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Monitoring systems (MS) provide additional information that many developers and researchers expect will reduce the uncertainty surrounding decision-making in livestock production and therefore enhance management decisions. However, the actual economic value of the information (VoI) yielded by MS has hardly been investigated. The aim of this study was to fill that void based on two objectives. The first is to estimate the VoI of MS prior to implementation using decision analysis based on scarce data from different sources. The second objective is to identify which factors most influence the VoI of MS and to develop recommendations about the focus of future MS development. To illustrate our objectives, we used a case study of two milk biomarkers used to monitor subacute ruminal acidosis (SARA) in dairy cows: fat-to-protein ratio (FPR) and the fatty acid profile (FAP). FPR is currently used to monitor SARA, while FAP is a newly developed test, currently in the pre-commercial phase, with reports of better accuracy than FPR. A stochastic decision tree model was used to estimate the expected monetary value of three levels of information with regards to SARA: (i) no monitoring, monitoring (ii) with FPR or (iii) with FAP. The VoI of FPR and FAP were calculated as the difference in expected monetary value of monitoring with FPR and FAP as compared with no monitoring, respectively. Several scenarios were modelled using sensitivity and elasticity analysis. The aim was not only to compensate for the scarcity of data for some variables, but also to identify under which conditions decisions based on FAP monitoring were indeed the best. In all the scenarios, monitoring SARA with FPR had the lowest expected monetary value. No monitoring was a better decision in 70% of the iterations in the scenario that described the most probable situation. The VoI of FAP was positive when SARA prevalence was between 0.21 and 0.79 with its maximum value at 0.61, when the treatment costs were lower than €116/case/year and when the disease costs were higher than €260/case/year. Moreover, an increase of specificity of the FAP to 0.95 yielded a positive VoI, whereas an increase of its sensitivity to 1.0 still yielded a negative VoI, suggesting that developers of the FAP should focus on improving its specificity rather than its sensitivity. To avoid suboptimal use of finite resources while developing MS, we recommend ex-ante investigation of the VoI of the MS under development.