the nutritionist 2019 · computer program with optimizers $ 700-850/year ... (k2) rate of passage...
TRANSCRIPT
The Nutritionist 2019
Live and Recorded Ruminant Nutrition WebinarsMore Information at https://agmodelsystems.com/webinars/
Email: [email protected]
Formulation and Optimization
Dr. Sam Fessenden, Nutrition Technical Support and Development
First, a question:
•What is the largest factor leading to variability in feeding management on your farm(s)?
• If I could guess...• Variability in forage DM (we mix on a AF basis)
• Variability in loading and mixing feeds (one bucket shake too much?)
• Variability in feed bunk management (TMR delivery, sorting, etc)
• Variability in forage quality information (How often do you test?)
•Can we implement a change next week?
Which one fixes the problem?
Computer Program with optimizers
$ 700-850/year
Moisture Tester$ 50-300 one time
Why even use a formulation model or optimizers?
• Which feed is better at making milk?• Corn silage or wheat straw?
• Whole corn grain or fine-ground barley?
• Canola meal or protected soybean meal?
• Which feed is less expensive?• Per ton? Per kg of potential milk?
• Need to use a feeding system (mathematical model) to predict future performance on hypothetical diets.
• To effectively use any nutrition model and tools like optimizers, we need to understand the biology and behavior of the cow first.
Cornell Net Carbohydrate and Protein System• A mathematical model accounting for supply and requirements• Focused around energy, protein and amino acid balance
Requirements• Generally use empirical
equations• Maintenance, pregnancy
Lactation, growth, reserves• Animal characteristics are
most important• Adjustments made for
environment and activity
Supply• Mechanistic equations• Rumen sub-model
(microbes) and intestinal digestibility drive supply
• Feed characteristics are most important
• Diet associated effects are taken into account
2017 Webinar Today
Determining nutrient supply in the CNCPS• Digestibility = kd/(kd+kp)
• Rate of degradation (kd): intrinsic to the feed (Protein and Carbohydrate pools in each feed)• Rate of passage (kp): intrinsic to the animal (based on intake, weight, stage of lactation)
• Equation used to calculate disappearance of given substrate• Microbial growth rate is calculated directly from CHO kd
• Metabolizable Energy (ME): Calculated from digested nutrients (modified TDN system)• Metabolizable Protein (MP): Microbial protein & undegraded feed protein
• AA calculated from profile of each protein source (factorial system)
Substrate in the
rumen
Intake Passage
Digestion
Rate of intake
(K1)
Rate of digestion
(K2)
Rate of
passage (K3)
CNCPS Feed Fractions
Protein Fractions Carbohydrate Fractions
Label Lab Measure Relative kd Label Lab Measure Relative kd
PRT A1 Ammonia Fast CHO A1-A3 VFA Moderate
PRT A2 Sol. True Prot. Med-Fast CHO A4 Sugars Fast
PRT B1 By difference Moderate CHO B1 Starch Med-Fast
PRT B2 NDIP-ADIP Slow CHO B2 Sol. Fiber Moderate
PRT C ADIP Undegradable CHO B3 Digestible NDF Slow
CHO C uNDF undegradable
Feed analysis: Composition
• Which measurements of composition are most important?
• Evaluated ME and MP sensitivity to 1 SD increase in amount of nutrient
• Higgs et al., 2015
NDF
Feed analysis: Digestibility
• Which measurements of digestibility are most important?
