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

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• The 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 many 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, 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 the monitoring of the movement and environmental effects on grazing and browsing sheep and goats • Large data set 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
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Apr. 2019, Vol. 9, No. 2
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Feature Article
How can nutrition models increase the
production efficiency of sheep and goat
operations?
Antonello Cannas, Luis O. Tedeschi, Alberto S. Atzori, and Mondina F. Lunesu
Department of Agricultural Sciences, University of Sassari, 07100, Sassari, Italy
Department of Animal Science, Texas A&M University, College Station, TX 77843-2471
Keywords: efciency, goats, nutrition models, sheep
Introduction
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 bil-
lion 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, geographi-
cal areas, and by using diverse breeds, populations, and crosses,
due to their high nutritional and environmental adaptability.
For this reason, the production efciency of sheep and goats
is much more variable and difcult 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 specic nutritional features of
small ruminants and the many management and environmental
factors that affect their performances and efciency.
Sheep and Goats Are Not Just Small Cattle
Recommendations for feeding sheep and goats are often
derived from work on cattle, whose nutrition and feeding man-
agement 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,
Implications
Improvement of nutrition efciency 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 ap-
proaches to more comprehensive and dynamic models, and
are able to account for many more variables than in the past.
Further improvements in the nutritional efciency 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 articial intelligence tools and ma-
chine 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.
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34 Animal Frontiers
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 sat-
isfy their maintenance requirements, which results in a higher
passage rate of feed and lower feed digestibility (especially
ber). 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 gure 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 ll, and whose digest-
ibility is less affected by rumen feed passage rate; 3) are more
negatively affected in their intake by the particle size and the
ber content of the forages and have to grind the feed par-
ticles more nely 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 rumi-
nating each kilogram of feed to achieve critical particle size
Figure 1. 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).
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35
Apr. 2019, Vol. 9, No. 2
to allow feed particles to exit the foregut and ruminate even
small size feed particles; thus, 4) ruminate more nely grains
and pellets and thus tend to have higher digestibility of these
energy-rich feeds.
Production Efficiency in Sheep and Goats
Production efciency is always targeted as the maximiza-
tion of obtained products relative to the amount of input used
(e.g., kg of milk or meat per unit of input). The most com-
mon indexes used to describe production efciency consider
dry matter, energy, or nutriens intake as the main inputs. Feed
efciency is usually calculated as kilograms of milk produced
per kilogram of dry matter intake (higher is more efcient),
whereas the feed conversion ratio is computed as kilograms of
dry matter intake per kilogram of milk (lower is more efcient).
The feed conversion ratio is conventionally the reciprocal of the
feed efciency. Complementary to feed efciency, it is possible
to quantify input losses (inefciency in terms of the surplus of
feed) and the environmental impact of the production process.
Large heterogeneity in small ruminant production systems
makes it difcult to dene exhaustive indicators and bench-
marks for efciency and optimal feed efciency levels. In live-
stock systems, feed efciency 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 calcula-
tions and evaluations of production efciency. The main factor
affecting feed efciency 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 efciency.
Feed efciency values in dairy sheep vary from 0.3 to 1.0 kg
of milk/kg of dry matter intake. In meat sheep, feed efciency
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 efciency 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 efciency and economic perfor-
mances 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 efciency equal to 210 g/kg of
dry matter intake (ranging from a minimum of 140 to a max-
imum 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, rstly proposed by Koch et al. (1963). It
is dened as the difference between the actual feed intake and
the predicted intake based on BW and animal performance.
A low-residual feed intake corresponds to less feed consump-
tion, for equal weight gain, since animals with low-residual
feed intake eat less food than the amount estimated by their
BW and weight gain. Residual feed intake is considered a
reliable indicator of the differences in feed conversion ratio
based on the diverse genetic background of individuals. It
is largely used in meat production more than in dairy pro-
duction, due to the possibility to directly relate residual feed
intake with body mass deposition in the former, and the dif-
culty to attribute residual feed intake to nutrient utilization
for milk synthesis or body reserve variation in the latter. The
reduction of residual feed intake would allow a reduction of
the feed costs and of the environmental impact of the ani-
mals by reducing methane and other greenhouse gas emis-
sions (Zhang et al., 2017). Many factors related to individual
genetic background and farm conditions affect residual feed
intake, such as breeds, age, body composition, nutrient diges-
tion and metabolism, energy output, body activity, thermal
regulation processes, and feeding behavior. There are several
studies that attempted to use residual feed intake to improve
production efciency in fattening lambs, especially for selec-
tion programs (François et al., 2007). However, as reported
and reviewed by Lima et al. (2017), there is no way to iden-
tify animals with high-feed efciency and high gains through
residual feed intake because this index does not consider the
production level (Berry and Crowley, 2012). Indeed, residual
feed intake can be low even in animals with low-weight gains,
which usually are considered inefcient from a productive
point of view, because of the high incidence of their main-
tenance costs over total nutritional costs. Given the limita-
tions of residual feed intake, it is possible to use the “Residual
intake and BW gain index” (called RIG; Berry and Crowley,
2012), which identies animals, at equal BW, with fast growth
and which consume less food than the average intake of the
population. Indeed, in feedlot lambs, feed efciency meas-
ures and feed conversion ratio were highly correlated with
the “Residual intake and BW gain index” (0.699 and −0.685,
respectively) and less correlated with the residual feed intake
(−0.462 and 0.443, respectively; Lima et al., 2017). The same
authors also observed that both indexes can be used effec-
tively to represent the differences in economic performance
among different animals or production systems, being highly
associated with returns and protability.
Nutritional models also allow estimation of environmental
consequences of animal production. In small dairy ruminants, as
milk production increases, there is a large reduction in the emis-
sions of methane per kilogram of milk due to the dilution effect of
methane emitted related to maintenance costs, as shown for dairy
sheep in Figure 2A. In 35-kg BW meat lambs, fed on ryegrass-
based pasture, with increasing levels of dry matter intake to 0.36,
0.56, 0.70, and 0.87 kg/d, changes in the energy available for gain
were −25%, +13%, +47%, and +80% of that required for mainte-
nance, leading to a lower intensity of methane emissions of 25.2,
23.8, 23.1, and 20.8 g/kg of dry matter intake, respectively (Knight
et al., 2011). Indeed, methane and carbon dioxide emissions per
unit of product can be considered a proxy for animal nutritional
efciency, because decreasing emission of these gases implies a
more efcient utilization of nutritional resources.
At the farm level, nutritional efficiency depends on
nutritional and managerial factors, and farm or production
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36 Animal Frontiers
conditions. Farm efficiency increases when, assuming a
constant level of farm outputs, the inputs used decrease.
Important improvements in efficiency are generally reached
by reducing mortality andmorbidity, and increasing repro-
duction efficiency. Hygiene, animal welfare and animal
comfort, thermoregulation, feed, and water availability,
among others, are often fundamental checkpoints to pre-
vent these causes of inefficiency. Differences in efficiency
can be highlighted among different farms also consider-
ing their methane emissions, which are always reduced as
milk production increases, denoting a marked increase in
nutrition efficiency that leads to more efficient use of the
resources. Figure 2B exemplifies this behavior. From this
point of view, environmental indicators can be considered
among the most useful proxies to target efficiency of pro-
duction systems.
