and researchers are aware of spatial variation in grain yield within farms or fields. Fertilizer management may be improved if techniques can be developed to identify grain yield variations in wheat fields. Aerial color infrared (CIR) photography was used to identify winter wheat (Triticum aestivum L.) canopy biomass variability in the Spring of the growing season. Low yielding areas identified from CIR photography were associated with shallow soil profiles consistent with soil forming factors of the region, and were significantly different from average and high yielding areas. The high yielding areas were located within a few meters of a drainage way, and were not significantly different than the average yielding areas except in one field with a deep soil profile and low variance. Fields with heterogeneous CIR photographs had high variances because of many dissimilar inclusions. CIR photography, although useful to distinguish vegetational differences, requires complex timing, ground verification, and correction to estimate yield variability. A geographic information system (GIS) was used to overlay photo interpreted biomass and soil map units. The overlay analysis allowed construction of a higher (first) order soil map indicating inclusions. Area calculation of the inclusions and map units using a GIS function combined with estimated yield (no variance estimates or confidence intervals associated with the estimated yield) data suggests fertilizer management with a first order soil map to increase fertilizer efficiency by up to six percent. Future research combining remotely-sensed subsidiary variables correlated with moisture supply capacity estimates from soil survey methods may assess, using relatively new spatially dependent interpolation methods, the local and regional variation in wheat grain yield. Close