Implementation of multi objective feed formulation approach by dr. s.m. mutua

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Collaborating Organizations: 

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Collaborating Institutions: 

Egerton University,Department of Animal Sciences,

P. O. Box 538-2011Njoro-Kenya

TEL: 0735-752-823; EMAIL: deanagric@egerton.ac.ke

 Ministry of Agriculture, Livestock and Fisheries,State Department of Livestock,

P. O. Box 34188-00100Nairobi-Kenya

TEL: 0735-752-823; EMAIL: smutuah@yahoo.com

and  

Fika Systems Limited-NairobiP. O Box 73432-00200,

Nairobi-Kenya, TEL: 020-2495918; EMAIL: info@fikasystems.com

Mutua S. M.04/12/2023 3

State Department of Livestock-Nairobi, KenyaAnimal Sciences Department-Egerton University, Kenya

Presented

By

Stanley Mutua

Session overview

• Introduction• Overview of feed industry• Problem domain

• Program development process• Prediction functions

• Milk Yields (MY)• Dry Matter Intake (DMI)• Minimum nutrient excretion (MPE)

• Software validation process• Results of on-farm feeding trials • Potential benefits of MoF-Dairy program

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Introduction-1

• Feed industry founded on 3 main dimensions

• Feed manufacturing chain• Who are involved? Dependencies

• Technical dimension • What to feed?/Feed resources

• Nutrition and feeding management• (When and how much to feed)

Principles of Feed Formulation

In Conjunction with Least-Cost Function

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Problem Domain: Limitations of LCF Based on singular objective (ingredients cost only)

• Focused on millers entrepreneurial

gains only

Fails to provide options to:

• Match feeding with animal production

potential

Fails to impose critical formulation goal of:

• Minimum pollutant manure nutrient

excretion into environment

Introduction-2

• Emerging economic, production and policy challenges

• Driving the need for a MoF approach • Entrepreneurial needs /regulatory

mandate

Innovation platform

System Design and Development

Process Improvement Strategic Objective

(PISO-Approach)

Process Re-Engineering

Feed industry actors

Model objects

• Program data objects include

• Users,

• Ingredients,

• Dairy cows

• Feed policy guidelines

Model data organisation

•

Model schematic representation

•

Predictive equations

• Milk Yields (MY)

• Dry Matter Intake (DMI)

• Minimum Phosphorous Excretion (MPE)

Predictive equations

• Milk Yields (MY)

• BF (Butter Fat)

• FCM (Fat Corrected Milk)

Predictive equations

• Dry Matter Intake (DMI)

• FCM (Fat Corrected Milk)

• BW0.75 (Metabolic Body Weight)

• WIM (Weeks in Milk)

Predictive equations

• Minimum nutrient excretion (MPE)

• Minimum nutrient excretion (Min P)

• K (Efficiency of P utilisation)

• P (Feed phosphorous inclusion levels)

• M (Optimal daily milk yield)

Optimizing predictive functions

• Integration of production and policy regulations

• Regulatory guidelines

• CP: P ratio

• Minimum P inclusions

• Least-Cost affordable feeds

• Representing critical formulation goals

Optimizing Formulation Objectives

Integration of production and policy

regulations

• Regulatory guidelines

• Quality and Pollution

• Energy: CP

• CP: P ratio

Max milk production

Minimum P inclusions• Least-Cost affordable feeds

Representing critical formulation goals

In Conjunction with Least-Cost Function

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Available Ingredients

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MOF-Formulation Approach(Formulation design)

Ingredient Ingre-Group

Must-include

CP % P % CP:P ratio

Price/100kg

Safe max

1 Dairy premix M X

2 Limestone M X

3 DCP M X

4 Energy rich source(s)

5 Wheat bran E 15.15 0.92 16.46

6 Maize germ E 10.53 0.53 19.87

7 Protein rich source(s)

8 sunflower cake meal

P 26.88 0.57 47.16

9 Cotton cake meal

P 29.29 0.62 47.24

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MOF-Formulation Approach(Ingredient, ingredient grouping and nutrition data)

Formulation objectives

Ingredient Ingre-Group

CP:P ratio Price/ TDN%

Price/CP%

Price/100kg

1 Dairy premix M

2 Limestone M

3 DCP M

4 Energy rich source(s) E

5 Protein rich source(s) P

6

7

8

9

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MOF-Formulation Approach(Formulation outline)

Formulation objectives

Ingredient Inclusion %

Ingre-Group

Must-includ

e

CP:P ratio

Price/ TDN%

Price/CP%

Price/100kg

Safe max

1 Dairy premix M X

2 Limestone M X

3 DCP M X

4 Energy rich source(s)

E

5 Protein rich source(s)

P

Sub-total 50%

6

7

8

RequirementsOptimal range:

100% Min: Min:Max:

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Software program validation process

Compare milk yield and quality,

nutrient excretion and cost from dairy

rations formulated with LCF and MOF

approaches

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Materials and Methods

Study site

Egerton University-Ngo’ngo’ngeri Farm

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Materials and Methods Animal management

12 Dairy Cows at same

Breed = Friesian

Age = 54 months

Lactation = Third (3rd )

Advance De-Worming (2 weeks)/Albendazole

10%

Placed in cross-over repeated measures design,

with 6 replications each for two (2) periods

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Materials and Methods

Animal experimental design

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Statistical analysis

One-way (no Blocking) ANOVA) using GenStat Discovery Edition-3 statistical software (2007);

Where: Yij = Overall cow performance under the ith diet

µ = the overall mean effect   αi = the effect of the ith diet (i = LCF, MOF)

  eij = the random error

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ijiijY

Results and Discussion Milk yield, milk composition and manure P

levels

04/12/2023 32Mutua S. M.

Concentrate diet type

Herds average

LCF SED MOF SED

Yield (kg/day) Actual Milk 12.00 13.35 4.695 13.39 4.483

Milk Composition (%) Butter fat 3.760 3.914 0.098 4.019 0.165

Protein 3.135 3.154 0.022 3.152 0.033 Phosphorous 0.081 0.148 0 0.151 0.009Manure Composition (%) P-Manure 0.609 1.71 0 1.66 0.026

Body Weight Mean (kg) Change (kg/day)

420 430.600.225

10.990.108

425.700.207

8.7700.108

OthersConcentrate efficiencyCrude protein: Phosphorous ratioCost price/kg feed (Kenya shillings) 1USD ≡ 100 Kshs

-- -

0.3423.02

30.38

-- -

0.3522.75

23.44

-- -

Innovative product and service

•• Improved efficiency with which to formulate dairy rations

• Integration of economic, production and policy needs as critical formulation goals

• Blending of entrepreneurial needs as well as institutional mandates

PARADIGM SHIFTFROM TO

Singular Objective

IS

One Factor Methodolo

gy

Multiple Objective

IS

Many Factors

Methodology

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Acknowledgement 

Mutua S. M.

END

Thank you ALL for listening

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