Evolution of Nutrition Models for Sheep and
Goats
Nutritional models for sheep and goats
There is a vast area of application of models to improve
production efciency of sheep and goats. Probably the most
important and explored area is that related to nutrition mod-
els, but epidemiological or whole-farm decision support mod-
els might also provide some assistance to improve production
efciency. Historically, nutrition models have developed from
simple systems, which referred to general feeding situations,
to complex systems, with mechanistic components, which aim
to account for many variables, including animal, dietary, and
environmental factors. Because modern nutritional models are
more comprehensive, they require more inputs than the older,
simpler systems. They are also subjected to continuous revi-
sions and updates as new research becomes available. Although
the nutrition recommendations in the 1970s were mostly based
on tabular values, since the 1980s nutrition models have been
implemented in nutritional software, making possible more
complex predictions in a continuous range of variables and
conditions. However, all of these models face the challenge of
being able to consider a wide range of feeding and environmen-
tal conditions. For this reason, development of nutrition mod-
els for sheep and goats is even more challenging than for cattle.
Despite this, the available nutrition models for sheep and goats
are often more empirical, account for fewer variables, and are
updatedless frequently than those for cattle (Cannas, 2004a;
Cannas et al., 2008).
North American models
The National Research Council (NRC) was formed in 1916
by the National Academy of Sciences with a specic mandate
by the President of the United States: organize the scientic
resource of the country during the First World War (NRC,
1982). Subsequently, the Committee on Animal Nutrition was
formed in 1917 under the auspices of the Committee on Food
and Nutrition (NRC, 1982), and these days it is under the spon-
sorship of the Board on Agriculture and Renewable Research.
The Committee on Animal Nutrition has published reports of
diverse topics, including nutrition (energy and nutrient require-
ments) and reproduction of farm animals such as poultry,
swine, cattle, sheep, goats, horses, andshes.
For sheep, the Committee on Animal Nutrition released the
rst attempt of Recommended Nutrient Allowances for Sheep
in 1945 (NRC, 1945), recognizing the importance of adequate
nutrition of gestating ewes to produce vigorous and strong
lambs at birth. The protein requirement for maintenance was
based on that recommended for cattle in 1945 (i.e., 0.6 g/kg
of BW/d) and total digestible nutrients requirement was estab-
lished at 8 g/kg of BW/d (NRC, 1945). Minor modications
were included in subsequent revisions (NRC, 1949, 1957). The
third revision in 1964 (NRC, 1964) discussed the conversion
of total digestible nutrients to digestible energy, metaboliz-
able energy, and net energy based on the work of Garrett et
Figure 2. Measurements of production efciency. (A) Feed efciency and
methane emissions per kilogram of milk of an average dairy sheep (50 kg of
body weight), assuming dry matter intake (DMI) intake and energy require-
ments estimated with the Small Ruminant Nutrition System model (Cannas
et al., 2007; Tedeschi et al., 2010). (B) Whole farm emissions of methane per
Mcal of milk metabolizable energy (ME) per headfrom semi-extensive and
extensive dairy sheep and dairy goats farms of Sardinia, Italy (Atzori A.S.,
Lunesu M.F, Cannas A., unpublished data from the project Forage4Climate;
EU LIFE+15).
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37
Apr. 2019, Vol. 9, No. 2
al. (1959), but recommendations were still based on the die-
tary total digestible nutrients (27 g/kg0.75 of BW/d). In 1968,
the fourth revision (NRC, 1968) marked the beginning of the
exponential growth pattern in the content of these publications
(Figure 3), including a revised table of feed composition and
adoption of the metric system rather than the Imperial system.
The fth revision came in 1975 (NRC, 1975) and included a
detailed discussion on nutrient requirements and symptoms of
deciency, signicant changes to the protein requirement for
lactation and growing lambs, given the changes in the genetic
potential of the animals compared with previous publications,
and renement of energy requirements for maintenance and
growth. After 10 yr, the sixth revision of nutrition requirements
for sheep was released in 1985 (NRC, 1985) and included many
modications to factorize the requirements of energy and
nutrients for different physiological stages by providing equa-
tions to facilitate the calculations.
In 1981, after 35 yr that the Recommended Nutrient
Allowances for Sheep was released, the Committee on Animal
Nutrition issued the rst report on the energy and nutrient
requirements for goats, given their increasing economic rele-
vance in the world and accumulated knowledge from national
and international symposia (NRC, 1981). Considerable data
on energy and protein requirements, from the Raja Balwant
Singh College at Bichpuri in India for dairy and meat goats
and the Texas A&M Agricultural Experiment Station at San
Angelo, TX for Angora goats, were used in construction
of this report. In a limited, but signicant, chapter on the
browsing habit of goats, the committee members indicated
key differences between goats and other domesticated rumi-
nants (cattle and sheep) and similarities with wildlife (NRC,
1981). It brought to light many aspects of browsing and
grazing and portrayed goats as “mobile pruning machines”
of bushy shrubs, being an intrinsic benet for cattle ranchers
(Provenza, 1978).
After 22 yr without revisions, a breakthrough occurred in
2007 with the release of the Nutrient Requirements of Small
Ruminants for sheep, goats, cervids (e.g., white-tailed deer,
red deer, American elk, and caribou/reindeer), and new world
camelids (e.g., alpacas and llamas) (NRC, 2007). Until then,
the nutritional recommendations for sheep and goats were
separate, and cervids and new world camelids never had a
nutrient recommendation publication. The 2007 publication
contains more than 360 pages and 1900 citations (Figure 3),
containing profound departures and many innovative ideas
compared with previous small ruminant publications by the
National Academy of Sciences. In summary, the NRC’s (2007)
Committee adopted the deterministic, mechanistic mathemat-
ical model developed by Cannas et al. (2004) for sheep and
relied almost exclusively on publications from the E (Kika) de
la Garza American Institute for Goat Research at Langston
University for goats (Sahlu et al., 2004).
The model developed by Cannas et al. (2004) was based on
the Cornell Net Carbohydrate and Protein System (CNCPS),
which was originally developed for beef and dairy cattle (Fox
et al., 2004). It was called CNCPS-Sheep and accounted for
nutrient requirements of sheep, developed integrating data
and equations of Agricultural Research Council (ARC, 1980),
Institut National de la Recherche Agronomique (INRA, 1988;
cited by INRA, 2018), and especially of the Commonwealth
Scientic and Industrial Research Organisation (CSIRO, 1990)
models. From the latter were taken, with some modications,
the approach of the use of the degree of maturity of growing
animals to estimate their requirements, the concept that energy
and protein requirements for maintenance increase in propor-
tion to the dietary intake, and the cold stress submodel of main-
tenance requirements. In the CNCPS-sheep, an original body
reserve model and new prediction equations for liquid and solid
passage rates were proposed. The model included components,
derived from an Italian earlier model (Assis-T; http://www.
crpa.it/nqcontent.cfm?a_id=1468&tt=crpa_www&sp=assist-
dev), for its utilization with dairy sheep. Compared with the
CNCPS for cattle, the fecal endogenous matter prediction
was modied, after an extensive evaluation, to avoid double
accounting of microbial matter.
