Knowledge-based techniques for parameterizing spatial biophysical models.

摘要

This study presents new approaches for practical problems related to using crop models in precision agriculture. Agriculture is becoming increasingly competitive and regulated. Farmers must maximize profits yet decrease their farms' environmental impact. Precision agriculture has been proposed as a way to improve farmers' income and minimize the environmental impact of farming by optimizing the applied levels of fertilizers and other crop inputs on a site-specific basis. However, for spatially variable prescriptions to be effective, farmers need to thoroughly understand how several interacting physical and biological factors contribute to cause spatial yield variability.;Crop simulation models are software programs that imitate plant growth and development. They can help us understand spatial yield variability and how to manage it. However, crop models have expensive and impractical soil data requirements, especially for spatial applications. A technique called inverse modeling uses the crop models themselves to search for the model parameters that best fit observed results. This technique is very convenient for practical applications in precision agriculture, but its current state of development does not ensure good predictive power.;Our objectives were (1) To identify and quantitatively compare different sources of error in the use of inverse modeling to parameterize spatially coupled and uncoupled crop models. (2) To develop methods for optimizing spatial sampling schemes for representing the spatiotemporal variability of yield and yield-limiting factors. (3) To develop and evaluate a portable framework for eliciting knowledge from experts using that knowledge to parameterize a spatial crop model.;We found that crop yield spatiotemporal variability in a field can be represented using a limited number of sampling locations; that those locations can be found using efficient combinatorial optimization algorithms; and that in many applications crop model results in the sampling locations can be kept within acceptable error levels without needing the computationally intensive coupling (i.e., interchange of water) between simulation locations. This can be facilitated by imposing a set of spatial constraints on the system during the inverse modeling process. The constraints can be elicited from local domain experts.