ABSTRACT If the world population continues to increase exponentially, wealth and education inequalities might become more pronounced in the developing world. Thus, offering affordable, high-quality protein food to people will become more important and daunting than ever. Past and future challenges will increasingly demand quicker and more innovative and efficient solutions. Animal scientists around the globe currently face many challenging issues: from ensuring food security to prevent excess of nutrient intake by humans, from animal welfare to working with genetic-engineered animals, from carbon footprint to water footprint, and from improved animal nutrition to altering the rumen microbiome. Many of these issues are most likely to continue (or to exacerbate further) in the coming years, but animal scientists have many options to surmount the obstacles posed to the livestock industry through tools that are presently available. The frequency, interval, and intensity of livestock impacts, however, differ across regions, production systems, and among livestock species. These differences are such that the generalization of these issues is impossible and dangerous. For instance, when we discuss domesticated ruminant nutrition in the human food context, we look for the most efficient ruminant feeds that complement, rather than compete with, grains grown for direct human nutrition. Greater scrutiny and standardization are needed when developing and validating methodologies to assess short- and long-term impacts of livestock production. Failure in correctly quantifying these impacts may lead to disregard and disbelief by the livestock industry, increased public confusion, and the development of illusionary solutions that may amplify the impacts, thereby invalidating its original intent.

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.

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.

A mechanistic model that predicts nutrient requirements and biological values of feeds for sheep (Cornell Net Carbohydrate and Protein System; CNCPS-S) was expanded to include goats and the name was changed to the Small Ruminant Nutrition System (SRNS). The SRNS uses animal and environmental factors to predict metabolizable energy (ME) and protein, and Ca and P requirements. Requirements for goats in the SRNS are predicted based on the equations developed for CNCPS-S, modified to account for specific requirements of goats, including maintenance, lactation, and pregnancy requirements, and body reserves. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal fermentation rates, forage, concentrate and liquid passage rates, and microbial growth. The evaluation of the SRNS for sheep using published papers (19 treatment means) indicated no mean bias (MB; 1.1 g/100 g) and low root mean square prediction error (RMSPE; 3.6 g/100g) when predicting dietary organic matter digestibility for diets not deficient in ruminal nitrogen. The SRNS accurately predicted gains and losses of shrunk body weight (SBW) of adult sheep (15 treatment means; MB = 5.8 g/d and RMSPE = 30 g/d) when diets were not deficient in ruminal nitrogen. The SRNS for sheep had MB varying from -34 to 1 g/d and RSME varying from 37 to 56 g/d when predicting average daily gain (ADG) of growing lambs (42 treatment means). The evaluation of the SRNS for goats based on literature data showed accurate predictions for ADG of kids (31 treatment means; RMSEP = 32.5 g/d; r2= 0.85; concordance correlation coefficient, CCC, = 0.91), daily ME intake (21 treatment means; RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99), and energy balance (21 treatment means; RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats. In conclusion, the SRNS for sheep can accurately predict dietary organic matter digestibility, ADG of growing lambs and changes in SBW of mature sheep. The SRNS for goats is suitable for predicting ME intake and the energy balance of lactating and non-lactating adult goats and the ADG of kids of dairy, meat, and indigenous breeds. The SRNS model is available at http://nutritionmodels.tamu.edu.