Many modications to the original CNCPS sheep model
were proposed (e.g., calculation of fecal crude protein, main-
tenance cost, and efciency for gain of growing animals) and
along with the inclusion of the nutrient requirements for
goats, which was largely spearheaded by Cannas et al. (2008),
the Small Ruminant Nutrition System (http://nutritionmod-
els.com/srns.html) and computer model were conceived and
evaluated (Cannas et al., 2007; Tedeschi et al., 2010). Further
advancements of the Small Ruminant Nutrition System were
proposed by Regadas Filho et al. (2014), including a two-com-
partment model to predict rumen feed passage rate, instead of
the original one-compartment model, and testing a new intake
prediction for goats, and by Cannas et al. (2016), who proposed
reference values for optimal NDF intake in lactating ewes.
Recently, the Small Ruminant Nutrition System was integrated
into the Ruminant Nutrition System model (http://nutrition-
models.com/rns.html) (Tedeschi and Fox, 2018), with some
modications compared with the original version (e.g., goat
Figure 3. Chronological progress of numbers of pages (open symbols) and
citations (closed symbols) of the National Research Council publications on
the nutrient requirements of sheep (red circles) and goats (blue squares).
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38 Animal Frontiers
passage rates were based on Seo’s et al., 2006, and Tedeschi et
al., 2012) and the inclusion of prediction equations for meth-
ane production.
European and Australian models
Several nutrition models for sheep and goats have been
proposed in Europe and subsequently in Australia. In 1965
the ARC presented a feeding model (ARC, 1965, cited by
ARC, 1980), which was markedly improved and expanded in
the 1980s (ARC, 1980). The ARC feeding model represented
a major advancement in the knowledge of requirements and
feed nutritive values for sheepand cattle, whereas goats were
not considered. This model explored in detail the composi-
tion of the body of sheep from the uterine life to the mature
stages, developing specic prediction models for each stage of
life, which were largely based on slaughter data. The energy
requirements were based on calorimetric studies of Sir Kenneth
Blaxter, which also provided data and models for prediction of
the efciency of conversion of metabolizable energy (ME) to
net energy (NE) for various physiological functions, by using
dietary metabolizability as a predictor (ratio of dietary or feed
ME to gross energy). This efciency declines as the feeding level
increases, but the estimates of feed energy values were measured
at maintenance level. Dietary energy was expressed regarding
ME and the diets balanced with the same unit, by converting
the NE requirements for the various physiological functions
(e.g., maintenance, milk production, growth, and pregnancy)
to ME values with the efciencies above mentioned.
Regarding dietary protein utilization, this model overcame
the earlier approaches based on crude protein or digestible
crude protein, developing, similarly to what was done by North
American models, a model in which the energy and nitrogen
requirements of rumen bacteria were considered, and the
microbial efciency was estimated. The model predicted rumen
degraded and undegraded protein and the supply of metab-
olizable protein of feed and microbial origin to the intestine.
The protein requirements of sheep were calculated by explicitly
accounting for endogenous urinary nitrogen excretion, wool
nitrogen losses, and the net protein content of the gain or the
milk. The rumen degraded and undegraded protein require-
ments for each category were estimated and reported in tables
as a function of the BW of the animals and, depending on the
physiological stage, of their average daily gain, milk produc-
tion, pregnancy, and also ofdietary metabolizability. The ARC
model has been the base for the development of later sheep
models in the United Kingdom (AFRC, 1993, 1998), and in
Australia (CSIRO, 1990, 2007), as depicted in Figure 4. Even
the French Institut National de la Recherche Agronomique
sheep model (INRA; 1978, 1988, 1989, 2007, 2018; cited by
INRA, 2018) adopted considerable information from the ARC
nutrition model.
The CSIRO (1990, 2007) model was developed by inte-
grating the information presented in the ARC and AFRC
models with the large body of research carried out on sheep
in Australia. It made major advancements in several areas,
especially in requirements, introducing many mechanistic
components in their prediction. Indeed, for the rst time, the
degree of maturity was used in a comprehensive feeding sys-
tem to estimate the composition of the gain and the energy
and protein requirements of growing sheep. Thus, instead of
using different growth and body composition equations for
early and late maturing breeds and for males and females, as
done before, the ratio of current to mature weight was used as
a general predictor, together with the level of feeding, allow-
ing use of the same equations to estimate composition of gain
and the growth requirements for sheep breeds of very differ-
ent mature size and precocity and for different sexes. The con-
cept of the degree of maturity and standard mature weight
was also used in the prediction of dry matter intake. Another
major improvement, compared with the existing models, was
the fact that energy and protein requirements for maintenance
increased not only as a function of metabolic weight, as in the
earlier systems, but also in proportion to the level of intake. As
intake increases, there is an increase in the size and activity of
the visceral organs, the most metabolically active and expensive
organs. Thus, at equal BW, slowly growing or adult dry ani-
mals would have a substantially lower maintenance cost than
fast growing or lactating animals. A mechanistic submodel to
estimate the extra maintenance cost due to cold stress cost in
sheep was developed and models to estimate the cost of grazing
and pasture intake, based on pasture quality, were included.
The CSIRO (1990, 2007) model is the only model for small
ruminants to estimate the effect of cold stress on requirements.
As previously mentioned, the CNCPS for sheep and the Small
Ruminant Nutrition System for sheep and goats adopted many
of the submodels of the CSIRO (1990, 2007) model, including
that for cold stress prediction.
In contrast to the previous models, the Agricultural and
Food Research Council (AFRC, 1993, 1998) and the INRA
model include all three major ruminant species. The sheep and
goat model of the AFRC is based on a modication and sim-
plication of the ARC (1980) model, making it more practi-
cal. The goat component was further detailed and improved in
1998 with a specic report (AFRC, 1998), which built on the
previous British models by using specic data and models on
dairy goats.
The INRA sheep and goat model was rst published in
1978 and then evolved with a very recent update (INRA, 1978,
1988, 1989, 2007, 2018; all cited by INRA, 2018). This model
is widely used in many European countries (e.g., France, Spain,
and Italy) and African countries and made the basis of the
Dutch system. The INRA model uses the same feeding units
for all species. They are estimated as a ratio between the NE
value of a feed and the corresponding value of a reference feed,
a kilogram of barley grains (1,760 kcal of NE/kg as fed; INRA,
2018). The corresponding units are called the forage unit for
milk, used for all females of dairy animals (cows, ewes, and
goats) and the forage unit for meat, used for all males of all
breeds and females of meat breeds (cattle and sheep). Although
earlier versions (from INRA 1977 to INRA, 2007) estimated
the energy and protein value of feeds at a xed feeding level
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39
Apr. 2019, Vol. 9, No. 2
(near or at maintenance feeding level for energy, xed at 6%
rumen passage rate for proteins), the latest version (INRA,
2018) developed equations to account for the reduction in
digestibility and the increase in rumen escape of nutrients as
the level of intake increases. The energy values are also cor-
rected for negative associative effects due to high concentrate
intake, considered as a probe of low rumen pH, and for rumen
nitrogen balance, reducing the values in case of rumen nitrogen
shortage. Regarding requirements, the INRA sheep submodel
is fully empirical, accounted for few variables and evolved very
little since it was rst published. It does not consider environ-
mental factors in the estimation of requirements, has a sim-
plied body reserve model, and estimates the cost of grazing
by increasing the energy maintenance cost by xed coefcients,
based on the quality of the pasture, dened in three main
classes. Despite this, it has the merit of being the rst nutri-
tion model to consider the requirements of dairy sheep and not
those of meat or wool sheep only.
