Outfield grazing as a way of utilizing and sustaining semi-natural ecosystems

Background/Question/Methods

In Norway the sheep farming industry counts more than 2 million animals. In recent years the number of animals released to outfield pastures during summer, being mountains, forests or smaller oceanic islands, has decreased, despite political pressure to sustain outfield grazing. Most Norwegian sheep graze in mountainous areas where it has been shown that their stocking rates affect the vegetation and indeed the growth of the animal themselves. The performance of animals roaming freely in the mountains is obviously affected by climate conditions. It is therefore of utmost importance to understand the relationships between climatic conditions, at both local and regional scales, vegetation phenology and the growth of the animals if the aim is to sustain meat production on outfield pastures. To model these relationships and estimate the effects of yearly variations in weather conditions we have used a hierarchical model within a Bayesian framework for inference.

Results/Conclusions

We found that climatic conditions the previous winter (snow depth) and in spring (temperature and precipitation before the sheep were released to the mountains) significantly explained variations in lamb autumn body weight. Most of the effect operated through the spring phenology of the vegetation, measured as satellite derived NDVI. Not surprisingly precipitation and temperature during the summer months also affected autumn weights. However, the effects of weather variables varied, not only in strength but also in direction even over relatively short distances within Southern Norway. In addition we found that the North Atlantic Oscillation index (NAO) in many cases outperformed local weather variables as explanatory variable and that average monthly temperature was a better measure than growing degree days, despite its weaker connection to the biological process.

This analytical framework will, in future projects, be used to analyze data with higher spatial and temporal resolution, with respect to the growth curves of the animals, the vegetation phenology and local weather variables.