How can nutrition models increase the production efficiency of sheep and goat operations?

Improvement of nutrition efficiency in sheep and goats is more challenging than for other species. Because of their nutritional and environmental adaptability, sheep and goats are reared in very diverse farming (from extensive to highly intensive) and feeding (from grazing and browsing to total mixed diets) systems, in wide-ranging geographical areas and by using different breeds, populations, and crosses. In the last decades, nutrition models for sheep and goats have greatly evolved, from very simplistic and empirical approaches to more comprehensive and dynamic models, and are able to account for many more variables than in the past. Further improvements in the nutritional efficiency of sheep and goats can be obtained with the integration of mechanistic nutrition models and the data derived from sensor technology, especially those that allow monitoring of the movement and environmental effects on grazing and browsing sheep and goats. Large data sets made available by sensory technology can be interpreted with artificial intelligence tools and machine learning techniques. However, they were designed to learn from data and provide forecasting, but not as a tool to help us understand the underlying mechanisms. Sheep and goat production is an important economic resource in many countries around the world. The industry produces wool (2 million tons/yr), meat (14 million tons/yr, 21% of cattle meat production), and milk (28 million tons/yr, 4.4% of cow milk production). The total world number of small ruminants is growing quickly. As shown in Figure 1, the world population of goats (1.05 billion in 2017) increased by 49% in the last 20 yr, whereas that of sheep (1.20 billion in 2017) and cattle (1.49 billion in 2017) increased more slowly (+15% and +14%, for sheep and cattle, respectively) (FAOSTAT, 2019. http://www.fao.org/faostat/en/#home). Sheep and goats are raised in a wide range of farming (from extensive to highly intensive) and feeding (from grazing diets to total mixed rations) systems, geographical areas, and by using diverse breeds, populations, and crosses, due to their high nutritional and environmental adaptability. The world population of cattle (green circles), sheep (orange squares), goats (golden triangles) as head numbers (A) and relative percentage (B) from 1961 to 2017. The solid lines after 2017 represent 8-yr forecasts for each species population, and the shaded areas represent their 80% (darker) and 95% (lighter) prediction intervals. Adapted with permission from Tedeschi and Fox (2018). For this reason, the production efficiency of sheep and goats is much more variable and difficult to predict and pursue than for cattle. Although wool production is usually based on extensive systems and does not require large daily amounts of nutrients per animal, meat production and, to a greater extent, milk production require more nutrients per production unit (fattening lamb and milking ewe). Adequate nutrition requires proper feeding techniques and ration balancing. These, in turn, require estimation of animal nutrient requirements, of feed intake, and of the nutritive value of feed, which accounts for the specific nutritional features of small ruminants and the many management and environmental factors that affect their performances and efficiency. Recommendations for feeding sheep and goats are often derived from work on cattle, whose nutrition and feeding management have been studied more extensively. Even though sheep, goats, and cattle are all ruminants and have many similarities, they have different feeding strategies and are also different in some physiological functions (e.g., wool growth for sheep). Some of the most important differences between small ruminants (sheep and goats) and cattle (or buffaloes) are related to their body size, where small ruminants are 10 to 12 times smaller than cattle. The wet fermentation contents of the reticulorumen increase in direct proportion to body weight (BW). However, there is a lower proportional increase in energy requirements, due to their allometric scaling as a function of metabolic BW (BW0.75). Thus, per kilogram of BW, small ruminants have higher maintenance requirements and a lower ratio between reticulorumen volume and energy requirements than cattle, i.e., they have less rumen volume available per unit (e.g., Mcal of net energy) of requirements. As summarized by Cannas (2004b), due to these differences, sheep and goats compared with cattle: 1) have to eat more as a percentage of BW to satisfy their maintenance requirements, which results in a higher passage rate of feed and lower feed digestibility (especially fiber). Despite this, the total amount of nutrients digested per day usually increases, due to the higher intake of dry matter. This explains why high-producing dairy sheep and goats may have a level of intake of between 4% and 7% of BW, whereas in high-producing cows this figure does not usually exceed 4%. 2) have a more selective feeding behavior, choosing feeds, or parts of feeds (young stems, leaves, and buds) which are of good quality, which cause lower rumen fill, and whose digestibility is less affected by rumen feed passage rate; 3) are more negatively affected in their intake by the particle size and the fiber content of the forages and have to grind the feed particles more finely to allow them to pass through the rumen and other compartments of the foregut. Consequently, sheep and goats have to spend more time eating, chewing, and ruminating each kilogram of feed to achieve critical particle size to allow feed particles to exit the foregut and ruminate even small size feed particles; thus, 4) ruminate more finely grains and pellets and thus tend to have higher digestibility of these energy-rich feeds. Production efficiency is always targeted as the maximization of obtained products relative to the amount of input used (e.g., kg of milk or meat per unit of input). The most common indexes used to describe production efficiency consider dry matter, energy, or nutriens intake as the main inputs. Feed efficiency is usually calculated as kilograms of milk produced per kilogram of dry matter intake (higher is more efficient), whereas the feed conversion ratio is computed as kilograms of dry matter intake per kilogram of milk (lower is more efficient). The feed conversion ratio is conventionally the reciprocal of the feed efficiency. Complementary to feed efficiency, it is possible to quantify input losses (inefficiency in terms of the surplus of feed) and the environmental impact of the production process. Large heterogeneity in small ruminant production systems makes it difficult to define exhaustive indicators and benchmarks for efficiency and optimal feed efficiency levels. In livestock systems, feed efficiency can be measured considering two different reference boundaries: the animal and the farm. At the animal level, nutritional models aim to provide accurate estimates of feed requirements, intake, and production levels. Model outputs should also allow performing adequate calculations and evaluations of production efficiency. The main factor affecting feed efficiency is undoubtedly the production level of the animals. Increases in production levels dilute the incidence of maintenance requirements on total requirements per unit of product, thus increasing feed efficiency. Feed efficiency values in dairy sheep vary from 0.3 to 1.0 kg of milk/kg of dry matter intake. In meat sheep, feed efficiency can be quite variable depending on the BW, breeds, and gain composition. Oliveira et al. (2014), reviewing performances of dairy goats, reported that average feed efficiency in 17 studies was 1.06 kg of milk/kg of dry matter intake (varying from 1.44 to 0.74), with ranges of daily milk production that varied from 1.1 to 3.5 kg of milk. Under Brazilian conditions, Lima et al. (2017) reviewed key technical efficiency and economic performances of feedlot lambs (ranging from 16 to 52 kg of BW and average daily gain ranging from 0.15 to 0.38 kg of average daily gain/d), observing values of feed efficiency equal to 210 g/kg of dry matter intake (ranging from a minimum of 140 to a maximum of 280) and feed conversion equal to 4.7 (ranging from 3.5 to 6.9). Feed conversion index can also be indicated regarding residual feed intake, firstly proposed by Koch et al. (1963). 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