The INRA goat model is also empirical and does not
account for environmental factors, but it evolved mark-
edly over time, with much new information and predictions
included in the latest version (INRA, 2018). It is focused on
specialized dairy goat breeds, namely, Saanen and Alpine. It
includes models to predict the lactation curve based on parity,
genetic potential and days in milk, and the corresponding milk
fat and protein concentrations, separately for the two breeds
mentioned above. In addition, for the same breeds prediction
equations of the kinetics of energy reserves and live weight
changes, whichaccounts for the homeorhetic control of milk
production and body reserve variations, have been developed.
Eventually, based on previous predictions, empirical mode-
ling of the energy balance driven by homeorhesis and poten-
tial milk yield was proposed. These are major advances in the
direction of improving the production efciency of goats, since
they allow an accurate prediction of the expected body reserve
evolution and thus a close monitoring of the animals that do
not follow the expected patterns, with potential positive impli-
cations on the production level of the animals and also on their
health. Indeed, too fast body reserve losses are considered one
of the main causes of nutritional disorders and goat culling.
The Future of Sheep and Goat Nutrition Models
The scientic community has surmounted many obstacles
since the mid-1940s in collecting data and acquiring knowl-
edge to develop recommendations on nutrient requirements
for domesticated ruminants, including sheep and goats. This
effort resulted in a huge leap forward, obtained building upon
previous models (Figure 4), with the release of the CNCPS-
Sheep (Cannas et al., 2004), the goat model of the American
Institute for Goat Research of Langston University (Sahlu
et al., 2004), the NRC (2007), the Small Ruminant Nutrition
System (Cannas et al., 2007; Tedeschi et al. 2010), and eventu-
ally the INRA (2018).
An extensive comparison of many sheep and goat models
was carried out by Cannas (2004) for sheep and Cannas et al.
(2008) for goats. The comparisons highlighted that although
the total cumulated prediction for maintenance and milk
Figure 4. Evolution and interconnection of the main sheep and goat nutrition models. C = cattle; S = sheep; G = goats; AIGR = American Institute for Goat
Research, Langston University; ARC = Agricultural Research Council; AFRC = Agricultural and Food Research Council; INRA = Institut National de la
Recherche Agronomique; CSIRO = Commonwealth Scientic and Industrial Research Organization; NRC = National Research Council; CNCPS = Cornell
Net Carbohydrate and Protein System.
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40 Animal Frontiers
production in thermoneutral conditions did not differ much
among feeding systems, there were large differences in the var-
iables considered to estimate the maintenance requirements
besides the basal metabolic rate, in the prediction of move-
ment, in those for pregnancy requirements, and, above all, for
growth. An example of these large numerical and methodolog-
ical differences is illustrated in Figure 5 for growing goats.
Thus, considerable work is still required to improve the pre-
dictions of energy and nutrients for sheep and goats (and other
wildlife and small ruminants), given the diverse environment
and management practices in which they are raised around the
globe. New hurdles exist (e.g., environmental pollution, climate
change, and water scarcity) and they must be defeated to provide
high-quality protein to an astounding human population that
continues to grow. Sahlu et al. (2004) discussed some elements
in the scientic literature on goat nutrient requirements that
require further investigation. Although some of these elements
have been addressed in the Ruminant Nutrition System model
(Tedeschi and Fox, 2018), others persist (e.g.,ruminal recycled
nitrogen, browsing and pasture forage intake, the efciency of
use of amino acids for maintenance and growth, the effective-
ness of ber, the prediction of rumen pH, and the effects of heat
stress).
Further improvements in the nutritional efciency of small
ruminants could be based on the integration of nutrition mod-
els with the data derived from sensor technology, which can
markedly increase the amount of information available by con-
tinuously monitoring the animals, their environment, and their
performances. Many systems at research and market level are
already available for small ruminants (Fogarty et al., 2018), and
many others will be soon ready, for identifying, tracking and
weighing animals, monitoring their body temperature and heart
rate, andassessing body condition score, among many others.
Utilization of this information would require a change in the
approach taken when developing nutrition models, byincreas-
ing the few and mostly static inputs (e.g., intake, BW, yield, and
composition of milk) used so far. Great improvements could
be achieved especially for sheep and goats on pasture, e.g., to
assess their actual movement or the direct effects of climatic
conditions, and thus the corresponding requirements. This
could produce a great improvement in prediction accuracy and
thus in nutritional efciency of small ruminants.
Using Predictive Analytics to Improve Nutritional
Modeling
The media publicity about articial intelligence and other
data technology breakthroughs can be daunting at times. It
may even catch savvy experts unprepared about the evolution
that these technologies have to go through before reaching their
state-of-the-art reputation.
Our education in science is grounded on the Platonic think-
ing that knowledge is not simply a collection of beliefs; rather,
it reects a systematic and natural way the universe works. The
word “science” derives from the Latin “scientia,” which in turn
translates the Greek “episteme,” from which English derives
“epistemology,” the study of what knowledge is and how to
acquire it. Consequently, learning is needed to develop ideas to
gain knowledge. Much of the Platonic thinking on knowledge
was incorporated into the data–information–knowledge–wis-
dom hierarchy (Figure 6), which acts as a lighthouse that has
guided much scientic research.
Figure 5. The relationship between BW and net energy requirements for 100 g/d of average daily gain of growing goats, as predicted by different feeding systems
(Cannas et al., 2008, modied), assuming a mature weight of 55 kg for females and 85 kg for males for the Small Ruminant Nutrition System model (SRNS).
The different approaches taken bring to very different estimations of the energy requirements for growth. AFRC = Agricultural and Food Research Council;
NRC = National Research Council.
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41
Apr. 2019, Vol. 9, No. 2
At the beginning of research endeavors in any science eld,
scientists lacked data and frequently blamed that lack on our ina-
bility to make adequate predictions or forecasts. In the Animal
Science eld, circa the 1960s data started to become abundant
as governmental and commercial experimental research sta-
tions were built around the world. Not until recently, circa the
2000s, remote sensing, and many other electronic devices were
developed and made broadly available to ease the collection
and storage of data (e.g., the Internet). Then, we generated data
but lacked ways to analyze large data sets. Statistical methods
have been used to compress the data (e.g., principal component
analysis and partial least squares) and to make sense of the
vast amount of data. However, the arrival of the big-data era
necessitated more powerful data analytics methods. Articial
intelligence has evolved to make sense of the big data, but it has
some aws: although it may represent the data structure, it does
not explicitly explain the underlying assumptions of the data
and the selective combination of specic inputs that it used
to reach the result. Articial intelligence misses the wisdom in
the data–information–knowledge–wisdom hierarchy because
wisdom requires judgments that are unique to individuals
who assimilate information and knowledge simultaneously to
make intelligent decisions and novelty creations. The scien-
tic road we have traveled since the 1940s has had its ups and
downs (Tedeschi, 2019), reecting our appetite for scientic
data, the need to understand the unknown, and the desire to
make rational decisions to improve our livelihoods, assuming
that greater knowledge and wisdom would reduce the risks of
being wrong (Figure 6). Despite our ignorance of how articial
intelligence works its way through data in developing its sets of
neural network weights for the inputs—what some call learn-
ing—it is a powerful advancement in predictive analytics. The
articial intelligence technique is a fantastic data-driven tech-
nology that was originally developed in the 1950s with the goal
of automating human intelligence through computational pro-
gramming. As depicted in Figure7, the programming codes for
articial intelligence (i.e., rules of logic and calculations) were
initially hardcoded, like most computer programming tasks.
