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NetLogo User Community Models
REQUIRED ASSETS FOR RUNNING THE MODEL
Download from http://dl.dropbox.com/u/59330867/BEFERGYONET%20MODEL.zip
## WHAT IS IT?
This is an intertemporal and spatial model that shows the potential economic and environmental impacts in the Appalachian region by introducing a diversified pasture-based beef (PBB) industry, including beef, bio-fuel, and carbon offsets.
The BEFERGYONET model (BET) is divided into two stages. In the first stage, the model simulates forage growth based on solar radiation, temperature, precipitation and location coordinates. When the pasture available is maximized, the model is able to obtain the optimal stocking rate for each farm distributed in the spatial domain. During the second stage, BET simulates agents' interactions within the whole system. As a result of agents interactions, high quality beef, renewable energy and eventually carbon offset production is determined. It basically simulates the introduction of this industry and its interaction with surrounding communities in an attempt to satisfy both producer goals and societal goals. Results are obtained using both deterministic and stochastic scenarios.
Finally, the model allows for the development of policy instruments based on the results from the agent-based model that would enhance the implementation of these management techniques as a form of maximizing beef farmers' profitability as well as social welfare in which clustering among locations contributes in intensifying the benefits from sustainable BMPs.
## HOW IT WORKS
Every farm in the spatial domain relies on 93 acres of pastureland (average for this region, Evans et. al, 2007) which is divided into 6 paddocks (Schuster et. al, 2001) of approximately 15 acres each where 10 patches represent 1 acre. The farm of interest is a stocker farm identified with the color red surrounded by adjacent cow/calf farms (gray), stocker farms (brown) and one silage farm (blue). This occurs within a radius of approximately 22 miles (when distance-factor = 0.5) derived from the interaction among participating farms in the clustering system and invoked by the farm of interest. The farm of interest (or contracting farm) is the business taking the primary risk of investing in an anaerobic digester.
Farms are distributed throughout a grid of patches identified by their coordinates allowing the simulation to measure their distances when the clustering system is activated. The model also simulates farmers� interaction with the stocking rate during the grazing season by rotating it from one paddock to the next within a dynamic context. This interaction provides a fairly realistic representation of a PBB industry where the land resource is optimized considering both producer and societal goals.
It is assumed that daily pasture intake per head is 3 percent of its body weight (Zobell, Burrell and Bagley, 1999, Rayburn, 2005, Schuster et. al, 2001) with a daily weight gain of 1.5 and 0.87 pounds during the grazing and winter seasons, respectively (Rayburn, 2008, Blaser et., al 1986, VAFS, 1969). The stocking rate grazes 2/3 of the paddocks while 1/3 is used for hay or silage for winter feed each year based on expert opinion. The stocking rate composed of an Angus breed with an initial weight of 500 pounds (Rayburn and Lozier, 2002, Schuster et. al, 2001) is bought at the end of April and grazing is initiated in early May and moved to a building during the winter where animals are fed and manure is collected for energy generation.
The model also simulates the interaction of several agents such as hauling trucks for the movement of forage as well as manure from adjacent farms to the farm of interest which is invoked by the farm of interest depending on the parameters selected by the user. During the system interaction, the production of beef, electricity and CO2E emissions reduction and CO2E emissions baseline are graphically generated as well as sent to a spreadsheet file named "netlogoresults.csv" indicated at the beginning of the simulation.
## HOW TO USE IT
1. Select the desired parameters by clicking on each of the choices available such as "initial-weight".
## THINGS TO NOTICE
If the slope range between the farm of interest and adjacent stocker farms are the same, then the stocking rate for both are equal (the first phase). Observe what happens to electricity generation during grazing season (the second phase). Also observe what happens to daily beef production. Are stochastic death loss percentages the same on an annual basis? What happens to pasture growth when precipitation and temperature are high?
## THINGS TO TRY
The default parameters are the following:
Try comparing results between different counties. This might help policy-makers and entrepreneurs to determine what counties could bring more benefits to farmers and society based on resources available in the area which are affected by topographical as well as climatological conditions.
Moreover, try adjusting dry matter intake as well as initial-pounds and num-acres-list parameters to observe sensitivity of the model to these particular parameters.
Also, try adjusting the distance-factor to observe the impacts toward profitability since longer distances tend to increase transportation costs when inputs are moved from nearby farms to the farm of interest.
## EXTENDING THE MODEL
The model can be extended to capture CO2 emissions generated by the hauling trucks to reflect a more complex and closer to reality approach. Other policy implications can also be added to the model.
