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Methodologies on estimating the energy requirements for maintenance and determining the net energy contents of feed ingredients in swine: a review of recent work

Overview of attention for article published in Journal of Animal Science and Biotechnology, May 2018
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Title
Methodologies on estimating the energy requirements for maintenance and determining the net energy contents of feed ingredients in swine: a review of recent work
Published in
Journal of Animal Science and Biotechnology, May 2018
DOI 10.1186/s40104-018-0254-0
Pubmed ID
Authors

Zhongchao Li, Hu Liu, Yakui Li, Zhiqian Lv, Ling Liu, Changhua Lai, Junjun Wang, Fenglai Wang, Defa Li, Shuai Zhang

Abstract

In the past two decades, a considerable amount of research has focused on the determination of the digestible (DE) and metabolizable energy (ME) contents of feed ingredients fed to swine. Compared with the DE and ME systems, the net energy (NE) system is assumed to be the most accurate estimate of the energy actually available to the animal. However, published data pertaining to the measured NE content of ingredients fed to growing pigs are limited. Therefore, the Feed Data Group at the Ministry of Agricultural Feed Industry Centre (MAFIC) located at China Agricultural University has evaluated the NE content of many ingredients using indirect calorimetry. The present review summarizes the NE research works conducted at MAFIC and compares these results with those from other research groups on methodological aspect. These research projects mainly focus on estimating the energy requirements for maintenance and its impact on the determination, prediction, and validation of the NE content of several ingredients fed to swine. The estimation of maintenance energy is affected by methodology, growth stage, and previous feeding level. The fasting heat production method and the curvilinear regression method were used in MAFIC to estimate the NE requirement for maintenance. The NE contents of different feedstuffs were determined using indirect calorimetry through standard experimental procedure in MAFIC. Previously generated NE equations can also be used to predict NE in situations where calorimeters are not available. Although popular, the caloric efficiency is not a generally accepted method to validate the energy content of individual feedstuffs. In the future, more accurate and dynamic NE prediction equations aiming at specific ingredients should be established, and more practical validation approaches need to be developed.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 13%
Student > Doctoral Student 4 13%
Student > Bachelor 3 9%
Student > Master 3 9%
Student > Ph. D. Student 3 9%
Other 4 13%
Unknown 11 34%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 31%
Veterinary Science and Veterinary Medicine 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Unspecified 1 3%
Computer Science 1 3%
Other 1 3%
Unknown 14 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 August 2018.
All research outputs
#16,053,755
of 25,382,440 outputs
Outputs from Journal of Animal Science and Biotechnology
#307
of 905 outputs
Outputs of similar age
#197,648
of 342,098 outputs
Outputs of similar age from Journal of Animal Science and Biotechnology
#6
of 22 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 905 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 57% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,098 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.