Then, with the boom of expert systems in the 1980s, the “learn-
ing” era began to take shape (Chollet, 2018). The question has
Figure 6. The data–information–knowledge–wisdom (DIKW) hierarchy as a pyramid to manage knowledge. Reproduced with permission from Tedeschi (2019).
Figure 7. A schematic representation of how (A) classical programming and (B) “learning” programming paradigms use inputs, outputs, and codes (i.e., rules)
for predictions. The arrow from code to “learning” programming indicates back propagation frequently used in deep learning. Adapted from Chollet (2018).
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42 Animal Frontiers
always been: can computers create a code given the inputs and
the outputs rather than create outputs from inputs and codes?
Articial intelligence comprises different highly sophisticated,
data-driven technologies that are based on neural network
programming.
Articial intelligence has seen limited use in agriculture
production (Liakos et al., 2018), and even less in animal sci-
ence. Different articial intelligence technologies have been
featured in cattle studies: animal welfare, genome-wide predic-
tions, breed classication, expected progeny differences, ana-
tomical biometrics for cattle identication and recognition,
growth patterns, and rumen functioning in lactating dairy
cows (Tedeschi, 2019). In studies of small ruminants, articial
intelligence applications have been incipient and restricted to
milk production, including the relationship between prota-
bility and production systems of sheep and goats (Magdalena
et al., 2009) and weekly prediction of milk yield in goats
(Fernández et al., 2007). Some recent exploratory, sporadic
applications of articial intelligence in the sheep industry
have been publicized for wool production (https://www.sheep-
central.com/does-artificial-intelligence-have-a-role-in-the-
wool-industry/) and animal distress (https://www.dailymail.
co.uk/sciencetech/article-4559216/Scientists-use-AI-sheep-
pain.html).
In conclusion, articial intelligence technologies were
designed to learn from data and provide forecasting, but not
as a tool to help us understand the underlying mechanisms.
Little is known about the reasoning behind each prediction by
an articial intelligence algorithm, so as Knight (2017) asked,
can we trust articial intelligence predictions if we cannot rea-
sonably explain them? This might be an important bottleneck
for combining articial intelligence technologies with nutri-
tion models, a bottleneck that needs to be transcended to fur-
ther improve ruminant nutrition systems (Cannas et al., 2004;
Tedeschi et al., 2010; Tedeschi and Fox, 2018). It feels like we
have traveled far and developed powerful advancements in data
and predictive analytics and digital computing, only to relive
the black-box era. Inexplicability is a known limitation of arti-
cial intelligence, but it was not developed to provide explana-
tions. However, the question still stands: how can we benet
from articial intelligence, or its variants, to further advance
our mathematical modeling efforts in animal production, more
specically ruminant nutrition?
Funding
The data reported in Figure 2 have been developed in the pro-
ject Forage4Climate (LIFE15CCM/00039), funded by the EU
LIFE+15 program on Climate Change Mitigation.
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About the Authors
Antonello Cannas is a profes-
sor of Animal Nutrition in the
Department of Agricultural
Sciences (Dipartimento di
Agraria), and Director of the
PhD School at the University
of Sassari, Sardinia, Italy. He
holds a Bachelor’s degree in
Agricultural Sciences from the
University of Sassari, and MSc
and PhD in Animal Science from
Cornell University, Ithaca, NY.
His main areas of research are on
the nutrition and feeding of small
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els, estimation of energy and protein requirements, prediction of rumen
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Sheep and its evolution into the Small Ruminant Nutrition System, jointly
with D.G. Fox, Cornell University, USA and L.O. Tedeschi, Texas A & M
University, USA.
Corresponding author: cannas@uniss.it
Luis Tedeschi is a professor and
fellow in the Department of
Animal Science at Texas A&M
University, College Station, TX.
He holds a Bachelor of Science
degree in Agronomy Engineering
and Master of Science degree
in Animal and Forage Sciences
from the University of São Paulo,
Brazil, and Doctor of Philosophy
degree in Animal Science from
the Cornell University, Ithaca,
NY. His research focuses on the
integration of accumulated scien-
tic knowledge of ruminant nutrition into applied mathematical models
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impact on the global warming effect through the emission of methane.
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of The National Academies of Sciences, Engineering, and Medicine to
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Alberto
Stanislao Atzori
is an Assistant
Professor
in Animal
Nutrition at
the University
of Sassari. He
holds a degree
in Agricultural
Science and a
PhD in Animal
Science from
the University of Sassari (Italy). He spent 6 mo as visiting scholar at
Texas A&M University (USA) studying system dynamics modeling. His
education background is in ruminant nutrition and dairy farm manage-
ment. His current research focuses on the relationship among ruminant
nutrition and environmental impact of farms, to enhance production ef-
ciency and protability of farming systems. In his research activity, he
also uses system dynamics and life cycle assessment techniques applied
to livestock systems.
Mondina Francesca
Lunesu is a Post-Doctoral
Fellow in Animal Science
at the University of
Sassari. She holds a
degree in Agricultural
Science and a PhD in
Animal Science from the
University of Sassari
(Italy), with a dissertation
on the effect of type of
carbohydrates on dietary
energy partitioning in lac-
tating ewes and goats. She
spent 3 mo in South Africa
(Pretoria University) and
4 mo at the Department of
Animal Science at Texas A&M University, College Station, TX, studying
the digestibility and characteristics of dietary carbohydrates. Her current
research focus is on quantifying greenhouse gas emissions of sheep and
goats and on mitigation strategies based on the improvement of nutri-
tion and management techniques.
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... There is ample research literature on energy and protein requirements, and some modern feeding models for sheep have been published or updated in the last decade [2][3][4]. Still, none of the existing feeding standards or models reports optimal dietary fiber (NDF) concentrations for sheep. ...
... The Cornell Net Carbohydrate and Protein System (CNCPS) for sheep [4], as well as the Small Ruminant Nutrition System (SRNS) for sheep and goats based on CNCPS [8], suggest an optimum dietary NDF concentration of 200-245 g/kg DM. Although small ruminants adapt their feeding behavior to the fiber proportion of the offered diet [7,9], a reduction in fiber concentration below 20% has been shown to increase the risk of ruminal acidosis in feedlot cattle, which can challenge animal health and welfare [10]. ...
... Increasing dietary starch inclusion may also decrease dry matter intake, initially through poor digestibility of NDF and physical NDF filling and later through chemical satiety depending on dietary and animal variables [13]. Given the nutritional models have been evaluated based on meta-analyses of microbial protein flow [2,4]. Not accounting for depressed efficiency of microbial protein synthesis with higher starch diets or reduced DMI could overpredict microbial protein supply, limiting metabolizable protein. ...
