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. 2019 Apr 11;9(2):33-44.
doi: 10.1093/af/vfz005. eCollection 2019 Apr.

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

Antonello Cannas  1 , Luis O Tedeschi  2 , Alberto S Atzori  1 , Mondina F Lunesu  1
Affiliations

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

Antonello Cannas et al. Anim Front. .
No abstract available

Keywords: efficiency; goats; nutrition models; sheep.

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Figures

Figure 1.
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).
Figure 2.
Figure 2.
Measurements of production efficiency. (A) Feed efficiency 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 requirements 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semi-extensive and extensive dairy sheep and dairy goats farms of Sardinia, Italy (Atzori A.S., Lunesu M.F, Cannas A., unpublished data from the project Forage4Climate; EU LIFE+15).
Figure 3.
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).
Figure 4.
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 Scientific and Industrial Research Organization; NRC = National Research Council; CNCPS = Cornell Net Carbohydrate and Protein System.
Figure 5.
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, modified), 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.
Figure 6.
Figure 6.
The data–information–knowledge–wisdom (DIKW) hierarchy as a pyramid to manage knowledge. Reproduced with permission from Tedeschi (2019).
Figure 7.
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).
See this image and copyright information in PMC

References

    1. AFRC . 1993. Energy and protein requirements of ruminants. Wallingford (Oxon, UK): CAB International.
    1. AFRC. 1998. The nutrition of goats. Wallingford (UK): CABI Publishing.
    1. ARC . 1980. The nutrient requirements of ruminant livestock. London (UK): Agricultural Research Council. The Gresham Press.
    1. Berry, D. P., and Crowley J. J.. 2012. Residual intake and body weight gain: a new measure of efficiency in growing cattle. J. Anim. Sci. 90:109–115. doi: 10.2527/jas.2011-4245. - DOI - PubMed
    1. Cannas, A. 2004a. Energy and protein requirements. In: Pulina, G, editor. Dairy sheep nutrition. Wallingford (Oxon, United Kingdom): CAB International; pp. 31–49.

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