Corn sialge (Zea mays L.) is the most widely used in the world in dairy cattle diets because of its higher biomass yields, palatability, homogeneous quality at harvest, and ease of silage due to its higher soluble sugar content. Studies were systematically searched to know the silage corn quality and yield from different countries concerning dry matter yield (t/ha), population density (plant density/ha), quantity of Dry Matter (DM), Crude Protein (CP), Neutral Detergent Fiber (NDF), Non-Fibrous Carbohydrates (NFC), Organic Matter (OM), DM Digestibility (DMD), Neutral Detergent Fiber Digestibility (NDFD), and milk production (kg) per hectare (kg milk/ha) using MILK2006®. Six clusters (CL, PROC CLUSTER) of corn silage were analyzed: CL1: “Starch,” which included DMD, DNDF, TDN1 x DM, Mega calories per kg of DM, and kg of milk/t DM; CL2: “ether extract” which included TDN1 x DM, Mcal/kg DM, and kg of milk/t DM; CL3: “DM,” composed only of DNDF; CL4: “Plant Density,” including Yield of DM/ha and Milk Production/ha; CL5: “NDF,” and CL6: “CP.” In conclusion, CL1 is characterized by a higher DMD, NDFD, CNF, and starch, which allow higher TDN1 x DM and an energy concentration (Mcal/kg DM) with higher milk production (kg milk/ha). CL2 with a higher ether extract (EE) allows higher TDN1 x DM and an energy concentration (Mcal/kg DM) having a higher milk production (kg milk/ha). The DM concentration increases in the whole plant because of maturity, affecting the starch content and NDFD; the DM content (> 35 g/100 g DM) mainly causes a decrease in the NDFD, while in a very early stage of DM harvest (< 25 g/100 g DM) a low ratio of starch:NDFD is present.

This study was conducted to estimate the effects of sire breed (Charollais, Dorper, Dorset, Hampshire, and Suffolk) and genetic group of the dam [local whiteface (Criolla), grade Hampshire, grade Suffolk, F₁ Dorper × Pelibuey, Australian composite, hair sheep (Barbados Blackbelly or Pelibuey), Dorset, Hampshire and Suffolk] on birth weight, survival rate from birth to weaning, and weaning weight adjusted to 65 d of age. Data were recorded from 2004 to 2006 on crossbreed lambs obtained by artificial insemination from 114 flocks in Central Mexico. High (above the mean) or low (below the mean) environmental categories were assigned to each record from flock–year–season effects solutions obtained in fixed-effects linear model analyses. Birth weights of lambs sired by Charollais rams (3.94 kg) were heavier (P < 0.05) than those sired by Suffolk (3.69 kg) whereas Dorper-, Dorset-, and Hampshire-sired lambs were intermediate. For weaning weight, lambs sired by Charollais were the heaviest (19.16 kg) compared with lambs sired by Hampshire (17.86 kg), Suffolk (17.79 kg), and Dorper (17.28 kg) whereas Dorset sired were the lightest (16.77 kg; P < 0.05). Lambs sired by Dorset rams had a lower survival rate (77.1%) than lambs sired by Charollais, Dorper, Hampshire, or Suffolk (81.6 to 83.3%; P < 0.05). Lambs from Australian composite, Dorset, Hampshire, and Suffolk dams were heavier for birth and weaning compared with lambs from local whiteface (Criolla), hair sheep breeds (Barbados Blackbelly or Pelibuey), or crosses (F₁ Pelibuey × Dorper) dams (P < 0.05) whereas lambs from grade Hampshire and grade Suffolk were intermediate. The genetic group of the dam had no effect (P > 0.05) on survival rate. Sire breed × environmental category interaction effect was significant for birth weight (P < 0.01) and weaning weight (P < 0.05), but relatively small changes on the ranking of sire breeds were observed between environmental categories for weaning weight. Genetic group of the dam × environmental category interaction effect was significant for birth and weaning weights (P < 0.01). Weaning weight of lambs from hair sheep breeds (Barbados Blackbelly or Pelibuey) and crosses (F₁ Pelibuey × Dorper) as well as purebred Dorset, Hampshire, and Suffolk dams were more affected when changing from the high to the low environmental category compared with the other genetic groups. No breed of the sire or genetic group of the dam × environmental category interactions were (P > 0.05) observed for survival rate. Copyright © 2012. American Society of Animal Science .