Article
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Abstract: This study was conducted to investigate the effect of decreasing concentrations of dietary neutral detergent fiber (NDF) at high rumen undegradable protein (RUP) on performance, digestibility, chewing activity, blood attributes, and carcass characteristics in 32 weaned male Afshari lambs (90 days of age; 26 kg initial body weight; BW). Dietary metabolic energy (ME) was increased from 10.6–11.5 and 11.8 MJ/kg dry matter (DM) by replacing alfalfa hay with grain to achieve NDF concentrations of 270, 245, and 220 g/kg DM, respectively, at 66.6 g/kg DM of RUP. The control (CON) diet contained 10.9 MJ/kg ME, 270 g/kg NDF and 59.6 g/kg RUP on DM basis. Rations containedsimilar concentrations of crude protein (CP, 160 g/kg DM). Lambs were slaughtered after a 56-d feeding period. The increase in dietary RUP had no effect on BW and average daily gain (ADG) but tended to decrease apparent digestibility of CP and DM, significantlydecreasedplasma urea concentration, and increased carcass CP content. Other body or carcass characteristics were unchanged. Decreasing dietary fiber at high RUP did not result in adverse effects on BW, ADG, body length, withers height, apparent digestibility of DM and CP, and BFT, but decreased DM intake (1539 vs. 1706 g/d) and feed conversion ratio (FCR; 4.33 vs. 5.39) compared with CON. Gradual reduction in NDF and physically effective NDF did not affecteating, ruminating or chewing times. Plasma glucose concentration was greater for NDF220 than for the three other treatments (p = 0.015).Slaughtering traits were not affected by dietary treatment except for hot carcass weight, which increased in NDF220 and NDF245 compared with NDF270 (p = 0.021). The concentration of meat CP increased in NDF270 versus CON (167 vs. 152 g/kg). Quadratic effects occurred for meat ether extract concentration (highest in NDF220) and fat-tail weight (highest in NDF245). In conclusion, the results showed that increasing the proportion of RUP within dietary CP improves carcass protein accretion. Decreasing dietary NDF to 220 g/kg DM at high RUP does not impair eating behavior and improves FCR in 3-month-old fat-tailed lambs.
... Basándose en la GDP del estudio, se predijo el CMS y los requerimientos de energía neta (EN). El coeficiente de EN estimado para ganancia para las raciones con 10 y 20 g PCa/ kg MS fue de 1.12 y 1.05, respectivamente; valores >1 indican un uso eficiente de la EN que teóricamente contenían las dietas (29). El coeficiente de CMS estimado fue de 1.03<0.90<0.85 ...
... El coeficiente de CMS estimado fue de 1.03<0.90<0.85 para las dietas CON, 10 y 20 g de PCa, respectivamente; valores <1 indican aumentos en la eficiencia de retención de EN por kg de MSC, equivalente a un menor consumo de alimento para formar un kg de peso vivo (29). La relación entre el consumo observado vs estimado permite calcular el grado de eficiencia en el uso de la energía de los tratamientos (30), la cual al parecer fue mejor aprovechada por los corderos cuando se incluyó PCa lo que favoreció la GDP, CA y EF. ...
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Objetivo. Evaluar el efecto de la inclusión de propionato de calcio (PCa) sobre variables productivas y características de la canal en corderos en finalización. Materiales y métodos. Se utilizaron 24 coderos machos de la cruza Dorper*Pelibuey de 5 meses de edad, con un peso corporal promedio (μ±SD) de 27±2.7. Fueron asignados a uno de tres tratamientos [control (CON) y dos niveles de PCa: 10 y 20 g/kg de MS] en un diseño completamente al azar (3 tratamientos, 8 repeticiones por tratamiento, considerando cada cordero como una unidad experimental). Las variables de respuesta se redujeron a 1 valor medio para cada cordero, y los datos se analizaron en SAS versión 9.4 usando Proc Mixed. Resultados. La ganancia diaria de peso (GDP), conversión (CA) y eficiencia alimenticia (EF) fueron mayores en 13, 20 y 24%, respectivamente por la inclusión de 20 g PCa/kg MS (p≤0.05). El peso de la canal fría (PCF), rendimiento en canal caliente (RCC) y rendimiento en canal fría (RCF) fueron mayores al incrementar el nivel de inclusión de PCa (p≤0.05). Conclusiones. La suplementación con PCa en dosis de hasta 20g/kg en dietas de finalización puede mejorar los parámetros productivos y de rendimiento en canal sin afectar el consumo de materia seca (DMI).
... The observed similarity in FCR for Pedi, Damara, Dorper and Meatmaster can be attributed to efficient nutrient digestion, and/or utilisation for growth of different body tissues (Brand et al., 2017;Cannas et al., 2019;Claffey et al., 2018). Overall, all the breeds exhibited FCR within the expected range of 3.5-6.9 ...
... Overall, all the breeds exhibited FCR within the expected range of 3.5-6.9 (average 4.7) for the economic viability of a sheep enterprise under feedlot conditions (Cannas et al., 2019;Lima et al., 2017). The observed differences in hot and cold carcass weights were consistent with reported breed differences in final live weights. ...
Article
The study compared water intake, balance and efficiency, nutrient intake, digestibility and utilisation, growth performance, carcass attributes and income over feed costs for six South African sheep breeds; Pedi, Damara, Meatmaster, Dorper, Dohne Merino and Merino. Wether lambs (4–5 mo) were adapted to a pelleted total mixed diet containing 143.5 g crude protein/ kg DM and 10.29 MJ/ kg DM metabolisable energy for 21 d followed by 7 d and 42 d of digestibility and performance data collection, respectively. Water and nutrient intakes were lowest in Pedi and highest in Meatmaster (P ≤ 0.05). Compared to other breeds, Damara and Meatmaster had higher (P ≤ 0.05) DM digestibility, nitrogen balance and average daily gain, while Dorper and Damara had higher (P ≤ 0.05) neutral detergent fibre and ether extract digestibility, respectively. Pedi had lower (P ≤ 0.05) water balance than the rest of the breeds. Water and feed conversion ratios were lowest for the Pedi and Damara and highest for Dohne Merino (P ≤ 0.05). Damara and Pedi had lower (P ≤ 0.05) dressing percentages, hot and cold carcass weights than the other breeds. The lowest and highest income over feed costs were reported for Pedi and Dohne Merino, respectively. It was concluded that although Damara and Pedi had the most inferior meat production attributes, they were the most feed and water efficient breeds.
... The generated reinforcing loop, labeled "flock size", is expected to drive exponential growth of the system ( Figure 3.1). Since regional flock maintenance is highly associated with methane emissions (Cannas et al. 2019;Atzori et al. 2020), an exponential growth in flock size will also drive exponential growth in environmental impact (Figure 3.1). Exponential growth is not a sustainable pattern in systems (Ford 1999;Turner et al. 2016). ...
... that would respect the current cheese and milk market conditions and the land use capacity of Sardinia. Indeed, a constant increase in production efficiency would push for a reduction in sheep heads, which would lead to a positive reduction in environmental impact Cannas et al. 2019;Marino et al. 2016). ...