## NETLOGO FEATURES
The model compares a diversified pasture-based business (beef, electricity and carbon offset) with a specialized one (beef production only) under deterministic and stochastic simulations. The model uses climatological, price and cost data as well as published equations and parameters from PBB studies, NOAA (15 years data), governmental agencies and renewable energy studies, among others.
## RELATED MODELS
Wolf Sheep Predation (System Dynamics and Docked)
This model was created by Inocencio Rodriguez, Gerard D'Souza, Edward Rayburn and Tom Griggs as part of a dissertation at West Virginia University-Resource Management Division, June 2012.
If you mention this model in an academic publication, we ask that you include these citations for the model itself, the NetLogo software and the NetLogo-R-Extension:
**Note: We would like to acknowledge the helpful input of Alan Collins, Tim Phipps and Don Lacombe. We would also like to express our gratitude to the USDA, ARS-funded Appalachian pasture-beef project for providing the funding to undertake this research project and Dr. Jan Thiele for his support during the NetLogo-R-Extension installation.
-Blaser, R.E., Hammes, R.C., Fontenot, J.P., Bryant, H.T., Polan, C.E., Wolf, D.D., McClaugherty, F.S., Kline, R.G. and Moore, J.S. (1986). Forage-Animal Management Systems.Virginia Agricultural Experiment Station, Virginia Polytechnic Institute and State University Research Division Bulletin No. 86-7.
-Evans, J., D'Souza, G.E., Brown, C., Collins, A., Rayburn, E.B. and Sperow, M. (2007). Determining Consumer Perceptions of and Willingness to Pay for Appalachian Grass-fed Beef: An Experimental Approach. (Doctoral Dissertation). Retrieved from http://wvuscholar.wvu.edu:8881//exlibris/dtl/d3_1/apache_media/L2V4bGlicmlzL2R0bC9kM18xL2FwYWNoZV9tZWRpYS8xMzg4MQ==.pdf
-Rayburn, E. (2005). Pasture Management for Pasture-Finished Beef. Extension Service, West Virginia University. Retrieved from http://www.wvu.edu/~agexten/forglvst/pasturemang.pdf
-Rayburn, E., ed. (2008). Animal Production Systems for Pasture-Based Livestock Production. Natural Resource, Agriculture, and Engineering Service-NRAES-171: New York.
-Rayburn, E. and Lozier, J. (2002). Pasture-Based Beef Systems for Appalachia Preliminary Report of a Nationwide Survey. Retrieved from http://www.wvu.edu/~agexten/forglvst/PFBPrpt.pdf
-Schuster, D., Undersander, D., Schaefer, D., Klemme, R. M., Siemens, M. and Smith, L. (2001). Stocker Enterprise Budgets for Grass-based Systems. A3718. UW-Extension. University of Wisconsin. Retrieved from http://learningstore.uwex.edu/assets/pdfs/A3718.pdf
-Thiele J.C. and Grimm V. (2010). NetLogo meets R: Linking agent-based models with a toolbox for their analysis. Environmental Modelling and Software 25(8): 972 - 974. [DOI: 10.1016/j.envsoft.2010.02.008]. Retrieved from http://netlogo-r-ext.berlios.de/
-Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
-Wilensky, U. (2005). NetLogo Wolf Sheep Predation (docked) model. http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation(docked). Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
-Wilensky, U. (2005). NetLogo Wolf Sheep Predation (System Dynamics) model. http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation(SystemDynamics). Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
-William, J.C. and Hall, M.H. (1994). Four Steps to Rotational Grazing. Cooperative Extension Service, The Pennsylvania State University: Agronomy Facts 43. Retrieved from http://www.forages.psu.edu/agfacts/agfact43.pdf
-Virginia Forage Research Station [VAFS]. (1969). Managing Forages for Animal Production: 1949-1969. History and Research Findings. Virginia Forage Research Station, Virginia Polytechnic Institute-Research Division Bulletin No. 45.
-ZoBell, D., Burrell, C. and Bagley, C. (1999). Raising Beef Cattle on Few Acres. Extension Service. Utah State University.
**Note: For complete information regarding references associated with the BET model as well as its concept, please take a look at the dissertation titled: Land as a Renewable Resource: Integrating Climate, Energy, and Profitability Goals using an Agent-Based NetLogo Model at the West Virginia University website. We will post the link to the dissertation, once it is available.
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