Alberto Stanislao Atzori

Feeding strategies to design the fatty acid profile of sheep milk and cheese

The majority of sheep milk produced in the world is transformed into cheese. Feeding is a major factor affecting the quality of sheep milk and, therefore, of sheep cheese. Because fat is the main compound of cheese, this review gives an update on the effects of feeding and nutrition on milk fat content and deeply discusses feeding strategies aimed at increasing the levels of healthy fatty acids (FA), such as conjugated linoleic acid and omega-3 FA, in milk and cheese in the human diet. In addition, the use of alternative feed resources such as by-products, aromatic plants, and phenolic compounds in the sheep diet and their effects on milk and cheese FA composition are also discussed. Among feeding strategies, grazing and the use of supplements rich in oils seem to be the best and the cheapest strategies to improve the nutritional value of the fatty acid profile in sheep cheese.

Models for estimating feed intake in small ruminants

This review deals with the most relevant limits and developments of the modeling of intake of sheep and goats reared intensively and extensively. Because small ruminants are normally fed ad libitum, voluntary feed intake is crucial in feeding tactics and strategies aimed at optimal animal production. The effects of genetic, neuroendocrine, hormonal, feed and environmental factors on voluntary feed intake were discussed. Then, several mathematical models to estimate dry matter intake (DMI) were examined, with emphasis on empirical models for sheep and goats in intensive farm systems or in extensive areas under pasture or rangeland conditions. A sensitivity analysis of four models of prediction of DMI in housed lactating dairy sheep and meat sheep breeds was also presented. This work evidenced a large variability in the approaches used and in the variables considered for housed sheep and goats. Regarding the estimation of feed intake for grazing sheep and browsing goats, the accuracy of estimates based on empirical models developed so far is very low when applied out of the boundaries of the studied system. Feeding experiments indoors and outdoors remain fundamental for a better modeling and understanding of the interactions between feeds and small ruminants. However, there is a need for biological and theoretical frameworks in which these experiments should be carried out, so that appropriate empirical or mechanistic equations to predict DMI could be developed.

The Small Ruminant Nutrition System: development and evaluation of a goat submodel

Abstract The Small Ruminant Nutrition System (SRNS) is a computer model based on the structure of the Cornell Net Carbohydrate and Protein System for Sheep. A version of the SRNS for goats is under development and evaluation. In the SRNS for goats, energy and protein requirements are predicted based on the equations developed for the SRNS for sheep, modified to account for specific requirements of goats. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal degradation rates, on forage, concentrate and liquid passage rates, on microbial growth, and on physically effective fiber. The evaluation of the SRNS for goats based on literature data showed that the SRNS accurately predicted the ADG of kids (RMSEP = 32.5 g/d; r2 = 0.85; CCC = 0.91), and the daily MEI (RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99) and the energy balance (RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats.

A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability

The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21 farms had dRI<-1 SD, 32 farms were between -1 SD and 0, 67 farms were between 0 and +1 SD, and 15 farms had dRI>+1 SD. The top 10% of the farms had a dRI greater than 0.170 €/L, whereas the bottom 10% farms had a dRI lower than 0.116 €/L. This stochastic approach allowed us to understand the relationships among the inputs of the studied dairy farms and to develop a ranking index for comparison purposes. The developed methodology may be improved by using more inputs at the dairy farm level and considering the actual cost to measure profitability.

Development and evaluation of a model to predict sheep nutrient requirements and feed utilisation

