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Image analysis and nonlinear modeling to determine dimensions of wet-sieved, masticated forage particles.

Authors: J M, Luginbuhl; D S, Fisher; K R, Pond; J C, Burns;

Image analysis and nonlinear modeling to determine dimensions of wet-sieved, masticated forage particles.

Abstract

A procedure to estimate dimensions of digesta particles was developed and evaluated. After wet sieving, particles of masticated switchgrass and bermudagrass hays retained on sieves with apertures of 1.0 mm2 or larger were placed on moist white filter paper, separated manually, and photographed. Photographs were scanned with a flat-bed scanner, scaled to size, and digitized. Projected area, perimeter, length, and width of particles were determined by a microcomputer system. Variables derived for each particle included the form factor, which was defined as (4 x pi x area)/perimeter2, and each particle's length: width ratio. Ninety-three percent of data from two trials with steers had significant lack of fit (P less than .05 to P less than .0001) to normal, lognormal, Weibull, or gamma distributions. As an alternative, individual particle measurements were summed on a cumulative percentage basis for each variable and were fit to an inverted Gompertz function to estimate median, mode, and mean dimensions of scanned particles. Estimates from the equation fit the distribution well; asymptotic SE averaged 3.5 and 1.4% of parameter means for dimensions of masticated switchgrass and bermudagrass hay particles, respectively. No analytic solution exists for the mean, which must be estimated numerically, but analytic solutions are available for the median and mode. Use of this equation will prevent bias caused by lack of fit to a particular distribution and yield more accurate estimates of mean particle dimensions than arithmetic means.

Keywords

Male, Models, Statistical, Image Processing, Computer-Assisted, Animals, Mastication, Cattle, Poaceae, Animal Feed

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Top 10%
Average
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