This study was aimed to evaluate the degree of thermal stress exhibited by Holsteins under a continental temperate climate. Milk, fat, protein, and somatic cell count test-day records collected between 2000 and 2011 from 23,963 cows in 604 herds were combined with meteorological data from 14 public weather stations in Luxembourg. Daily values of 6 different thermal indices (TI) weighted in term of temperature, relative humidity, solar radiation, and wind speed were calculated by averaging hourly TI over 24h. Heat stress thresholds were first identified by a broken-line regression model. Regression models were thereafter applied to quantify milk production losses due to heat stress. The tipping points at which milk and protein yields declined were effectively identified. For fat yield, no valid threshold was identified for any of the studied TI. Daily fat yields tended to decrease steadily with increasing values of TI. Daily somatic cell score patterns were marked by increased values at both lowest and highest TI ranges, with a more pronounced reaction to cold stress for apparent temperature indices. Thresholds differed between TI and traits. For production traits, they ranged from 62 (TI(1)) to 80 (TI(3)) for temperature-humidity indices (THI) and from 16 (TI(5)) to 20 (TI(6)) for apparent temperature indices. Corresponding somatic cell score thresholds were higher and ranged from 66 (TI(1)) to 82 (TI(3)) and from 20 (TI(5)) to 23 (TI(6)), respectively. The largest milk decline per unit of mild, moderate, and extreme heat stress levels of 0.164, 0.356, and 0.955 kg, respectively, was observed when using the conventional THI (TI(1)). The highest yearly milk, fat, and protein losses of 54, 5.7, and 4.2 kg, respectively, were detected by TI(2), the THI index that is adjusted for wind speed and solar radiation. The latter index could be considered as the best indicator of heat stress to be used for forecast and herd management in a first step in temperate regions under anticipated climate changes.

The use of breeding programs for the Pacific white shrimp (Penaeus (Litopenaeus) vannamei) based on mixed linear models with pedigreed data are described. The application of these classic breeding methods yielded continuous progress of great value to increase the profitability of the shrimp industry in several countries. Recent advances in such areas as genomics in shrimp will allow for the development of new breeding programs in the near future that will increase genetic progress. In particular, these novel techniques may help increase disease resistance to specific emerging diseases, which is today a very important component of shrimp breeding programs. Thanks to increased selection accuracy, simulated genetic advance using genomic selection for survival to a disease challenge was up to 2.6 times that of phenotypic sib selection.

Heritabilities and correlations for milk yield (MY), fat yield (FY), protein yield (PY), combined fat and protein yield (FPY), fat percentage (F%), protein percentage (P%), age at first kidding (AFK), interval between the first and second kidding (KI), and real and functional productive life at 72mo (FPL72) of 33,725 US dairy goats, were estimated using animal models. Productive life was defined as the total days in production until 72mo of age (PL72) for goats having the opportunity to express the trait. Functional productive life was obtained by correcting PL72 for MY, FY, PY, and final type score (FS). Six selection indexes were used, including or excluding PL72, with 6 groups of different economic weights, to estimate the responses to selection considering MY, FY, PY, and PL72 as selection criteria. The main criteria that determined the culling of a goat from the herd were low FS, MY, and FY per lactation. Heritability estimates were 0.22, 0.17, 0.37, 0.37, 0.38, 0.39, 0.54, 0.64, 0.09, and 0.16 for PL72, FPL72, MY, FY, PY, FPY, F%, P%, KI, and AFK, respectively. Most genetic correlations between the evaluated traits and PL72 or FPL72 were positive, except for F% (-0.04 and -0.06, respectively), P% (-0.002 and -0.03, respectively), and AFK (-0.03 and -0.01, respectively). The highest genetic correlations were between FPL72 and MY (0.39) and between PL72 and MY (0.33). Most phenotypic correlations between the traits evaluated and FPL72 and PL72 were positive (>0.23 and >0.26, respectively), except for F% (-0.004 and -0.02, respectively), P% (-0.05 and -0.02), KI (-0.01 and -0.07), and AFK (-0.08 and -0.08). The direct selection for PL72 increased it by 102.28d per generation. The use of MY, FY, PY, KI, or AFK as selection criteria increased PL72 by 39.21, 27.33, 35.90, -8.28, or 2.77d per generation, respectively. The inclusion of PL72 as selection criterion increased the expected response per generation from 0.15 to 17.35% in all selection indices studied.