Technical and economic analysis, and benchmarking associated with dairy farms in Minas Geraiz, Brazil
Jardeson de Souza Pinheiro A , Lucas Henrique de Souza Matias A , Claudia Batista Sampaio A and Marcos Inácio Marcondes
B *
+ Author Affiliations
- Author Affiliations
A Animal Science Department, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
B Department of Animal Sciences, Washington State University, Pullman, WA, USA.
Animal Production Science 63(2) 178-191 https://doi.org/10.1071/AN22050
Submitted: 15 February 2022 Accepted: 6 September 2022 Published: 30 September 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Dairy operations have adopted benchmarking as a methodology to rank farms and establish target indexes; however, a connection between benchmarking and farms in the tropics is still warranted.
Aims: To evaluate the technical and economic quartiles based on farm return on assets (ROA) of three regions (Centre, South and Triangle) of Minas Gerais state, Brazil, and use them to establish benchmarks for dairy farms.
Methods: We collected data from 128 dairy farms (from January to December of 2019). All properties were part of the Educampo® project/Sebrae-MG. Farms were grouped into the Centre, South and Triangle regions, and subdivided into three groups within each region according to their ROA, where 25% of the farms that presented the lowest ROA were classified as the first quartile, 50% of farms were classified as interquartile and the 25% remaining farms were classified as the fourth quartile. Data were analysed as a randomised block design in a split-plot scheme, where the production systems were blocks, the regions were the main plots and the groups were the split plots. Differences were declared when P ≤ 0.10.
Key results: Total operating cost ($/L; $ – this currency is in US dollars and it applies throughout the paper); accrual operating cost ($/L); production costs, such as roughage ($/L), hired labour ($/L), percentage of concentrate and hired labour in accrual operating cost (%), were affected by regions and groups. The South and fourth quartile had the greatest total operating cost (0.24 $/L; 0.26 $/L) and accrual operating cost (0.27 $/L; 0.30 $/L), respectively. The majority of economic indexes were higher for Triangle than South and Centre, respectively. The fourth quartile had the greatest net margin (0.09 $/L), profit (0.07 $/L), return on assets (2%) and assets turnover rate (24%).
Conclusions: We suggest that benchmarks should be established by region, as there were too many variations among regions. In addition, this study demonstrated the importance of understanding the behaviour of the technical and economic indicators to stratify farms based on their return on assets.
Implications: We evaluated technical and economic indexes from three regions and stratified by ROA. Then, we established benchmarks by regions to better guide the producer in decision-making in dairy operations.
Keywords: animal production, benchmarks, dairy cows, dairy operation, financial, heifers, production costs, profitability.
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