Article
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Three million sheep raised on 10,000 active farms operating in traditional and innovative farming systems in Sardinia, Italy account for 13% of sheep milk production in the European Union (EU). Almost all delivered milk is processed to sheep cheese and is destined for world trade. The Sardinian dairy sheep sector also emits approximately 1,600 kt CO2eq/year, approximately 60% of regional livestock greenhouse gas (GHG), prompting the need for regional mitigation plans. The SheepToShip LIFE project (EU‐LIFE Climate Change Action 2014‐2020) is a regional case study to test emissions mitigation strategies. Based on the SheepToShip LIFE findings, this paper presents a systems perspective against the backdrop of the Sustainable Development Goals (SDGs) framework, with the aim of underlining system interlinkages between environmental, societal, and economic objectives. The project included i) a life cycle thinking approach featuring environmental and socioeconomic traits of 18 sheep farms; ii) on‐farm implementation and demonstration of eco‐innovative mitigation techniques indicating the most viable actions to reduce impact; iii) focus groups discussing beliefs and reactions of the main stakeholders; and iv) group model building producing a causal loop diagram from a systems thinking approach and exploring insights for regional policy‐making that aligns with the SDGs. Causal links connect public interventions and stakeholder interaction (SDG 17) to boost farm eco‐innovations (SDGs 9 and 8) and education and farmer training (SDG 4), and they foster efficient production (SDG 12) and high‐quality food provisioning (SDG 2). These benefits contribute to climate change mitigation (SDG 13), water quality (SDG 6), and farm ecosystem services (SDG 15). This article is protected by copyright. All rights reserved.
... The FAO statistical database (FAOSTAT, 2020; http://www.fao.org/faostat/en/#home; accessed on 15 March 2021) reports 1239 million sheep heads in the global population in 2020, which increased by approximately 15% in the last 15 years [5]. However, this estimated population size has remained almost stable over the last three decades (FAOSTAT, 2020). ...
Article
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Considerable improvements in sheep multiple ovulation and embryo transfer (MOET)protocols have been made; however, unlike for cattle, MOET is poorly developed in sheep, and thus has not been broadly applicable as a routine procedure. The tightly folded nature of the ewe cervix, the inconsistent ovarian response to various superovulatory treatments, and the requirement of labor to handle animals, particularly during large-scale production, has limited the implementation of successful MOET in sheep. Moreover, several extrinsic factors (e.g., sources, the purity of gonadotrophins and their administration) and intrinsic factors (e.g., breed, age, nutrition, reproductive status) severely limit the practicability of MOET in sheep and other domestic animals. In this review, we summarize the effects of different superovulatory protocols, and their respective ovarian responses, in terms of ovulation rate, and embryo recovery and transfer. Furthermore, various strategies, such as inhibin immunization, conventional superovulation protocols, and melatonin implants for improving the ovarian response, are discussed in detail. Other reproductive techniques and their relative advantages and disadvantages, such as artificial insemination (AI), and donor embryo recovery and transfer to the recipient through different procedures, which must be taken into consideration for achieving satisfactory results during any MOET program in sheep, are also summarized in this article.
... Generally, precision feeding is recognised to be an effective GHG mitigation strategy for livestock sector with lowmedium potential especially for non-CO 2 GHG emissions, namely CH 4 emission from enteric fermentation, identified as the main source of impact in carbon footprint studies, while no clear results were identified on the other impact categories (Gerber, Steinfeld, et al. 2013;Henderson et al. 2017). Specific mathematical models have been developed in literature by several research institutions and universities, in order to predict requirements, feed utilisation, animal performance and nutrient excretion for ruminants using knowledge about feed composition, digestion and metabolism (Morgan-Davies et al. 2018;Cannas et al. 2019Cannas et al. , 2004Odintsov Vaintrub et al. 2021). ...
Article
Dairy sheep systems are relevant for the economy of many rural areas of the Mediterranean Basin and the optimisation of their productive factors is necessary to improve their competitiveness and to reduce their environmental impact. The objective of this study was to assess, through a farm-scale life cycle assessment (LCA) approach, the potential of reducing environmental impacts on ewe milk of an innovative farming system (IF), based on the adoption of a precision feeding approach to improve milk production and quality, compared with a conventional farming system (CF) in dairy sheep farms in Tuscany region, Italy. The LCA analysis was carried out through a cradle-to-farm-gate LCA, comparing three conventional farms with three innovative farms, using 1 kg of fat protein corrected milk (FPCM) as a functional unit. The Small Ruminants Module of Nutritional Dynamic System (NDS) software was used to estimate methane emissions due to enteric fermentation. The introduction of precision feeding strategy reduced the environmental impacts of ewe milk as a consequence of the increased milk production efficiency (+50%). Indeed, the environmental impact of ewe milk was reduced in IF by 42% as the average of the impact categories being significantly different between the two farming systems. • Highlights • Precision feeding is recognised as a strategy to mitigate the environmental impacts of ruminant production. • Dairy sheep innovative farms (IF), using a precision feeding approach, were compared with conventional farms (CF). • Environmental impacts of ewe mik were assessed through an LCA approach and a mechanistic model for ruminant diet formulation and evaluation. • Precision feeding improved milk production efficiency (+50) and lowered environmental impacts. • Environmental impact of ewe milk was significantly reduced in IF by 42% in eight impact categories out of 15.
... An interesting finding is that the extensive efficient farms seem to depend more on home-grown feed, whereas the intensive and semi-intensive farms depend more on purchased feed. Sheep farmers even under the same production system apply different feeding strategies; some produce home-grown feed, whereas others prefer to purchase a large part of their feed from markets and a debate regarding which strategy is the most profitable differ [29,40]. Another interesting result is that the fixed cost, which is the most important source of production cost followed by feeding cost, was much higher in the inefficient farms. ...
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The slow adoption of innovations is a key challenge that the European sheep sector faces for its sustainability. The future of the sector lies on the adoption of best practices, modern technologies and innovations that can improve its resilience and mitigate its dependence on public support. In this study, the concept of technical efficiency was used to reveal the most efficient sheep meat farms and to identify the best practices and farm innovations that could potentially be adopted by other farms of similar production systems. Data Envelopment Analysis was applied to farm accounting data from 458 sheep meat farms of intensive, semi-intensive and extensive systems from France, Spain and the UK, and the structural and economic characteristics of the most efficient farms were analyzed. These best farmers were indicated through a survey, which was conducted within the Innovation for Sustainable Sheep and Goat Production in the Europe (iSAGE) Horizon 2020 project, the management and production practices and innovations that improve their economic performance and make them better than their peers.
... The nutritional data collected may also be affected due to methodological differences (Castro-Montoya & Dickhoefer, 2020). According to Mandal et al. (2005), the database in Kearl (1982) was based on a few publications on Indian animals and used protein requirement estimations derived from nitrogen balancing tests or studies with nonproducing animals, which could explain the disparity between the two databases (Cannas et al., 2019) provided in Table 6. ...
Article
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An elaborate multiple regression analysis was done to arrive a nutrient requirement equation for goat including dry matter intake, DMI (kg/day), total digestible nutrient, TDN (g/day) and crude protein, CP (g/day) based on animal body weight (BW)(kg) and average daily gain (ADG)(g/day). The derived equations were highly significant (p < 0.001) and had high R2 (0.99) values. The estimated values of TDN, CP and DMI are compared with NRC (1981), Kearl (Nutrient Requirements of Ruminants in Developing Countries, All Graduate Theses and Dissertations, 1982), as well as ICAR (Livestock Management, 2013). The estimated total TDN and CP requirements at different body weights and ADG are close to the values of recommended feeding standards of Mandal et al. (Small Ruminant Res., 58, 2005, 201). The estimated DMI values are close to the values of ICAR (Livestock Management, 2013) but lower (26.5%–43.8%) as compared to NRC (1981). Regressed values are used to develop a linear programming (LP) model and a stochastic model (SM) for least‐cost ration formulation for the Indian goat breed, whose average BW is about 45 kg and ADG is 130 (g/day), and which is solved using LP simplex and Generalised Reduced Gradient (GRG) nonlinear of Microsoft Excel. The models satisfy the nutrient requirement calculated by regression equations with minimum specified level of variation (usually 5%–10%) in CP and TDN. Both methods adequately meet the nutritional requirements. Therefore, an electronic sheet is developed in Excel to calculate DMI, TDN and CP for different body weights, ADG and formulate the ration by LP and stochastic model.