• Evaluated ME and MP sensitivity to 1 SD increase in digestion of nutrient
NDF kdStarch kd
Higgs et al., 2015
Protein kd
Measuring Fiber• ALL ‘NDF’ (including
digestion) for CNCPS use should be aNDFom
• Large equipment that move fast make dust and dirt fly• Dust at filling• Dust during harvest• Dust at feed-out
• Especially important in flood-irrigated soils and wide swathing of haycrop
NDF digestibility: most models
• Whole-crop silage from UK
• Traditional single time point:
• Used 30 h NDFd:
• 31.3 % of NDF
• Lignin 2.4 as estimation of iNDF:
• 29.3 % of NDF
• Kd: 2.13 %/h
NDF digestibility: 3 time point method
• Same UK forage
• 3 time point
• 30h: 31.3 %
• 120h: 36.5 %
• 240h: 38.4 %
• Measured uNDF
• 61.6 % of NDF
• Kd: 5.7 %/h
Why has the 3 time point method helped?
• More accurately reflects biology:• Actually measures the digestible pool of NDF
• Reduces error in kd estimation• Multiple time points reduces reliance on any one point
• 24-48hr NDFd remain pretty variable across labs
• Works well on all forages and non-forages• More time points may be added over time (12hr)
• Is there an opportunity to use uNDF 240h to estimate intake potential?
uNDF formulation guidelines• Early Miner Institute data suggested: 0.40 % BW as possible fill max, DMI max, 0.30 %
BW as possible fill minimum for rumen health and functioning. • Caveats: Have to measure TMR (diagnostic, retrospective) or all feeds w/ NDF to use in formulation
• Newer data---Is it universal across systems? Testing the limits: • Irish cattle on pasture: 0.18% of BW
• English cattle, highly digestible grass: 0.22% of BW
• Alfalfa Hay as only forage: 0.48% of BW
• US 30% forage diet: 0.49 % of BW
• DO YOU KNOW THE ACTUAL BODY WEIGHT?? (see 2017 Webinar)
• Forage NDFu30 intake• Application in low-forage diets? Grass vs. alfalfa diets? High byproduct diets? Cattle size?
• uNDF % of DM intake--- Maybe good for directionality of expected DMI change
• peuNDF ---Check out Rick Grant’s Webinar (Feb 2019) for more information
• Starch digestibility has the single largest effect on predicted milk
• Internal and external factors are involved
Digestible starch: best source of MP
Starch kd
Higgs et al., 2015
Internal factor: Floury vs. Flinty starch• Ensiling and
processing have important effects
• Disruption of the matrix will increase starch digestibility
• Grind, Heat, Pressure, ensiling
• Popcorn?Figure from Holding (2014) DOI: 10.3389/fpls.2014.00276
Particle size measures: CSPS and Grain Particle size
P. Hoffmann UW FeedGrain 2.0 background slides
Dairyland Labs
Starch Particle Size—Field application
• For CNCPS implementation, it all relates to CHO B1 kd and ID
• We are limited to some ‘rules of thumb’ at this point:
• Corn Silage• Poor kernel processing? Pick “Unprocessed” out of the library
• This will decrease the intestinal digestion of starch and avoid ME bias
• High Moisture Corn (shelled, ear)• ‘New’ stuff will have lower kd, increases over time
• Corn Grain• Mean particle size can help. Below 1000 gets us into the 20%/h range for kd• Keep an eye on distribution of particle size
Starch summary• The good
• We are at a similar place as NDFd was 15 years ago. • IVSD seems to be sensitive to lab particle size• Single digestible pool, common time points
• The bad• Focused around corn starch• Drying starch (oven vs. microwave debate)
• The ugly (at least for now)• Is lab particle size (4mm grind) relevant? What does the
cow really see?• On-farm variation (‘processed’ silage, old hammer mill)• Drying starch (oven vs. microwave debate)
• Overall, progress is being made. Lab-derived kds are popular, but always look carefully and always ask questions
Protein Digestibility
Conceptual Quiz: Why does increasing soybean meal digestibility lead to decreased allowable milk?
