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ARS Home » Plains Area » Mandan, North Dakota » Northern Great Plains Research Laboratory » Research » Publications at this Location » Publication #242583
Title: Land Allocation in Agricultural Systems: Use of a Simple Model to Examine Impacts of External Drivers

Author
item Hendrickson, John
item Hanson, Jonathan
item Archer, David
item Sassenrath, Gretchen
item Halloran, John

Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/13/2009
Publication Date: 8/23/2009
Citation: Hendrickson, J.R., Hanson, J.D., Archer, D.W., Sassenrath, G.F., Halloran, J.M. 2009. Land Allocation in Agricultural Systems: Use of a Simple Model to Examine Impacts of External Drivers. Meeting Proceedings.

Interpretive Summary:

Technical Abstract: One of the most basic and expensive resources in the agricultural enterprise is land. Optimally allocating this valuable resource among different enterprises can be difficult and has substantial impacts on profitability and sustainability of the production system. Land allocation is based on environmental, economic and production benefits and producers’ preference, risk tolerance and potential income. Producers must consider also future changes in societal values. We developed a model that uses producer derived and easily accessed information to determine land allocation among different enterprises. The model was parameterized using four agricultural enterprises common to the northern Great Plains of the USA. Initial data were derived from cropping experiments at the Northern Great Plains Research Laboratory in Mandan, North Dakota and North Dakota enterprise profitability and price data. Indices were developed for environmental quality, economic stability, production stability, and producer preference. Producer tolerance to risk and net return for each enterprise were also included. Examination of agricultural production in North Dakota suggested net return was the primary driver behind land allocation decisions. This indicates society must use economic drivers if land allocation patterns are to be impacted. Future iterations of the model will include information from different regional production systems by working with the Integrated Agricultural Systems Working Group.