Abstract: A retrospective study on seasonal variations in the characteristics of cow’s milk and temperature-humidity index (THI) relationship was conducted on bulk milk data collected from 2003 to 2009. The THI relationship study was carried out on 508 613 bulk milk data items recorded in 3328 dairy farms form the Lombardy region, Italy. Temperature and relative humidity data from 40 weather stations were used to calculate THI. Milk characteristics data referred to somatic cell count (SCC), total bacterial count (TBC), fat percentage (FA%) and protein percentage (PR%). Annual, seasonal and monthly variations in milk composition were evaluated on 656 064 data items recorded in 3727 dairy farms. The model highlighted a significant association between the year, season and month, and the parameters analysed (SCC, TBC, FA%, PR%). The summer season emerged as the most critical season. Of the summer months, July presented the most critical conditions for TBC, FA% and PR%, (52 054 ± 183 655, 3.73% ± 0.35% and 3.30% ± 0.15%, respectively), and August presented higher values of SCC (369 503 ± 228 377). Each milk record was linked to THI data calculated at the nearest weather station. The analysis demonstrated a positive correlation between THI and SCC and TBC, and indicated a significant change in the slope at 57.3 and 72.8 maximum THI, respectively. The model demonstrated a negative correlation between THI and FA% and PR% and provided breakpoints in the pattern at 50.2 and 65.2 maximum THI, respectively. The results of this study indicate the presence of critical climatic thresholds for bulk tank milk composition in dairy cows. Such indications could facilitate the adoption of heat management strategies, which may ensure the health and production of dairy cows and limit related economic losses.

Abstract: A model of a global food system need at least two points in time per year and two locations with different growing seasons so that planting and harvesting have different timing across locations. Moreover, planting decisions reflect soil states affected by stochastic weather since previous point in time, while harvest reflect the planting decisions and the stochastic weather through the growing season up to next point. Decisions on trade, storage and consumption are taken at every point in time. Despite stochastic influence, deterministic stationary general equilibrium is applicable. The world then runs in circles through a likely sequence of N given weather scenarios, while the decision-makers do not know the next scenario. The model will provide a setting in which the consequences of climate change can be assessed both with respect to expectations and variances. It will by construction be an integrated assessment model (IAM) in the sense that outcomes follow from agent choices in a world of biophysical processes. In this case the biophysical world is stochastic. At the prototype stage neither existing behavioral nor bio-physical models will be applied.