Article
The objective of this study was to investigate the response of Charolais and Ile-de-France meat sheep breeds to stimulate superovulation with various follicle-stimulating hormone (FSH) preparations. A total of 14 adult ewes of meat sheep breeds were used in our study as donors, including Charolais breed (n= 8) and Ile de France breed (n= 6). Donors ewes were randomly divided into two groups in equal numbers (first group, n=7; second group, n=7), every group included Charolais breed (n= 4) and Ile de France breed (n= 3). Ewes in the first group were treated with Folltropin-V (total dose of 200 mg per ewe, 7 injections), and ewes in the second group were treated with FSH-P (total of 280 IU per ewe, 6 injections). 37 ewes of Edilbay breed used as recipients, divided into two groups (first group, n=20; second group, n=17). Our results showed that the number of corpora lutea in donors group treated with Folltropin-V was significantly higher than donors group treated with FSH-P (P<0.01). A greater number of embryos recovery and embryos suitable for transplantation found in the first group compared with the second group of donors. After 30 days from transplantation, transabdominal ultrasonography showed that the presence of pregnancy in recipients groups was found in 16 recipients ewes (43.2%), in the first group of recipients were registered 9 pregnant ewes of 20 recipient ewes (45.0%), and in the second group of recipients were registered 7 pregnant ewes of 17 recipient ewes (41.2%). In conclusion, using Folltropin-V in Charolais and Ile-de-France meat sheep breeds is a more effective scheme for stimulating of superovulation than using FSH-P.
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This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler’s ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. AI might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.
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Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
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The objective of this study was to evaluate residual feed intake (RFI), residual intake and body weight gain (RIG) and their relationship with other traits of efficiency, performance, and economic analysis of sheep. Lambs (n = 102) were evaluated during 56 days and a herd consisting of 500 ewes was simulated with the mean of dry matter intake (DMI) and final body weight of the lambs, the same as that of the experimental lambs. The lambs were fed hay-based diet of Tifton 85 (Cynodon spp.), corn, and soybean in the voluminous:concentrate ratio of 35:65. Residual feed intake and RIG were correlated with DMI, feed conversion ratio, and feed efficiency. Residual intake and body weight gain were positively correlated with average daily gain, relative growth rate, and Kleiber's rate. The most efficient production systems (lower RFI and higher RIG) had lower costs and higher profit margins. The net present value (NPV) and internal rate of return (IRR) were higher in systems with more efficient lambs. In systems with less efficient lambs, NPV and IRR were negative and lower, respectively. Efficient animals for RFI and RIG showed satisfactory performance and better economic results.
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The aim of this study was to determine the association of residual feed intake (RFI) with growth performance, blood metabolic parameters, and body composition factors in growing lambs. Individual body weight (BW) and dry matter intake (DMI) were determined in 137 male Hu lambs that were given a pellet feed four times a day for 50 d. RFI did not show a correlation with metabolic BW (MBW) or average daily gain (ADG), but it showed a positive correlation with DMI and feed conversation ratio (FCR). Organ weight and intestine length had a large influence on RFI in lambs. The low-RFI lambs have smaller rumen and longer duodenum indicating the less feed intake and more sufficient absorption rate of low-RFI lambs. The smaller organs like liver, lung and kidney in low-RFI lambs may be related to lower energy consumption and slower metabolic rate. The observed bigger testis was in low-RFI lambs was another cause of the improved feed efficiency. Finally, the plasma concentrations of thyroxine (T4) and adrenocorticotropic hormone (ACTH) were lower in the ELow-RFI group than in the EHigh-RFI group. This study provides new insight into the biological processes underlying variations in feed efficiency in growing lambs.
Conference Paper
Mertens (1987) defined maximum concentrations of dietary neutral detergent fiber (NDF) that would not cause dry matter intake (DMI) reduction in dairy cows due to diet’s filling effect in the rumen. In his seminal work, the NDF values were obtained considering an optimal level of NDF intake as percent of body weight (NDFI%bw) of 1.2%. The actual value used was 1.1%, to include safety margins. Although there are no indications regarding optimal NDFI%bw for small ruminants, lactating ewes usually have an NDFI%bw markedly higher than 1.1%, e.g. 2.28% for 42-kg ewes (Molle et al. 2014 and 2016) and 1.76% for 92-kg ewes (Olsen 2016). Sheep also have considerably greater DM intakes as % of body weight (BW) than cattle (Van Soest 1994), bringing to dietary concentrations of NDF which are too low to allow proper rumen function, if 1.1% NDFI%bw is true. Thus, we developed a model to predict optimal NDFI%bw and dietary NDF concentration for lactating ewes, by using the equations of Mertens (1987) to develop NDF thresholds. The 1.1% NDFI%bw of Mertens (1987) was scaled to sheep BW assuming it varied as a function of BW-0.25, which is the result of the ratio of energy maintenance requirements, which scale with BW0.75, and reticolorumen volume, which scales with BW1 (Van Soest 1994), This is coherent with the scaling with BW-0.27of feed rumen passage rate reported by Illius and Gordon (1991). As a result, the NDFI%bw that would not restrict DMI due to rumen fill decreased exponentially (NDFI%bw = 5.4442×BW-0.25), ranging from 2.10% for 45-kg ewes to 1.77% for 90-kg ewes. Thus, by using these values of NDFI%bw, the maximum dietary concentrations of NDF to avoid rumen fill restriction on DMI were calculated for sheep of different mature BW and milk production (Table 1). These values were evaluated by using the 50 individual measurements of DMI, diet composition, and milk production of lactating ewes of Molle et al. (2014 and 2016) in which the animals were fed ad libitum. There was a fairly close agreement between predicted and observed dietary NDF concentrations (root of the mean squared prediction error (RMSPE) = 5.0% NDF, concordance correlation coefficient (CCC) = 0.64, r2 = 0.57) and a very close agreement between predicted and observed DMI (RMSPE = 0.11 kg d-1, CCC = 0.94, r2 = 0.94).
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This systematic review explores the use of on-animal sensor technology in sheep research. A total of 71 peer-reviewed articles reporting on 82 independent experiments were reviewed, ranging in publication date from 1983 to 2017 and distributed across all populated continents. The findings demonstrate increasing numbers of published studies that validate the application of sensor technology to categorise and quantify sheep behaviour. The studies also used sheep sensors for environmental management, validation of data analysis methods and for health and welfare research. Whilst historically many applications of sensors in sheep research have been conducted over a short period with small numbers of experimental animals, this trend appears to be changing as technology develops and access improves. The literature suggests that many applications of sensors have already or are currently moving through a proof-of-concept stage, allowing future applications to focus on commercialisation of technology and potential integration with other technologies already in use (e.g. weather data).