Abstract A new feeding system for sheep, called MIPAF, was developed by integrating previously published equations with new ones to predict energy and protein requirements as well as feed utilization of sheep. Special emphasis was given to dairy sheep, whose specific needs are not considered by most sheep feeding systems, and to some of the environmental factors that affect requirements. Original equations were added to predict fluxes in body energy reserves from body weight (BW) and body condition score. The prediction of supply of nutrients was based on the discount system of Van Soest. Thus, the MIPAF system predicts feed value as a function of the specific feeding level of the sheep that receive the ration. The ability of the MIPAF model to predict BW variations was evaluated using data from six studies with adult sheep (13 treatments with lactating ewes and 15 with dry ewes or wethers). The model predicted the variations of BW in sheep with no bias, but with high rooted mean squared prediction error (RMSPE) (mean bias = -0.1 g/d; P > 0.1; RMSPE = 44.9 g/d; n = 28). Three extreme outliers were discarded because the treatment diets, made only of wheat straw and supplied to mature wethers, had very low CP concentrations (less than 3.25%, DM basis). After the outliers were removed, the prediction error improved but the mean bias became significantly different from zero (mean bias = -12.3 g/d; P < 0.05; RMSPE = 29.6 g/d; n = 25). Prediction accuracy was different between lactating and non lactating sheep. Variations of BW in lactating ewes were predicted with high accuracy (mean bias = 6.8 g/d; P > 0.1; RMSPE = 18.7 g/d; n = 13), while for dry ewes the model was less accurate, under predicting the variations in BW (mean bias = -33.0 g/d, P < 0.001; RMSPE = 38.1 g/d; n = 12). The evaluations included published experiments with sheep of diverse body sizes and physiological stages fed diverse diets at various levels of nutrition. This suggests that the MIPAF model can be used to evaluate diets and animal performance in a variety of production settings with good accuracy.

Estimation of nitrogen volatilisation in the bedded-pack of dairy cow barns.

Abstract The objective of the study was to measure N volatilisation (NVOL) from bedded-pack dairy cow barns. Feed intake, milk production, bedding material used and meteorological variables were monitored in 3 dairy farms near the town of Arborea (39°46’26” N, 08°34’53” E, 7 m a.s.l.) during one month for each season from summer 2007 to spring 2008. NVOL was measured in the interval between excretion and removal of bedded-packs from barn floors. NVOL was measured using the N (nitrogen) to P (phosphorus) ratio marker method, considering P as an internal non-volatile marker in milk and manure. Nitrogen and P excretion was estimated by nutrient balance as intake minus N and P excreted in milk. NVOL coefficient and bad-pack temperature were almost constant among seasons. The annual mean NVOL in bedded-pack barns was equal to 38.8% of excreted N. Accounting also for the NVOL that occurred in the concrete floor of the feeding and walking areas ofthe barns, NVOL was 40.7% of N excreted.

Testicular development in male lambs prenatally exposed to a high‐starch diet

Maternal nutrition during critical gestation periods impacts on offspring in later life; effects of high‐starch maternal diet on testicular development in lambs were addressed. Dairy ewes were fed diets providing either 27% (Starch, S) or 11% (Fiber, F) of starch from mating to lambing (∼147 days; S147, F147) or for the last 75 days of gestation (S75, F75). Testes of single male lambs were measured and then sampled for histological and gene expression analyses at selected ages. Testicular dimensions and weight were similar among groups, but the total area of seminiferous tubules increased with age and tended to be higher (p = 0.057) in lambs from starch‐ than fiber‐fed ewes. Sertoli and germ cells number increased with age, but was not influenced by maternal diet. Transcript abundances of androgen receptor (AR), insulin‐like growth factor 1 (IGF1), and hydroxysteroid (17‐beta) dehydrogenase 3 (HSD17B3) was similar between S147 and F147 lambs (p > 0.1). Abundance of luteinizing hormone/choriogonadotropin receptor (LHCGR) and steroidogenic acute regulatory protein (STAR) was higher in young vs older lambs, whereas insulin‐like growth factor 2 (IGF2) levels increased with age. The expression of vascular endothelial growth factor A (VEGFA), Anti‐Müllerian hormone (AMH), IGF1, follicle stimulating hormone receptor (FSHR), and insulin‐like growth factor 2 receptor (IGF2R) was not influenced by maternal diet or lamb age (p > 0.1). In conclusion, a high‐starch maternal diet did not influence gene expression, but may have affected testicular structure in infant offspring, as seen by an increase in the total area of seminiferous tubules.

Short communication: In vitro rumen gas production and starch degradation of starch-based feeds depend on mean particle size

