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| Filter results6 paper(s) found. |
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1. Is A Nitrogen-rich Reference Needed For Canopy Sensor-based Corn Nitrogen Applications?The nitrogen (N) supplying capacity of the soil available to support corn (Zea mays L.) production can be highly variable both among and within fields. In recent years, canopy reflectance sensing has been investigated for in-season assessment of crop N health and fertilization. Typically the procedure followed compares the crop in an area known to be non-limiting in N (called a N-rich area) to the crop in areas inadequately fertilized. Measurements from the two areas are used to calculate... N.R. Kitchen, K.S. Suddth, S.T. Drummond |
2. Comparison Of Three Canopy Reflectance Sensors For Variable-rate Nitrogen Application In CornIn recent years, canopy reflectance sensing has been investigated for in-season assessment of crop nitrogen (N) health and subsequent control of N fertilization. The several sensor systems that are now commercially available have design and operational differences. One difference is the sensed wavelengths, although these typically include wavelengths in both the visible and near-infrared ranges. Another difference is orientation – the sensors most commonly used in the US are designed to... K.A. Sudduth, N.R. Kitchen, S.T. Drummond |
3. Cotton Precision Farming Adoption In The Southern United States: Findings From A 2009 SurveyThe objectives of this study were 1) to determine the status of precision farming technology adoption by cotton producers in 12 states and 2) to evaluate changes in cotton precision farming technology adoption between 2000 and 2008. A mail survey of cotton producers located in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, Tennessee, Texas and Virginia was conducted in February and March of 2009 to establish the use of precision farming technologies... M. Velandia, D.F. Mooney, R.K. Roberts, B.C. English, J.A. Larson, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, C. Wang, E. Segarra, J.M. Reeves |
4. Vegetation Indices from Active Crop Canopy Sensor and Their Potential Interference Factors on SugarcaneAmong the inputs usually used in the sugarcane production the nitrogen (N) is the most significant. With the use of ground-based canopy sensors to obtain vegetation indexes (VI), it is possible to obtain recommendations of nutrient supply in... L.R. Amaral, J.P. Molin, L. Taubinger |
5. Measurement of Systematic Errors in Crop PredictionPrecision agriculture typically attempts to answer grower questions using an increasingly more fine-grained analysis. However, some entities, such as cooperatives, can have an interest in answers that are spatially course-grained, such as obtaining an estimate of the overall crop production within a season. Errors in factors that most influence fine-grained predictions, such as soil quality, may have a smaller impact on overall yield forecasts since their effect is likely to average... A.M. Denton, E.W. Mosmen, J.X. Xu |
6. A Long-Term Precision Agriculture System Maintains ProfitabilityAfter two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation practices can now be assessed. Field-scale profitability of a conventional or ‘business-as-usual’ system with an annual corn (Zea mays L.)-soybean (Glycine max [L.]) rotation and annual tillage was assessed for 11 years on a 36-ha field in central Missouri during 1993 to 2003. Following this, a ‘precision agriculture... M.A. Yost, N.R. Kitchen, K.A. Sudduth, S.T. Drummond, R.E. Massey |