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Subjects Triticum aestivum Remove constraint Triticum aestivum: Triticum aestivum Subjects grain yield Remove constraint grain yield: grain yield Subjects prediction Remove constraint prediction: prediction

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1. A remote sensing model for the prediction of wheat yields prior to harvest

Description:: Includes references
Author:: Idso, S.B. , Pinter, P.J. Jr. , Hatfield, J.L. , Jackson, R.D. , Reginato, R.J.
Subject:: Triticum aestivum , crop models , cultivars , grain yield , harvesting , plant growth , prediction , reflectance , remote sensing , temperature , wheat
Collection:: Journal Articles, USDA Authors, Peer-Reviewed
Year:: 1979

2. Grain yield and biomass relationship for crops in the Inland Pacific Northwest United States

Description:: Includes references
Author:: McClellan, R.C. , McCool, D.K. , Rickman, R.W.
Subject:: Brassica , Hordeum vulgare , Revised Universal Soil Loss Equation , Triticum aestivum , Water Erosion Prediction Project , agricultural land , barley , biomass , carbon sequestration , control methods , crop residues , energy crops , grain yield , legumes , prediction , soil , vegetation cover , water erosion , wind erosion , winter wheat , Idaho , Oregon , Washington
Collection:: Journal Articles, USDA Authors, Peer-Reviewed
Year:: 2012

3. In-season prediction of potential grain yield in winter wheat using canopy reflectance

Description:: Includes references
Author:: Raun, W.R. , Solie, J.B. , Johnson, G.V. , Stone, M.L. , Lukina, E.V. , Thomason, W.E. , Schepers, J.S.
Subject:: Triticum aestivum , grain yield , prediction , crop yield , canopy , reflectance , nitrogen fertilizers , vegetation , red light , infrared radiation , estimation , lodging , seed shattering
Collection:: Journal Articles, USDA Authors, Peer-Reviewed
Year:: 2001

4. Relating crop yield to topographic attributes using Spatial Analysis Neural Networks and regression

Description:: Includes references
Author:: Green, Timothy R. , Salas, Jose D. , Martinez, Ana , Erskine, Robert H.
Subject:: grain yield , prediction , topography , slope , spatial data , regression analysis , neural networks , digital elevation models , algorithms , Triticum aestivum , winter wheat , soil water , dryland farming , dry environmental conditions , semiarid zones , latitude , longitude , equations , Colorado
Collection:: Journal Articles, USDA Authors, Peer-Reviewed
Year:: 2007

5. Utilizing Existing Sensor Technology to Predict Spring Wheat Grain Nitrogen Concentration

Description:: Includes references
Author:: Qualm, A.M. , Osborne, S.L. , Gelderman, R.
Subject:: Triticum aestivum , spring wheat , grains , nitrogen , chemical constituents of plants , chemical concentration , fertilizer rates , fertilizer application , grain yield , prediction , remote sensing , precision agriculture , foliar application , optical equipment , sensors , developmental stages , Great Plains region , South Dakota
Collection:: Journal Articles, USDA Authors, Peer-Reviewed
Year:: 2010