According to the United Nations (UN, 2017), the world population increased by approximately 1 billion inhabitants during the last 12 years, reaching nearly 7.6 billion in 2017. Although this growth is slower than 10 years ago (1.24% vs. 1.10% per year), with an average increase of 83 million people annually, global population will reach about 8.6 billion in 2030 and 9.8 billion in 2050. Population growth, urbanization, and income rise in developing countries are the main driver of the increased demand for livestock products (UN, 2017). The livestock sector requires a significant amount of natural resources and is responsible for about 14.5% of total anthropogenic greenhouse gas emissions (7.1 Gigatonnes of carbon dioxide equivalents for the year 2005; Gerber et al., 2013). Mitigation strategies aimed at reducing emissions of this sector are needed to limit the environmental burden from food production while ensuring a sufficient supply of food for a growing world population. The objectives of this manuscript are to 1) discuss the main greenhouse gas emissions sources from the livestock sector and 2) summarize the best mitigation strategies.

The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives.

The 2 studies described investigated seasonal variations of mortality and temperature-humidity index (THI)-mortality relationships in dairy cows. Mortality data were extracted from the Italian Bovine Spongiform Encephalopathy databases, which contain records on cows older than 24 mo that died on a farm from all causes (98% of total records), were slaughtered in an emergency state, or were sent for normal slaughter but were sick in the preslaughter inspection (2% of total records). Both studies evaluated mortality data during a 6-yr period (2002 to 2007). The seasonal pattern study was conducted throughout Italy and was based on 320,120 deaths. An association between season and deaths was found for all 6 yr. Summer and spring were the seasons with the highest and lowest frequency of deaths (15,773.3 +/- 2,861 and 11,619.3 +/- 792.3), respectively, and within summer months, the number of deaths in July and August (5,435 +/- 284 and 5,756 +/- 676.2, respectively) was higher than in June (4,839 +/- 344.8). The THI-mortality relationships study was carried out only for deaths (51,240) reported for the Lombardia and Emilia Romagna regions. For this study, the mortality databases were integrated with THI data, which were calculated by using data from 73 weather stations. Each farm where deaths were recorded was assigned the THI values (maximum and minimum) calculated at the closest weather station for each day the events (deaths) were reported. Analysis of data indicated that approximate THI values of 80 and 70 were the maximum and minimum THI, respectively, above which the number of deaths in dairy farms starts to increase. Maximum and minimum THI values of 87 and 77 were the upper critical THI above which the risk of death for dairy cows becomes maximum. This study defined quantitative relationships between mortality risk and THI in dairy cows and may help to provide emergency interventions and mitigation measures, which may ensure survival of dairy cows and reduce replacement costs associated with heat stress-related mortality.

Milk characteristics are affected by heat stress, but very little information is available on changes of milk protein fractions and their relationship with cheesemaking properties of milk. The main objective of the study was to evaluate the effect of hot season on milk protein fractions and cheesemaking properties of milk for Grana Padano cheese production. The study was carried out in a dairy farm with a cheese factory for transforming the milk to Grana Padano cheese. The study was carried out from June 2012 to May 2013. Temperature and relative humidity of the inside barn were recorded daily during the study period using 8 electronic data loggers programmed to record every 30 min. Constant managerial conditions were maintained during the experimental periods. During the experimental period, feed and diet characteristics, milk yield, and milk characteristics were recorded in summer (from June 29 to July 27, 2012), winter (from January 25 to March 8, 2013), and spring (from May 17 to May 31, 2013). Milk yield was recorded and individual milk samples were taken from 25 cows selected in each season during the p.m. milking. Content of fat, proteins, caseins (CN), lactose and somatic cell count (SCC), titratable acidity, and milk rennet coagulation properties were determined on fresh samples. Milk protein fraction concentrations were determined by the sodium dodecyl sulfate-PAGE. Data were tested for nonnormality by the Shapiro-Wilk test. In case of nonnormality, parameters were normalized by log or exponential transformation. The data were analyzed with repeated measures ANOVA using a mixed model procedure. For all the main milk components (fat, protein, total solids, and solids-not-fat), the lowest values were observed in the summer and the greatest values were observed in the winter. Casein fractions, with the exception of γ-CN, showed the lowest values in the summer and the greatest values in the winter. The content of IgG and serum albumin was greater in summer than in the winter and spring. A mild effect of season was observed for milk SCC, with greater values in summer than in the winter and spring. A worsening of milk coagulation properties was observed in summer season. The alteration of cheesemaking properties during hot season seems strictly linked with changes of milk protein fractions mainly with the decrease of αS-CN and β-CN and the increase of undefined proteins.

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.

This study investigated the mortality of dairy cows during heat waves. Mortality data (46,610 cases) referred to dairy cows older than 24mo that died on a farm from all causes from May 1 to September 30 during a 6-yr period (2002-2007). Weather data were obtained from 12 weather stations located in different areas of Italy. Heat waves were defined for each weather station as a period of at least 3 consecutive days, from May 1 to September 30 (2002-2007), when the daily maximum temperature exceeded the 90th percentile of the reference distribution (1971-2000). Summer days were classified as days in heat wave (HW) or not in heat wave (nHW). Days in HW were numbered to evaluate the relationship between mortality and length of the wave. Finally, the first 3 nHW days after the end of a heat wave were also considered to account for potential prolonged effects. The mortality risk was evaluated using a case-crossover design. A conditional logistic regression model was used to calculate odds ratio and 95% confidence interval for mortality recorded in HW compared with that recorded in nHW days pooled and stratified by duration of exposure, age of cows, and month of occurrence. Dairy cows mortality was greater during HW compared with nHW days. Furthermore, compared with nHW days, the risk of mortality continued to be higher during the 3 d after the end of HW. Mortality increased with the length of the HW. Considering deaths stratified by age, cows up to 28mo were not affected by HW, whereas all the other age categories of older cows (29-60, 61-96, and >96mo) showed a greater mortality when exposed to HW. The risk of death during HW was higher in early summer months. In particular, the highest risk of mortality was observed during June HW. Present results strongly support the implementation of adaptation strategies which may limit heat stress-related impairment of animal welfare and economic losses in dairy cow farm during HW.