NDF kdStarch kd
Higgs et al., 2015
Protein kd
Metabolizable Lysine and Methionine Supply
• Need to find good measures of AA availability
Blood Meal1%
Corn Silage3% Protected soybean meal…
Soybean Meal3%Ground Corn
4%
Haylage5%
Distillers5%
Canola Meal8%
Protected Met16%
Microbes52%
METABOLIZABLE MET
Corn Silage2%
Blood Meal2%
Ground Corn2%
Distillers3%
Protected Soybean …
Soybean Meal6%
Haylage7%
Canola Meal9%
Microbes64%
METABOLIZABLE LYS
Protein digestibility assays
• Most procedures rely on solubility and pours bag/filter• Unfortunately, many proteins can be soluble (thus escape the bag) yet
indigestible.
• 3-step, in-situ, mobile bag, etc. –All suffer in this area
• Other approaches?• Enzyme inhibitor assay ---might be useful, but has historically not been
applied at a commercial level (a bit intensive for a commercial lab)
• Perhaps a good thing to consider now in the age of NIR??
• Intestinal digestibility of escaped protein is still hard to estimate.
What does this have to do with optimization?
• Too often nutritionists want to simply click a button and have the diet formulated for them
• To be successful in optimization, you have to understand the basics of how the CNCPS estimates nutrient supply
• Many types of products, especially rumen-protected protein, fat, and AA sources may be characterized to perform well in the CNCPS, but not all products have proven themselves in the cow.• This runs the risk of including products that may not be needed.• Also, is there a real difference between 1-2 grams of MP-AA?
• Formulation without thoughtful consideration of the practical on-farm situation will be a frustrating and expensive endeavor. • “Optimize” diet implementation first, then optimize the formulation
Diet was “optimized” and even fed through a Vector system....is there still an implementation problem?
• Photos: S. Fessenden
Optimizers in AMTS.Cattle.Professional
• Linear ration optimization• AMTS.Cattle.Pro
• Advanced optimization for non-linear constraints • AMTS.Cattle.Pro(ao)
• Linear mix optimization• AMTS.Cattle.Pro(mo)
• Advanced and Mix optimization• AMTS.Cattle.Pro(ao+)
Least cost linear optimization
• Set constraints on inputs and outputs• Least cost, works best with linear output like
content of CP, aNDFom, minerals, etc.
Non-linear optimization: The solution space
• Think of a mountain range, goal is to "climb to the top”
• Mountains (ie solution areas) are more complex….simple optimizers can be fooled.
• A solution space is defined by input and output constraints
Advanced optimization in AMTS
• Uses principles of evolution and genetics to find the optimal solution
• Initial population is randomly generated (F0)
• Rations are run through model, and top individuals are selected via a fitness function (how well do they meet constraints)
• Top individuals are used to generate the next population (F1)
• Process repeats until fitness reaches plateau
Advanced optimization in AMTS
• Using graphic on right →
• Mountain ‘above water’ is defined as solutions that fit all constraints.
• Stars are individual rations tested by the optimizer
• Goal is to find the solution at the peak by ‘breeding’ lesser solutions and applying selection pressure.
Mix Optimizer
• Allows user to formulate mixes on a least cost basis
• Simple to use, speeds formulation for min/vit premixes, grain mixes, protein supplements
• Can also be used to estimate most likely formulation of a mill mix if ingredients and output (from feed tag) is known.
My approach for optimization in AMTS
• When doing diet optimization (or formulation overall), always remember: Garbage in = Garbage out
• The optimizers should be used to:• Challenge pre-conceived notions of diet formulation• Re-establish price relationships between feeds• Aid in final ‘tuning’ of a ration, especially for economics
• Optimizers in AMTS are not ‘autobalancers’• It still takes common sense and knowledge of nutrition principles
• When constraints are thoughtfully established, optimizers can help find more economically efficient rations.
What are my typical parameters for optimization?
Intake & Cost
Production
Amino Acids
Rumen Nitrogen
Rumen Energy
Rumen Health & Milk Fat
Diet Nutrient Concentrations
Wrapping it up--Pop Quiz:• What is the most important on-farm
tool for nutrition management?
• No sense using a model or optimizing if we can’t get this right.
