Risk analysis of the economic viability of feedlot aberdeen angus steers fed with different proportions of concentrate
DOI:
https://doi.org/10.14393/BJ-v33n3-34547Keywords:
Investment analysis, investment project, probabilistic analysis, stochastic method, rank correlationAbstract
The economic viability of feedlot Aberdeen Angus steers fed with diets composed of different concentrate levels (CL) in dry matter (25, 40, 55 or 70%) was estimated using Monte Carlo simulation combined with Spearman rank correlation, considering nine random input variables, as well as stochastic dominance (DOM) and sensitivity (SENS) analyses. For the financial indicator simulation, net present value (NPV), cash flow with indicators of performance, and the probability distribution of all cost and income items (from 2003 to 2014) were used. Latin hypercube sampling and a Mersenne Twister random number generator was employed for the simulation, which included 2000 interactions. The expected mean values ± standard deviation for NPV (USD/animal) were 44.94 ± 68.01, 44.50 ± 69.25, 15.39 ± 69.22 and 54.20 ± 71.58 for the diets containing 25, 40, 55 and 70% CL, respectively. The probability of NPV ≥ 0 was 76.8, 76.0, 57.9 and 78.1%, respectively, from the smallest to largest CL. The DOM analysis showed that 25 and 40% CL have similar probability curves, the 70% level dominated the remaining and all CL dominated 55%. According to SENS analysis, the items that most influenced the NPV were, in decreasing order, finished and feeder cattle price, initial and final weights, concentrate and roughage price, concentrate intake, minimum rate of attractiveness and roughage intake. Based on the simulation results, the 70% CL showed a higher NPV and greater likelihood of economic viability. The probabilistic simulation technique is an interesting tool for decision-making in investment projects with beef cattle feedlot, therefore, further studies in this line of research is recommended.
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Copyright (c) 2017 Joilmaro Rodrigo Pereira Rosa, Angelina Camera, Paulo Santana Pacheco, Edom de Avila Fabricio, Daniel Batista Lemes
This work is licensed under a Creative Commons Attribution 4.0 International License.