|Bakhsh, A - IOWA STATE UNIVERSITY|
|Kanwar, Ramesh - IOWA STATE UNIVERSITY|
Submitted to: Asian Association for Agricultural Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 5, 2005
Publication Date: December 30, 2005
Citation: Bakhsh, A., Kanwar, R., Jaynes, D.B., Colvin, T.S., Ahuja, L.R. 2005. Modeling precision agriculture for better crop productivity and environmental quality. Asian Association for Agricultural Engineering. 14(4):235-243. Interpretive Summary: Recent research in agriculture has been targeted at evaluating and developing the concept of precision farming. The concept of precision farming is that agricultural inputs such as seed, fertilizer, and herbicides need to be applied at variable rates across a field rather than uniformly as currently practiced. It is thought that variable application of inputs will maximize yield and/or economic returns while minimizing detrimental affects on water and soil resources. Before these concepts can be successfully adopted by farmers, we need to understand how yields vary across fields, what factors are determining these yield patterns, and how and if they will respond to management. In a multi-year study, we showed that yield patterns within a 60-ac field changed yearly and that there was little similarity among yearly patterns. Yield variability is typically driven by variations in soil and topography, both of which varied little across this field. However, we were able to apply the ARS model RZWQM to simulate corn yield as a function of weather and nitrogen fertilizer rate, giving us confidence that the model can be used to fine-tune fertilizer management in the future. These findings are of great importance not only to agricultural scientists investigating precision farming technologies, but also to producers, crop consultants, and industries who are interested in pursuing precision farming technologies and practices.
Technical Abstract: Adoption of precision agriculture requires delineation of the stable management zones so that site-specific management practices can be applied to improve crop productivity and environmental quality. A quantitative approach was developed to delineate the zones by using the map overlay capability of Geographic Information System (GIS) for the soil type, topography and crop yield data layers based on field data from 1996 through 1999. The study was conducted on a 22-ha tile drained corn (Zea mays L.)-soybean (Glycine max L.) rotation field, located near Story City, Iowa. The integration of data layers showed that low yield zones were consistent from year to year and were affected by the interaction of soil type and topographic attributes. The simulations of the calibrated and validated Root Zone Water Quality model (RZWQM98) for this field showed that corn yield response function reached the plateau level when N-application rate exceeded 200 kg ha-1 in 1996 and 170 kg ha-1 in 1998. These results suggest that RZWQM can be used to simulate the yield response function for each zone delineated on the basis of long-term yield data to move a step forward in the adoption of precision agriculture system.