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Farmers have been slow to adopt decision support system (DSS) models and their outputs, mainly owing to (i) the complexity of the data involved, which most potential users are unable to collect and process; and (ii) inability to integrate these models into real representations of their informational environments. This situation raises questions about the way farm management researchers have modelled information and information management, and especially about the quality of the information assessed by the farmers. We consider that to review advisory procedures we need to understand how farmers select and use farm management-related information, rather than focusing on decisions made in particular situations. The aim of this study was to build a conceptual model of the farmer-targeted farm management-related information system. This model was developed using data collected in commercial beef cattle farms. The design structure and operational procedures are based on (i) data categories representing the diversity of the informational activity; and (ii) selected criteria for supporting decisions. The model is composed of two subsystems, each composed of two units. First, an organizational subsystem organizes, finalizes and monitors informational activity. Second, a processing subsystem builds and exploits the informational resources. This conceptual model makes it possible to describe and understand the diverse range of farmers’ informational activity by taking into account both the flow of information and the way farmers make sense of that information. This model could serve as a component of biodecisional DSS models for assigning information in the decision-making process. The next task will be to take into account the broad range of farmers’ perceptions of the management situations in DSS models.
Reproductive performance has recently been a growing concern in cattle dairy systems, but few research methodologies are available to address it as a complex problem in a livestock farming system. The aim of this paper is to propose a methodology that combines both systemic and analytical approaches in order to better understand and improve reproductive performance in a cattle dairy system. The first phase of our methodology consists in a systemic approach to build the terms of the problem. It results in formalising a set of potential risk factors relevant for the particular system under consideration. The second phase is based on an analytical approach that involves both analysing the shapes of the individual lactation curves and carrying out logistic regression procedures to study the links between reproductive performance and the previously identified potential risk factors. It makes it possible to formulate hypotheses about the biotechnical phenomena underpinning reproductive performance. The last phase is another systemic approach that aims at suggesting new practices to improve the situation. It pays particular attention to the consistency of those suggestions with the farmer’s general objectives. This methodology was applied to a French system experiment based on an organic low-input grazing system. It finally suggested to slightly modify the dates of the breeding period so as to improve reproductive performance. The formulated hypotheses leading to this suggestion involved both the breed (Holstein or Montbéliarde cows), the parity, the year and the calving date with regard to the turnout date as the identified risk factors of impaired performance. Possible use of such a methodology in any commercial farm encountering a biotechnical problem is discussed.
In response to environmental threats, numerous indicators have been developed to assess the impact of livestock farming systems on the environment. Some of them, notably those based on management practices have been reported to have low accuracy. This paper reports the results of a study aimed at assessing whether accuracy can be increased at a reasonable cost by mixing individual indicators into models. We focused on proxy indicators representing an alternative to the direct impact measurement on two grassland bird species, the lapwing Vanellus vanellus and the redshank Tringa totanus. Models were developed using stepwise selection procedures or Bayesian model averaging (BMA). Sensitivity, specificity, and probability of correctly ranking fields (area under the curve, AUC) were estimated for each individual indicator or model from observational data measured on 252 grazed plots during 2 years. The cost of implementation of each model was computed as a function of the number and types of input variables. Among all management indicators, 50% had an AUC lower than or equal to 0.50 and thus were not better than a random decision. Independently of the statistical procedure, models combining management indicators were always more accurate than individual indicators for lapwings only. In redshanks, models based either on BMA or some selection procedures were non-informative. Higher accuracy could be reached, for both species, with model mixing management and habitat indicators. However, this increase in accuracy was also associated with an increase in model cost. Models derived by BMA were more expensive and slightly less accurate than those derived with selection procedures. Analysing trade-offs between accuracy and cost of indicators opens promising application perspectives as time consuming and expensive indicators are likely to be of low practical utility.
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