Modeling And Economic Analysis Of A Crop-Livestock Production System Incorporating Cereal Rye As A Forage

This thesis consists of two chapters using agent-based modeling for a crop-livestock production system incorporating human labor. The first chapter examines the principles used to develop a fundamental simulation pertaining to grazing cereal rye (Secale cereal L.) with calves. Within the software guidelines, the base model has the ability to capture diverse system interactions between livestock/plants and land management with human labor efficiency. AnyLogic incorporates agent-based modeling while combining with discrete event modeling and system dynamics. The purpose of the model was to find the economic returns of grazing cover crops relative to the area of Mead, Nebraska. In our simulation model, we used data from the University of Nebraska-Lincoln Climate Center. The model was developed to create more in depth case studies to help further the understanding of crop and livestock interactions through simulation. AnyLogic is a complex tool that has the capabilities of discovering the interactions between crops, livestock, land, and humans. In the second chapter, we examined the economic returns of grazing cereal rye with calves versus mechanically removing the cover crop. This analysis evaluated production risks due to weather variability and cattle market risk to determine the theoretical best outcome using existing weather and market data. Working with the University of Nebraska-Lincoln’s agronomy and animal science departments, we modified a cereal rye growth production model first proposed by Feyereisen et al. (2006) to match recent on-farm production trial experience in Mead, Nebraska. Based on simulation results over multiple years, it was determined that mechanically harvesting cereal rye is a better option as a long term fixed strategy than grazing cereal rye. This is largely due to cattle market risk during the spring grazing period. The costs associated with mechanically removing the crop depend on farm size and equipment used. Both chapters utilize a model simulating the grazing of cover crops developed using the AnyLogic software while the analysis on mechanically removing the forage was completed with the use of a University of Nebraska-Lincoln cover crop budget. Through bridging the gap between production and economic information, this study sought to develop a financial comparison between the two cover crop strategies for eastern Nebraska farmers. Advisor: Jay Parson

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487