Abstract :
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.
Keywords :
livestock farming system , Bayesian Model Averaging , Model selection , Sensitivity , Specificity
