Abstract

Information products derived from multi-spectral remote sensing images, LIDAR elevations, or data products from other sensor systems (soil electrical conductivity measurements, yield monitors, etc.) characterize potential crop productivity by mapping biophysical aspects of cropland variability. These sensor systems provide spectral, spatial, and temporal measurements at resolutions and accuracies describing the variability of in-field, physical characteristic phenomena, including management practices from cropland preparation, selection of crop cultivars, and variable-rate applications of inputs. In addition, DGPS-equipped (differential, global positioning system) harvesters monitor yield response at closely spaced, georeferenced points. Geographic information system and image processing techniques fuse diverse information sources to spatially characterize cropland, describe management practices, and quantify the variable yield response. Following fusion of information sources, effectiveness of spatially applied management practices may be evaluated by designed experiments assessing impacts on yield caused by geo-referenced relationships between (1) uncontrollable spatial components (the environment) and (2) controllable management practices (cultivar selection, fertility management, herbicide, insecticide, and plant growth regulator applications, etc.). These kinds of experiments can be designed because farming equipment can be computer controlled through DGPS giving farmers the ability to continuously change applied treatments for many farming operations. A mixed linear model involving both uncontrollable and controllable management attributes attached as spatial descriptors to yield monitor points evaluates effects of management practices on yield. An example based upon cotton production demonstrates the methodology. Additional strategies for designing studies in commercial cotton fields involving spatial information are discussed.

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Apr 25th, 10:00 AM

INFORMATION TECHNOLOGIES AND THE DESIGN AND ANALYSIS OF SITE-SPECIFIC EXPERIMENTS WITHIN COMMERCIAL COTTON FIELDS

Information products derived from multi-spectral remote sensing images, LIDAR elevations, or data products from other sensor systems (soil electrical conductivity measurements, yield monitors, etc.) characterize potential crop productivity by mapping biophysical aspects of cropland variability. These sensor systems provide spectral, spatial, and temporal measurements at resolutions and accuracies describing the variability of in-field, physical characteristic phenomena, including management practices from cropland preparation, selection of crop cultivars, and variable-rate applications of inputs. In addition, DGPS-equipped (differential, global positioning system) harvesters monitor yield response at closely spaced, georeferenced points. Geographic information system and image processing techniques fuse diverse information sources to spatially characterize cropland, describe management practices, and quantify the variable yield response. Following fusion of information sources, effectiveness of spatially applied management practices may be evaluated by designed experiments assessing impacts on yield caused by geo-referenced relationships between (1) uncontrollable spatial components (the environment) and (2) controllable management practices (cultivar selection, fertility management, herbicide, insecticide, and plant growth regulator applications, etc.). These kinds of experiments can be designed because farming equipment can be computer controlled through DGPS giving farmers the ability to continuously change applied treatments for many farming operations. A mixed linear model involving both uncontrollable and controllable management attributes attached as spatial descriptors to yield monitor points evaluates effects of management practices on yield. An example based upon cotton production demonstrates the methodology. Additional strategies for designing studies in commercial cotton fields involving spatial information are discussed.