Stochastic simulation of the economic viability of feedlot steers fed with different proportions of concentrate
Keywords:Investment analysis, Investment project, Probabilistic analysis, Stochastic method, Rank correlation
The economic viability of feedlot zebu bulls, slaughtered at 450 kg after 90 days of feeding with diets consisting of different proportions of concentrate in dry matter (40, 60 or 80%), was estimated using Monte Carlo simulations, with or without the inclusion of Spearman rank correlations among random input variables, stochastic dominance (DOM) and sensitivity analysis (SENS). The roughage used was chopped sugar cane. Cash flow with indicators of performance, and probability distributions of all items of cost and revenue (from 2003 to 2014), were used to stimulate net present value (NPV), the financial indicator. Latin hypercube sampling and a Mersenne Twister random number generator were employed for the simulation with 2000 interactions. The risk was found to be more accurately estimated when correlations between random input variables were included (probability of NPV ≥ 0 ± standard deviation was 35 ± 166.05% and 31 ± 139.75% for the simulation without and with correlation, respectively). Considering this result, DOM and SENS were only carried out including these correlations. The expected value for NPV was similar between the different levels of concentrate (average USD -62/animal and NPV ≥ 0 of 33%) according to DOM analysis of simulations including correlations. From the SENS analysis, the final weight, finished cattle price, feeder cattle price and initial weight were the items with the greatest influence on NPV, regardless of the level of concentrate used, followed by intake and the cost-related items of diet and minimum rate of attractiveness. Based on the results obtained by simulation, the direct benefit of feedlot could be classified as high risk, suggesting the increased use of Monte Carlo simulation for decision-making.
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Copyright (c) 2017 Rodrigo Medeiros da Silva, Rodrigo Zaiden Taveira, Fabiano Nunes Vaz, Edom de Avila Fabricio, Josiane Rodrigues Miollo, Angelina Camera, Paulo Santana Pacheco
This work is licensed under a Creative Commons Attribution 4.0 International License.