Abstract: The work described herein was based on construction and query of four different large databases which included multiannual (5-7 years) meteorological, productive and health data from the field. Productive data were referred to dairy cows and included milk yield and composition (total bacterial count, fat and protein percentages) whereas health data were relative both to dairy cows (milk somatic cell counts and mortality data) and pigs (mortality data during transport and at lairage). The analysis pointed out significant seasonal variations of parameters under study. In synthesis, summer/hot season was associated with significant worsening of cows’ milk composition and with significant higher risk of death in pigs. The analysis also permitted to establish the themperature humidity index values above which a significant decline of performance and health of dairy cows or pigs has to expected. These results may help to predict the consequences of climate change in economically important sectors of the livestock industry, to identify and target adaptation options that are appropriate for specific contexts and that can contribute to environmental sustainability as well as to economic development. No Label

Abstract: The work carried out under LiveM, L1.2 and described herein was based on construction and query of large databases which included multiannual productive and health field data. Productive data referred to dairy cows and included milk yield and composition, whereas health data were relative both to dairy cows and pigs. The analysis established the THI values above which a significant decline in the performance and health of dairy cows or pigs is to be expected. These results may help to adopt management environmental strategies which may permit to limit THI increase under farming conditions and/or to provide animals with interventions which may reduce heat load and/or increase dissipation of heat. No Label

Abstract: The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.