Analytical Characterization of feedstuffs to optimize the cow performances
Charles J. Sniffen, Ph.D.
Fencrest, LLC
Andrea Formigoni, Ph.D.
U of Bologna
Introduction
Two things have happened in the last couple of decades.
First, we depended on average analyses of feeds and forages – book values
We now want analyses for the forages
Next our old nutrition models depended on the Weende analyses of CP, CF, EE and
ash
This is now different – we need more
Introduction
The reasons for the need for more
sophisticated and frequent analyses are simple
The margins are shrinking and there is an increasing need to predict responses to nutrition inputs more accurately
We now recognize that forages and
feedstuffs vary significantly
Introduction
The CNCPS system is a semi dynamic model.
The dynamic components
Rumen sub model
Digestion and passage
CHO’s Proteins Lipids
Microbial growth – fiber & NFC
Modified by
Introduction
The non dynamic components
Environmental sub model Mineral sub model
Absorbed nutrients
ME & MP to Net requirements
CNCPS Platforms
DinaMilk (both CNCPS 5.0 & 6.1) NDS – CNCPS 6.1, 6.5
AMTS – CNCPS 6.1
Dalex – CNCPS 5.0, 6.1
CNCPS 6.5 (uses the AMTS platform)
Components
Master Feed Dictionaries
Forages Energy Protein
Minerals, vitamins and additives
Commercial
Commercial Laboratories
In the USA there are 4 major forage labs
Cumberland Valley Dairy One
Dairy Land Rock River
They all focus on providing the latest
analyses – mostly NIR with chemistry back
up for calibration
Our concerns in building the correct rations
40 to 80% of the ration DM comes from forages
The most variable nutrient delivery comes from forages
We need to be concerned about the
analyses of the forages
Analyses
We will use Cumberland Valley analyses for most of our examples
CRPA and CVAS work closely together to provide the analyses needed
The chemistries that are done are based
on Italian forages and the NIR equations
are developed from these analyses
Corn Silage Protein Analyses
Alfalfa Hay Protein Analyses
Protein analyses
The soluble protein is an estimate of the protein that is solubilized in rumen fluid after one hour using a borate
phosphate buffer
This is corrected for the ammonia and
about 30 to 40% of the remainder will
escape fermentation
Corn Silage Fiber Analyses
Alfalfa Hay Fiber Analyses
Fiber Dynamics
The available Fiber is one of the major
source of fermentable carbohydrate in the ration
It is important that we be able to measure this more accurately going into the future
Work at the U Bologna, Miner Institute,
Cornell and Dr. Dave Mertens have moved us significantly ahead in this area
Dr. Formigoni will provide some of the details on
Corn silage CHO & Fat
Analyses
Fermentable starch
We have a long way to go in this area.
Starch is complex in its dynamics
There is more than one fraction – fast and slow fermentation fractions
Factors affecting fermentation in the rumen
Source – grain type and genetics
Time in silo – 6 months for some hybrids Particle size is surface area
Passage
Alfalfa Hay Carbohydrates and
Lipids
Corn Silage Predictions
Alfalfa Hay Predictions
Corn Silage Qualitative
Corn Silage Minerals
Alfalfa Hay Minerals
Additional Feed Analyses now and in the Future
Currently available
Individual FA analyses are now available Mold, yeast & Mycotoxins
Total tract digestibility of protein, fiber and starch Corn silage processing score
Future
Better estimates of forage fiber digestibility using NDFd 24 or 30, 120 and uNDF240
Individual sugars Amino acid analyses
Intestinal digestibility of proteins and AA’s?
Improved prediction of Ruminal and duodenal starch digestibility
The order of importance in formulation
When one looks at the factors affecting our ability to accurately formulate rations
Forage analyses comes to the top of the list Next comes the analyses of the non-forage ingredients
Then comes the accurate definition of the cows we are feeding
Finally a good understanding of the
Forage Sampling
Recommend
For silages have the person feeding remove forages from the face as usual and put in the mixer wagon.
Thoroughly mix the forage
then sample as it comes out of the mixer wagon
For hay, use a core sampler and sample 10 to 15 bales,
mix the core samples thoroughly
Sensitivity analysis
Prediction of animal performance
Many sensitivity studies have shown that with the analyses in CNCPS there is a significant improvement in predictability
Of milk yield BCS change
The model does not predict milk components
There are means to evaluate these with the model
Performance prediction
Generally if you have characterized all of the inputs accurately especially the feed and animal inputs
Experience has shown that when the
inputs are correct the prediction of milk produced is usually within 1 to 2 kg.
If there is a 3 to 5 kg difference the
Summary
The model biology is evolving as we learn more
We will be changing the CHO sub fractions and their fermentation rates in the rumen
Two pools of fiber and improved rates Better estimate of iNDF using uNDF
Two pools of starch with improved rates – in the future Expand to individual sugars – way in the future
The AA sub model has been improved with improved AA composition of feeds and improved efficiencies
The Fatty acid sub model will be improved with the significant increase in research done recently
Hopefully a pre-wean calf model will be in by the end