Our objective was to model the effect of mean particle size (mPS) on in vitro rumen starch degradation (IVSD) and the kinetics of gas production for different starch-based feeds. For each feed, 2 batches of the same grains were separately processed through 2 different mills (cutter or rotor speed mills), with or without different screens to achieve a wide range of mPS (0.32 to 3.31 mm for corn meals; 0.19 to 2.81 mm for barley meals; 0.16 to 2.13 mm for wheat meals; 0.28 to 2.32 mm for oat meals; 0.21 to 2.36 mm for rye meals; 0.40 to 1.79 for sorghum meals; 0.26 to 4.71 mm for pea meals; and 0.25 to 4.53 mm for faba meals). The IVSD data and gas production kinetics, obtained by fitting to a single-pool exponential model, were analyzed using a completely randomized design, in which the main tested effect was mPS (n = 6 for all tested meals, except n = 7 for corn meals and n = 5 for sorghum meals). Rumen inocula were collected from 2 fistulated Holstein dairy cows that were fed a total mixed ration consisting of 16.2% crude protein, 28.5% starch, and 35.0% neutral detergent fiber on a dry matter basis. The IVSD, evaluated after 7 h of rumen incubation, decreased linearly with increasing mPS for corn, barley, wheat, rye, pea, and faba meals, and decreased quadratically with increasing mPS for the other meals. The y-axis intercept for 7-h IVSD was below 90% starch for corn, barley, and rye feeds and greater than 90% for the other tested feeds. The mPS adjustment factors for the rate of rumen starch degradation varied widely among the different tested feeds. We found a linear decrease in starch degradation with increasing mPS for barley, wheat, rye, and pea meals, whereas we noted a quadratic decrease in starch degradation for the other tested meals. Further, we observed a linear decrease in the rate of gas production with increasing mPS in each tested feed, except for pea meal, which had a quadratic relationship. For each 1 mm increase in mPS, the gas production was adjusted by -0.009 h-1 for corn, -0.011 h-1 for barley, -0.008 h-1 for wheat, and -0.006 h-1 for faba, whereas numerically greater adjustments were needed for oat (-0.022 h-1), rye (-0.017 h-1), and sorghum (-0.014 h-1). These mPS adjustment factors could be used to modify the starch-based feed energy values as a function of mean particle size, although in vivo validation is required.

Multi-Omic Biogeography of the Gastrointestinal Microbiota of a Pre-Weaned Lamb

The digestive functions of the pre-weaned lamb gastrointestinal tracts (GITs) have been the subject of much research in recent years, but the microbial and host functions underlying these complex processes remain largely unknown. Here, we undertook a proof-of-principle metaproteogenomic investigation on luminal and mucosal samples collected from 10 GITs of a 30-day-old pre-weaned lamb. We demonstrate that the analysis of the diverse ecological niches along the GITs can reveal microbiota composition and metabolic functions, although low amounts of microbial proteins could be identified in the small intestinal and mucosal samples. Our data suggest that a 30-day lamb has already developed mature microbial functions in the forestomachs, while the effect of the milky diet appears to be more evident in the remaining GITs. We also report the distribution and the relative abundance of the host functions, active at the GIT level, with a special focus on those involved in digestive processes. In conclusion, this pilot study supports the suitability of a metaproteogenomic approach to the characterization of microbial and host functions of the lamb GITs, opening the way to further studies aimed at investigating the impact of early dietary interventions on the GIT microbiota of small ruminants.

The development and evaluation of the Small Ruminant Nutrition System

A mechanistic model that predicts nutrient requirements and biological values of feeds for sheep and goats (Small Ruminant Nutrition System, SRNS) was developed based on the Cornell Net Carbohydrate and Protein System for sheep. The SRNS uses animal and environmental factors to predict metabolisable energy (ME) and protein requirements. This model has been subjected to an extensive evaluation. In particular, evaluation of the SRNS for sheep using published papers indicated good accuracy and precision in the prediction of organic matter and CP digestibility, while NDF digestibility was underpredicted. In addition, the SRNS accurately predicted daily ME intake (mean bias (MB) = 0.04 Mcal/d; root mean square error of prediction (RMSEP) = 0.24 Mcal/d; r2 = 0.99) of lactating goats and goat wethers. The SRNS also accurately predicted the ADG of lambs (n = 42; MB = 1 g/d; RMSEP = 37 g/d; r2= 0.84) and kids (n = 31; MB = −6.4 g/d; RMSEP = 32.5 g/d; r2= 0.85) and the gains and losses of shrunk body weight of adult sheep (MB = −5.8 g/d; RMSEP = 30 g/d; r2= 0.73) and the energy balance (n = 21; RMSEP = 0.20 Mcal/d; r2 = 0.87) of lactating goats and wethers. In conclusion, based on our accumulated evaluation of the SRNS with literature data, the SRNS accurately predicts nutrient supply and requirements of sheep and goats. Recent unpublished evaluations, however, suggested that the SRNS may underpredict ADG when compensatory growth occurs.