RESUMO
Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.
RESUMO
Reactivity of cattle affects many aspects of animal production (e.g. reduced milk and meat production). Animals have individual differences in temperament and emotional reactivity, and these differences can affect how animals react to stressful and fear-eliciting events. Heart rate variability (HRV) is a good indicator of stress and balance of the autonomous nervous system, and low parasympathetic activity is connected with higher emotional reactivity. The study had two specific aims: (1) to compare HRV in dairy cows for standing and lying postures (no earlier results available), and (2) to assess whether dairy cows' emotional reactivity is connected to their HRV values. Eighteen dairy cows were subjected twice to a handling test (HT): morning (HT1) and afternoon (HT2), to evaluate emotional reactivity (avoidance score, AS). HRV was measured during HT (standing). HRV baseline values, both standing and lying down, were measured one week before HTs. HRV was analyzed with time and frequency domain analyses and with the Recurrence Quantification Analysis (RQA). Heart rate (HR), low-frequency/high-frequency band ratio (LH/HF), % determinism (%DET) and longest diagonal line segment in the recurrence plot (Lmax) were higher (p<0.05) while the cows were standing than when lying down, whereas the root mean square of successive R-R intervals (RMSSD) (p<0.05) and power of the high-frequency band (HF) (p<0.1) were higher while the animals were lying down. HR, the standard deviation of all interbeat intervals (SDNN), RMSSD, HF, power of the low-frequency band (LF), % recurrence (%REC), %DET, Shannon entropy (p<0.05), and HF (p<0.1) were higher during the handling test compared to standing baseline values. AS (i.e. tendency to avoid handling) correlated positively with SDNN (r=0.48, p<0.05), RMSSD (r=0.54, p<0.05), HF, RMSSD (r=0.46, p<0.1) and LF (r=0.57, p<0.05), and negatively with %DET (r=-0.53, p<0.05), entropy (r=-0.60, p<0.05) and Lmax (r=-0.55, p<0.05) in the baseline HRV measurements. AS correlated positively with SDNN (r=0.43, p<0.1) and HF (r=0.53, p<0.05) during HT. Some HRV parameters (HR, LF, %REC, %DET) indicated that the handling test may have caused stress to the experimental cows, although some HRV results (SDNN, RMSSD, HF, entropy) were controversial. The correlations between HRV variables and AS suggest that the emotional reactivity of the cow can be assessed from the baseline values of the HRV. It is debatable, however, whether the handling test used in the present study was a good method of causing mild stress in dairy cattle, since it may have even induced a positive emotional state. The posture of the cow affected HRV values as expected (based on results from other species), so that while standing a shift towards more sympathetic dominance was evident. Our results support the idea that linear (time and frequency domain) and non-linear (RQA) methods measuring HRV complement each other, but further research is needed for better understanding of the connection between temperament and HRV.
