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Simpler intake estimation using direct observation in small ruminants: grouping bites by plant structure and morphology

Overview of attention for article published in BMC Research Notes, July 2018
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Title
Simpler intake estimation using direct observation in small ruminants: grouping bites by plant structure and morphology
Published in
BMC Research Notes, July 2018
DOI 10.1186/s13104-018-3570-8
Pubmed ID
Authors

P. G. González-Pech, J. F. J. Torres-Acosta, C. A. Sandoval-Castro

Abstract

To validate the estimation of dry matter intake (DMI) obtained from bite categories (BC) and weight for every plant species (method 1: M1) vs. an alternative method (method 2: M2) grouping plants based on structure and leaf morphology. A dataset containing 80,813 bites and 33 plant species obtained by M1 for sheep and goats browsing a tropical forest was used. Plant species and their respective bite weight were regrouped according to M2. BC weights within each morphological group were compared using ANOVA and post hoc Tukey's honest significant difference comparisons. DMI was estimated for sheep, goats and DMI obtained with both approaches was compared using the t-test, Pearson correlation and orthogonal regression analyses. Dry matter intake estimations were: M1 = 369 ± 153 vs. M2 = 425 ± 161 gDM for sheep and M1 = 567 ± 190 vs. M2 = 681 ± 203 gDM for goats. DMI estimations by M1 and M2 were similar and strongly correlated. Orthogonal regression showed both procedures yielded a similar DMI estimation (P < 0.001). M2 reduces the amount of work required to estimate DMI in heterogeneous vegetation without reducing accuracy. M2 reduced the time required and made simpler to include data from larger number of animals/replicates.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 40%
Student > Ph. D. Student 2 13%
Professor > Associate Professor 1 7%
Student > Master 1 7%
Unknown 5 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 27%
Veterinary Science and Veterinary Medicine 3 20%
Biochemistry, Genetics and Molecular Biology 1 7%
Nursing and Health Professions 1 7%
Chemistry 1 7%
Other 0 0%
Unknown 5 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 July 2018.
All research outputs
#20,525,274
of 23,096,849 outputs
Outputs from BMC Research Notes
#3,583
of 4,287 outputs
Outputs of similar age
#286,266
of 326,767 outputs
Outputs of similar age from BMC Research Notes
#113
of 142 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.