Hybrid Simulation Model For Economic-Financial Evaluation In Integrated Crop-Livestock Systems

Abstract

Mixed or integrated Crop-Livestock Systems (iCLS) have been in focus for the potential benefits they can provide concerning environmental, social, and economic directions, as compared with those of monoculture systems; however, iCLS practices demand more processes, management, and organization to implement. The number of studies that used Hybrid Simulation Models (HSM) applied to the iCLS are narrow. In this study, we used Discrete Event (DES) and Agent-Based (ABS) Simulation methods to develop models in a top-down analysis with a more general view that examined the individual agents. The objective of this study was to create an HSM based on agentes for iCLS and to evaluate the productive and economic indicators of these Productions with monoculture systems. The model was developed to represent the behavior of corn production and grazing cattle and the integration between them, and the model was parameterized using real data from an experiment carried out in the field in the state of São Paulo, Brazil. The development of the HSM was implemented in AnyLogic® software. All information on the parameters used in the HSM came from spreadsheets, which were processed in the model (in AnyLogic®) and transcribed into text files. Results from the model evaluation found the total cost in Beef Cattle Monoculture (BCM) was about 58% higher than the results found in iCLS treatments for livestock production. Financial profitability for Net Margin (NM) was viable for corn monoculture (CM) and unviable for BCM. In the integrated systems, NM was viable for all treatments. These results were then represented by the Internal Rate of Return. The economic-financial gains depended in part on the productive arrangement in iCLS, and therefore the simulated (in silico) models gained importance by allowing researchers to test hypotheses in advance. ABS and DES methods with stochastic variables have demonstrated their utility for HSM in the context of iCLS. The increases in the operational capacity of computers and big data have allowed the programming models to be more complex, so the HSM developed in this study may be able to predict more precise outcomes. AnyLogic® allows interface with spreadsheets, which facilitates the modeler's work about changes in parameter values during the simulation run and gives the HSM more usability and supports the decision-making process. The HSM has the potential to serve other animal and plant production systems and is therefore worthy of the further studies needed to evaluate these systems more thoroughly.