Applications of crop growth models in precision agriculture through a GIS linkage and remote sensing
Date
2000
Authors
Seidl, Matthew Stephen
Major Professor
Advisor
Batchelor, William D.
Committee Member
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Abstract
Crop growth models are finding new uses in the area of precision farming. Two crop growth models, CERES-Maize and CROPGRO-Soybean, have recently been used to explain corn and soybean yield variability in a field in Iowa. A visual interface would facilitate management and analysis of the vast amount of data required for use of crop growth models to analyze spatial yield variability. The first objective of this thesis is to describe the design and application of a new system which links these two crop growth models to the ArcView 3.1 Geographic Information System (GIS). This program, called Crop Models Analyst, allows the user to: 1)create maps of any of the 200 variables predicted by the models on each day of simulation; 2) interactively run the model from the GIS to test hypotheses and to make comparisons with measured data; and 3) evaluate prescriptions over multiple years of simulation. The second objective of this thesis is to describe the use of imagery as an input data layer to the CROPGRO-Soybean model. The driving force behind remote sensing is the desire to cut the costs normally required for data collection and analysis. Incorporation of imagery into crop growth models is a natural fit, as the crop model is currently the only tool which can integrate the complex systems that cause yield variability into a single predictive package. It is demonstrated that the addition of imagery provides valuable information about the spatial distribution of soybean biomass across a field.
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thesis