The objective of the present study was to evaluate the effect of the grade of crossbreeding (Lacaune x Manchega) and environmental factors on milk production in a commercial flock in Spain. A total of 5769 milk production records of sheep with different degrees of purity of the Lacaune breed crossed with Manchega were used as follows: 100% Lacaune (n = 2960), 7/8 Lacaune (n = 502), 13/16 Lacaune (n = 306), 3/4 (n = 1288), 5/8 Lacaune (n = 441) and 1/2 Lacaune: Manchega (n = 272). Additional available information included the number of parity (1 to 8), litter size (single or multiple), and the season of the year of lambing (spring, summer, autumn and winter). A mixed model was used to evaluate the level of crossbreeding and environmental factors on milk production. The 100% Lacaune sheep presented the highest milk production with respect to the F1 Lacaune x Manchega sheep (p < 0.01), showing that as the degree of gene absorption increases with the Manchega breed, it presents lower milk yield. The 100%, 13/16, and 3/4 Lacaune genotypes had the highest milk yields with respect to the 1/2 Lacaune/Manchega breed (p < 0.001). The Lacaune registered on average 181.1 L in a period adjusted to 160 days of lactation (1.13 L/ day). Likewise, the parity number, litter size, and season of lambing effects showed significant differences (p < 0.01). It was concluded that 13/16 and 3/4 Lacaune/Manchega ewes presented the highest milk yields with respect to the other crosses.

This meta-analysis aims to identify if there are differences between the weight of lamb carcases and their main characteristics according to the production systems and type of feeding around the word. Lambs finished on feeding supplement (with or without supplement) and production system (feed-lot or free-range) were analysed. Data from 21 controlled studies were used, the variables of interest in the meta-analysis include: initial body weight (IBW, kg), final body weight (FBW, kg), hot carcase weight (HCW, kg), cold carcase weight (CCW, kg), area of longissimus dorsi (ALd, cm²), subcutaneous dorsal fat thickness (SFT, mm) and slaughter weight (kg). The high heterogeneity (>98%) found in this study may be due to many factors (breed, climate, age and management). Furthermore, the number of animals allocated for each treatment and the lack of repetitions make it difficult to correctly understand the effect of feeding and production system on the lamb meat. Lambs finished in a feedlot or with supplementation under extensive systems exhibit faster growth rates, achieve target weights quicker, and produce heavier carcase weights when compared to grazing lambs.

It is necessary to study small-ruminant production systems in a spatial context, as different areas, depending on their natural, socioeconomic, and cultural context, will respond differently to the effects of such causal factors. The aim of this study was to analyze the relationships between technology use and farm and farmer characteristics in dairy sheep production by developing a farm typology using multivariate statistical analysis and comparing it with the multi-scale sustainability evaluation of the Natural Resource Management Systems (NRMS) method. Quantitative and qualitative data were evaluated and compared using the NRMS method and cluster analysis to identify groups of farms. Fourteen variables were considered for the analysis, resulting in four farm systems with the NRMS method, named Intensive, Semi-Intensive, Semi-Extensive, and Extensive; however, when analyzing the data by cluster analysis, the following clusters were used: Cluster 1, Semi-Extensive Dairy Systems (SEDSs); Cluster 2, Semi-Intensive Systems (SIDSs); Cluster 3, Semi-Extensive Cheese Systems (SECSs); and Cluster 4, Intensive Dairy Systems (IDSs). This study concludes that the traditional classification of dairy sheep farms based on their dependence on natural resources can be enriched by incorporating additional indicators that allow for a more comprehensive and accurate characterization of farms.