Abstract
Simple SummaryThe objective of this paper was to evaluate the use of records in cattle production systems in order to determine and quantify the possible benefits on farm productivity and the economic benefit associated with their implementation. The results suggest that use of records contributes to cattle management improvement, opening a window to implement information and communication technologies in order to make management decisions based on certified information, which in turn will improve the efficiency of cattle production systems in developing countries.The level of adoption of herd management information system veterinary automated management and production control program (VAMPP) Bovine and its impact on productivity of 912 specialized dairy systems with at least 5 years of information recorded was studied. Herds were classified as low (n = 389), medium (n = 343), or high (n = 180) adoption level on the basis of extent and consistency of record keeping for variables related to production, reproduction, and health. For each herd, within-year averages were obtained for six performance traits: age at first calving (AFC), days open (DO), daily milk yield (DMY), productive life (PL), incidence of clinical mastitis (MAST), and incidence of lameness (LAM). These variables were investigated with a generalized linear mixed model that included the fixed effects of the adoption level, follow-up year, and their interaction, adjusted by the fixed effects of herd size, agroecological zone, calendar period, breed group, and the random effects of variation between/within herds. A significant effect of the adoption level over all the variables was observed, except DMY and PL. The follow-up year was significant for all the variables except LAM. There were marked reductions for AFC and DO in the first 4 years of follow-up. AFC was higher and DO shorter in the low compared to medium and high level of adoption herds (p < 0.001). DMY showed a significant increasing trend (p < 0.001), regardless of the adoption level. There was higher MAST and LAM incidence in the higher adoption level (p < 0.05). The economic benefit was estimated through a stochastic simulation model using an approach based in partial budget analysis. For a herd with a medium level of adoption, the change in gross margin (GMMIS $USD) and marginal return rate (MRRMIS %) for the first 5 years of use of the system was estimated. Under these conditions, there was a GMMIS of $6890 and MRRMIS of 163%. Variation of ±10% in DMY and DO caused changes in the GMMIS of ±$1000 and ±$110, respectively, and in MRRMIS ±24% and ±4%, respectively. The trends suggest a positive influence of VAMPP in productive and reproductive traits during the first years of implementation, with less benefit for the low adoption levels.
Highlights
To have a productive and efficient dairy production system, the producer should be able to evaluate the impact of each event and measure taken in the farm [1], or if not all, at least of the ones with the highest priority or impact on farm performance
The trends suggest a positive influence of veterinary automated management and production control program (VAMPP) in productive and reproductive traits during the first years of implementation, with less benefit for the low adoption levels
The most complete information was in the reproductive performance domain variables, with a maximum of 7901 herds per year for the at first calving (AFC) trait
Summary
To have a productive and efficient dairy production system, the producer should be able to evaluate the impact of each event and measure taken in the farm [1], or if not all, at least of the ones with the highest priority or impact on farm performance. Computer programs are available that facilitate record-keeping and its analysis, drastically shortening the time needed for data processing [2,3]. These computer programs are part of the information and communication technologies (TIC) [4] and the management information systems (MIS) [5]. These programs are tools for the automatization of the existing processes, which will help to make changes easier to drive productivity growth [6,7,8]. Most of the methods described in the literature focus on making a comparison before or after the adoption or on comparing users with non-users [13,14], without the existence of previously validated information [15]
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